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Glyco-Bioinformatics – Cracking the Sugar Code by Navigating the Glycospace, Martin G. Hicks and Carsten Kettner (Eds.), Proceedings of the 2nd Beilstein Symposium, June 27th – July 1st 2011, Potsdam, Germany.

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Preface

The Beilstein Symposia address contemporary issues in the chemical and related sciences by employing an interdisciplinary approach. Scientists from a wide range of areas are invited to present aspects of their work for discussion, with the aim of not only advancing science, but also enhancing interdisciplinary communication. Traditionally, the Beilstein Symposia are kept small with up to 50 participants to provide a convivial atmosphere for the both lectures and lively discussions and the ready exchange of thoughts and ideas.

Over the past decade, the scientific community of post-proteomics and post-genomics witnessed the rapid evolution of a new scientific field – the glycomics. The investigation of both glycans and glycan-binding proteins and lipids revealed increasingly the role of complex carbohydrates in diverse inter- and intracellular processes. Advances in analytical technologies, carbohydrate chemistry and structural biology led to an increased understanding of the high degree of variability of both composition, structure and function of sugars. Additionally, structure-function studies showed an inherent complexity which is determined by different branching patterns, various possible linkage positions and numerous building blocks.

Even though there has been significant progress in the development of interdisciplinary approaches such as experimental and computer scientific tools, the general technological developments in glycomics lag behind those in the proteomic and genomic areas. In particular, despite the current progress in glyco-bioinformatics regarding the computational characterization of carbohydrates, the glycomics community is still lacking a systematic and comprehensive database and analysis software. The database landscape is made up of numerous independent and disconnected databases with partially overlapping core areas. These databases provide neither complete nor compatible data sets on glycan structures. Additionally, since the individual data encoding and analysis software tools do not use a common language for the representation of schematic structures and sequence topologies, data processing and interpretation become extremely challenging.

The underlying aim of this symposium is to bring together glycochemists and biologists with experts in bioinformatics and computer sciences to pave the way for a concerted effort in the area of glyco-bioinformatics. Scientists who “produce” data met those who “use” the data. They discussed aspects such as data mining, structure prediction and docking of carbohydrates, as well as web-based services to combine proteomics and glycomics data for structure-function research and glycosylation analysis. This meeting continued successfully the efforts resulting from the first symposium held in October 2009 to discuss the necessity of a uniform data reporting practice that supports biologists, chemists and all interested in glycosciences to enable the integration of their experimental and analysis data into glyco-bioinformatics platforms.

We would like to thank particularly the authors who provided us with written versions of the papers that they presented. Special thanks got to all those involved with the preparation and organization of the symposium, to the chairmen who piloted us successfully through the sessions and to the speakers and participants for their contribution in making this symposium a success.

Frankfurt/Main, July 2012
Martin Hicks,
Carsten Kettner
Peter Seeberger

Contents

Titlepage

Imprint

Preface

Contents

Kiyoko F. Aoki-Kinoshita
Introduction to Glycoinformatics and Computational Applications

Harold Rüdiger and Hans-Joachim Gabius
Breaking the Sugar Code: Six Levels of Affinity Regulation in
Glycan-lectin Interaction

William S. York and René Ranzinger
MIRAGE: Minimum Information Required for a Glycomics Experiment

Maria Ortiz-Soto and Jürgen Seibel
Tailoring Enzymatic Reactions

W. Bruce Turnbull
Biophysical Studies on the Interactions of Bacterial Toxins

Roland Stenutz
Predicting the Biological Repeating Unit of Bacterial Lipopolysaccharides: Common Structural Motifs in Regular Polysaccharides from Different Genera

Daniel Kolarich and Peter H. Seeberger
Bioinformatics Analysis of the Glycome Guides Automated
Oligosaccharide Synthesis

Peter Both, Robert Šardzík, Martin Weissenborn, Anthony Green,
Josef Voglmeir and Sabine Flitsch
Glycoarrays on Gold Surfaces

René Ranzinger and William S. York
Glyco-Bioinformatics today (August 2011) – Solutions and Problems

Max Pillong and Gisbert Schneider
Representing Carbohydrates by Pseudoreceptor Models for Virtual Screening
in Drug Discovery

Thisbe K. Lindhorst
Glycomimetics: Tools for Investigation of Functional Diversity
in the Carbohydrate Regime

Matthew P. Campbell, Frédérique Lisacek, Marc R. Wilkins,
Pauline M. Rudd, Daniel Kolarich, Catherine A. Hayes,
Niclas G. Karlsson and Nicolle H. Packer
Linking Glycomics Repositories with Data Capture

Hans Heindl, Pamela Greenwell, Tamas Kiss, Chris Reynolds,
Daniel Farkas, Gabor Tersztyansky and Stephen Winter
Development of User-friendly, High-troughput Screening for Ligands and
Inhibitors of Carbohydrate Modifying Enzymes

Frédérique Lisacek, Matthew P. Campbell and Nicolle H. Packer
Expanding Knowledge of the Biological Context of Glycan Structures

Introduction to Glycoinformatics and Computational Applications

Kiyoko F. Aoki-Kinoshita

Department of Bioinformatics, Faculty of Engineering, Soka University,
1–236 Tangi-machi, Hachioji, Tokyo, 192–8577 Japan

E-Mail: kkiyoko@soka.ac.jp

Received: 11th December 2011/Published: 11th July 2012

Abstract

Glycoinformatics involves the development of computational methods and tools for the understanding of glycan function, including the development of databases, which use these methods and tools for validation of results. Thus several databases have been developed, storing a plethora of data from various analytical perspectives. These invaluable resources provide the data which theoretical computer scientists and data mining experts can use to develop new models and tools. This paper will describe the various carbohydrate-related databases that have become rather stable in this field, along with some of the methods that have been developed for analysing the glycan data from various perspectives. In particular, we focus on glycan biomarker and glycan binding pattern prediction.

Introduction

Glycoinformatics deals with the development of computational methods and tools as well as the construction of databases in order to gain insight into the functions of glycoconjugates and complex carbohydrates. As such, the glycoinformatics field has taken off with the development of various glycan structure databases and related information, as well as theoretical methods and practical tools for analysing glycans. In particular, with the development of major carbohydrate resources in Japan, the U.S. and Europe, it has become easier to develop and test these methods for practical applications. Here, we describe in detail some of these data resources and methods for beginners to get an idea of this newly emerging field of glycoinformatics.

Glycan Databases

The major databases that may be considered rather stable at the time of this writing are listed in Table 1. The GlycomeDB database [1] may be considered the main resource for glycan structure information, which is linked with several other glycan structure resources. Glycan binding data can be obtained from the CFG (Consortium for Functional Glycomics) [2] and JCGGDB (Japanese Consortium for Glycobiology and Glycotechnology Databases) which includes CabosDB [3]. Glycan profile data can be obtained from the CFG and EuroCarbDB [4], although the latter is still in the developmental stages. UniCarbKB [5] is also based on the EuroCarbDB infrastructure, so this resource will also provide profiling data once it is made available.

Table 1. Major glyco-databases.

Database

Description

URL

GlycomeDB

Portal for glycan structures that have been integrated from several of the major glycan-related databases

http://www.glycome-db.org

GLYCOSCIENCES.de

One of the earliest databases of glycan structure data, mainly extracted from the PDB (Protein DataBank). In addition to structural data, also includes NMR data and literature references.

http://www.glycosciences.de/

Consortium for Functional Glycomics (CFG)

A major database containing not only glycan structures, but also glycan binding affinity data from their glycan arrays, glycan profiling data from MALDI-TOF analysis of various samples, knock-out mouse phenotype data and glyco-enzyme expression data.

http://www.functionalglycomics.org

Japanese Consortium for Glycobiology and Glycotechnology Database (JCGGDB)

A comprehensive database portal for major glyco-related databases in Japan, including mass spectral data of glycan profiles, lectin array data, glycoprotein data, glycogene information including disease information, etc.

http://jcggdb.jp

KEGG GLYCAN

As one of the databases of the KEGG resource, glycan structures and their pathway data, including glycogene information as organized by the KEGG ORTHOLOGY can be obtained.

http://www.genome.jp/kegg/glycan/

EuroCarbDB

A database infrastructure for a distributed database of experimental data for glycobiology, including (tandem) MS, HPLC and NMR data and the related glycan structures.

http://www.eurocarbdb.org

http://www.ebi.ac.uk/eurocarb/home.action

UniCarbKB

A new database of experimental data for glycobiology, linked with glyco-related UniProtKB data.

http://unicarb-db.biomedicine.gu.se/unicarbkb

Glyco-resources refer to those web sites that provide computational tools for glyco-analysis. The database GLYCOSCIENCES.de [6] provides such tools as pdb-care for checking PDB files for accurate carbohydrate information, CARP for generating Ramachandran plots of glycosidic linkages, GlyVicinity and GlySeq for analysing statistical information around glycosylation sites and carbohydrate residues, and several glycan modelling tools based on molecular dynamics (MD) and molecular mechanics (MM) in addition to glycosylation prediction tools. GLYCAM Web [7] is another web-based tool for generating structural conformations of glycan structures, based on the GLYCAM force field parameters. Finally, RINGS [8] is a web-based resource providing a variety of tools for data mining glycan structures.

Glycan Data Formats

One issue in working with the various carbohydrate databases available is the handling of different glycan structure formats. An example of some of the major formats for a given glycan structure is illustrated in Figure 1. Each of these formats is used in different databases, and since there was no coordination between these database projects previously, different formats emerged to satisfy different requirements [9]. Even though a consensus has been obtained for GLYDE-II to be used as the standard format for carbohydrate data exchange between databases [10], it is not necessarily user-friendly or human-readable. Thus, some tools for converting between different glycan structure formats have been developed in RINGS, which provides utilities to convert between IUPAC, GlycoCT, Linear Code®, LINUCS and KCF. However, collaborations have emerged to link data between different databases based on GLYDE-II, and resources have started to either provide tools for converting between different formats or allowing different formats for input. Consequently, there is less concern now for which format to use to store glycan structure data.

Figure 1. Illustration of various glycan structure formats for the same glycan structure (illustrated). While many formats are linear sequences, some like KCF and GlycoCT use graph-like notations across multiple lines.

Computational Applications

Two of the major biological problems facing glycan structure analysis involve the discovery of glycan biomarkers and the understanding of glycan recognition patterns. The former can be obtained from the glycan profiles that are being generated by various databases, and the latter can be analysed from glycan binding and structural data.

Glycan profiles provide different sets of numerous glycan structures that are claimed to exist in particular cells or tissues. At times, there may be hundreds of glycan structures in a particular sample. It is understood that these glycan structures would consist of partially synthesized structures and portions of glycoproteins and glycolipids, these profiles may be considered snapshots of a particular sample. By comparing different snapshots of control versus target tissues, one may infer potential glycan biomarkers. In fact, classification methods from the machine learning field have been applied to predict such glycan biomarkers in the past [11–14].

However, another approach for the analysis of glycan profiles by computing is called the frequent subtrees that are found within the data set. There are several frequent subtree mining algorithms, but the one that has been applied to glycans is called the α-closed frequent subtree algorithm [15]. A parameter α is specified in order to provide the user with some leeway in computing the results. This method has been implemented as a web-based tool called Glycan Miner in RINGS, taking as input a set of glycan structures in any format in addition to the α and support parameter to specify frequentness.

The frequent subtree algorithm can also be applied to glycan binding data, to predict the glycan substructures that are mostly involved in binding, by specifying those glycan structures that bind strongly with a particular glycan binding protein (GBP). In fact, the Glycan Miner tool comes with a useful helper application for those using the CFG's glycan array data, as shown in Figure 2. Snapshots of the CFG web page and indications of where to retrieve the necessary data are illustrated. Additionally, the conversion tool is linked to the Glycan Miner Tool such that the converted data is automatically inputted and ready to be computed with the specified parameters.

In contrast, another method to predict glycan binding patterns of GBPs is a probabilistic model that computes disconnected patterns from within glycan data. This model was developed due to the understanding that some GBPs such as lectins (and Siglecs in particular) may recognize not only the residues at the terminal end, but also non-terminal residues further in the chain. Thus, probabilistic sibling-dependent tree Markov models called PSTMM [16] and OTMM (ordered tree Markov model) [17] were developed to capture such potential glycan binding patterns.

Figure 2. Snapshots of the CFG web page and indications of where to retrieve the necessary data are illustrated. Additionally, the conversion tool is linked to the Glycan Miner Tool such that the converted data is automatically inputted and ready to be computed with the specified parameters.

However, in order to allow general glycobiologists to use these models a method to extract the glycan patterns directly from the models was required. This was implemented in what was called the Profile PSTMM model [18], which is currently available as a tool in RINGS. The difficulty in extracting these patterns using Profile PSTMM came in the fact that the shape of the pattern that should be extracted must be specified beforehand. This is similar to the fact that the number of amino acids that must be extracted from Profile HMMs must be first specified when training the model. However, this is a bigger challenge for the branched structures of glycans. This difficulty is illustrated in Figure 3 in comparison with amino acid sequence profiles. Whereas the size (length) of amino acid sequence profiles can be specified directly based on the lengths of the input sequences, it is more difficult to specify the branched shape of a profile from a set of branched glycan structures. This is especially difficult to do automatically without any user input.

Figure 3. An illustration comparing amino acid sequence profiles and glycan structure profiles (patterns). Whereas the size (length) of amino acid sequence profiles can be specified from the dataset beforehand, it is more difficult to specify the branched shape of a profile from a set of branched glycan structures.

At first, the Maximum Common Subtree of all of the glycans were computed and used automatically as the default shape. However, if the input contained even one small structure, then the resulting glycan pattern would become very small. Thus, in order to enable larger sized patterns, the concept of aligning the input glycan structures and extracting a portion of the glycans that overlap the majority of times was devised. Multiple sequence alignment is actually considered a difficult problem even for linear sequences, so a heuristic was developed based on the popular multiple protein sequence alignment method ClustalW. This new method, called MCAW, for multiple carbohydrate alignment with weights, was then developed as a tool in RINGS. This tool is actually the basis of a number of potential analytical tasks, including the computation of glycan score matrices [19] for assessing glycosidic linkage similarity, in addition to the original intention of aiding probabilistic models in determining the glycan pattern to learn.

Thus, based on such fundamental computational methods, it may become possible to apply scoring functions to indicate the “similarity” of monosaccharides and glycosidic linkages. Such methods may also be applicable to spatial and structural analyses of glycans compared to protein structures, and even different patterns between organisms and species may be considered for future analysis.

Conclusion

In addition to the analysis of glycan structures, glycoinformatics also involves the understanding of glycan biosynthesis pathways, the functions of glycogenes and systems analysis of glyco-related pathways [20]. There is also a lack of glycoprotein data, but once a sufficient amount of such data is accumulated, more complex methods for analysing glycosylation and their functions may be developed. Additionally, another difficulty is the very large area of conformational analysis of glycans. Overall, it is probably safe to assume that this new area of glycoinformatics is sure to continue to progress in the years to come.

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[16] Ueda, N., Aoki-Kinoshita, K. F., Yamaguchi, A., Akutsu, T., and Mamitsuka, H. (2005) A probabilistic model for mining labeled ordered trees: capturing patterns in carbohydrate sugar chains. IEEE Transactions on Knowledge and Data Engineering 17(8):1051–1064.
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Breaking the Sugar Code: Six Levels of Affinity Regulation in Glycan-lectin Interaction

Harold Rüdiger¹ and Hans-Joachim Gabius²

¹Institut für Pharmazie und Lebensmittelchemie, Julius-Maximilians-Universität Würzburg, Am Hubland, 97074 Würzburg, Germany

²Lehrstuhl für Physiologische Chemie, Tierärztliche Fakultät, Ludwig-Maximilians-Universität München, Veterinärstraße 13, 80539München, Germany

E-Mail: ¹ruediger@mail.uni-wuerzburg.de and
²gabius@tiph.vetmed.uni-muenchen.de

Received: 18th November 2011/Published: 11th July 2012

Abstract

The glycan chains of cellular glycoconjugates harbour information essential for many physiological processes. It can be decoded by counter-receptors. Among them, lectins (carbohydrate-binding proteins except antibodies, enzymes working on the cognate glycans and sensor/transport proteins for free sugars) play a prominent role in translating the sugar code. The exquisite precision, with which distinct glycoconjugates and lectins form pairs, poses the question on the underlying molecular mechanism(s) that guide(s) lectins to only few target sites (‘the needles in the haystack’), and this even with cell-type specificity. To provide a detailed concept and hereby give research a clear direction, structural characteristics on the side of the glycans are identified and systematically listed. As a result, a six-level scheme is devised, moving from sequence and shape to density of presentation, in glycoconjugates and membranes. It is flanked by an illustration of the range of modes for the topological display of binding sites in human lectins, via covalent and non-covalent clustering, to document the remarkable degree of sophistication reached within this recognition system. The discovery of orchestration of glycan display with presence of the matching effector in space and time and its functional consequences, e.g. to keep activated T effector cells under control preventing auto-aggression or to drive pancreatic cancer cells into anoikis, sets examples of clinical relevance on how this concept is realized. In summary, the combination of sequence/shape and topology (e.g. local density of cognate sites, together with cross-linking capacity of lectins to elicit efficient post-binding signalling) appears to be the key for the understanding of molecular specificity. At the same time, it makes intricate dynamic regulation and perspectives for rational drug design possible. Figuring out the details is a challenge for interdisciplinary research involving fields from computational chemistry to molecular medicine.

Introduction

Cell sociology depends on molecular interactions with the environment. These recognition processes underlie the decision whether a cell can, for example, attach to a certain matrix or be responsive to potential effectors. The enormous advances in sequencing nucleic acids and proteins as well as in monitoring expression profiles by chip and proteomics technologies have led to profound insights into the versatility of the proteome as a platform for structure-activity considerations. However, a simple consideration of structural aspects with an eye on theoretical coding capacity in oligomers, as presented in Figure 1, reveals inherent limits of the linear structures of nucleic acids and proteins: linkage biochemistry with phosphodiester/peptide bonds mostly restricts the way monomers can be joined to a single mode. In other words, an oligomer of these two classes of biomolecules is entirely characterized by the sequence, except for rare cases of 2',5'-phosphodiester and isopeptide bonds. If at this level variability can yet commonly enter oligomer formation, then the coding capacity will automatically increase. The ensuing gain, however, comes at the (analytical) expense to make complete sequence determination much more demanding, and this is the case for carbohydrates.

Figure 1. Illustration of the linkage points for oligomer formation in biomolecules by arrows. The phosphodiester bond in nucleic acid biosynthesis (a) and the peptide bond in protein biosynthesis (b) yield linear oligomers. In contrast, the glycosidic linkage in oligosaccharides can involve any hydroxyl group, opening the way to linear and also branched structures (c). Using d-glucose as an example, its active form UDP-Glc allows conjugation of this sugar to carbohydrate acceptors to any hydroxyl group, as symbolized by arrows directed towards the hydroxy groups. The anomeric position in chain elongation can vary, as symbolized by two bold arrows pointing away from the molecule (from [5], with permission).

Simple visual inspection of the carbohydrate section in Figure 1, makes it readily apparent that sugars indeed offer new opportunities for structural variability. The chemistry of connecting monosaccharides to glycan chains allows to exploit both the anomeric nature of the glycosidic bond (α/β) and further diversity arising from changing the linkage position (Figure 1). The example of the 1,4-linked glucose polymers cellulose (β) and glycogen/starch (α) supports the significance of the anomer position, and an α(or β)1–4 linkage is just one of the five theoretically possible ways to build diglucosides by connecting the anomeric centre to a hydroxyl group. In nature, divergence in linkage position can in fact be nearly this complete. The sugar l-fucose (please see below for its detection as part of in AB0 blood-group epitopes by lectin application) can be added to glycan chains of glycoproteins in α1,2/3/4/6 positions (please note required sophistication on the level of the glycosyltransferases) to build AB0(H)/Lea/b/Lex/y blood-group epitopes and the core fucosylation in complex-type N-glycans [1, 2]. These two examples highlight the special features of carbohydrates, often not realized when focusing on their roles as biochemical fuel or as cell-wall concrete. In view of their ubiquitous presence as integral part of glycoproteins and glycolipids [3], a much broader role is more than likely, especially when considering the complexity of the enzymatic machinery for glycan assembly indicated for fucosyltransferases. This notion is graphically expressed by referring to the building blocks (‘letters’) of cellular glycans as the third alphabet of life, owing to their special chemical properties, which enable high-density information coding: the basis of the concept of the sugar code [3, 4]. Using this term implies the question on the individual members of this alphabet, the ABC of the sugar code.

The structures of the individual ‘letters’ of the alphabet for the sugar language are illustrated in Figure 2, along with their acceptor position(s) in glycoconjugates [5]. As seen, sugars which are present within chains such as mannose use the full theoretical capacity for versatility. This type of linkage-point variability already mentioned above ensures that oligosaccharides surpass nucleic acids and proteins by orders of magnitude in the capacity to build isomer panels (‘code words’) [6]. Additionally, branching is rather common in glycoprotein glycans (only less than 20% of 3299 examined mammalian glycans are linear, more than 50% contain one or two branch sites, the rest even more [7]). Typically, sialic acid and fucose reside at branch ends (this explains their comparatively reduced acceptor range, given in Figure 2), along with some other moieties such as galactose (Gal), N-acetylgalactosamine (GalNAc) or N-acetylglucosamine (GlcNAc), and there is a further mode to increase structural complexity. Following their synthesis, glycan chains can then be subjected to reversible additions. They are comparable in specificity and functionality with the site-specific post-translational modifications of distinct amino acid side chains in proteins. The introduction of specific substitutions into the mentioned (and other) units, e.g. sulfation in a branch-end GalNAc moiety [8] or in GlcNAc (O- and N-sulfation possible) of glycosaminoglycan chains [9], exemplifies this frequently taken route to further increase the coding capacity by sugars. Physiologically important cases involving phosphorylation and sulfation, here at least 35 enzymes are responsible for this modification in the human Golgi region, are presented in Figure 3 (please see legend for medical relevance).

Figure 2. Illustration of the alphabet of the sugar language. Structural representation, name and symbol as well as the set of known acceptor positions (arrows) in glycoconjugates are given for each letter. Four sugars have l-configuration: fucose (6-deoxy-l-galactose), rhamnose (6-deoxy-l-mannose) and arabinose are introduced during chain elongation, whereas l-iduronic acid (IdoA) results from post-synthetic epimerization of glucuronic acid at C-5. The ¹C₄ conformation of IdoA (a) is in equilibrium with the ²SO form (b) in glycosaminoglycan chains where this uronic acid can be 2-sulfated (please see Fig.3d). All other “letters” are d-sugars. Neu5Ac, one of the more than 50 sialic acids, often terminates sugar chains in animal glycoconjugates. Kdo is a constituent of lipopolysaccharides in the cell walls of Gram-negative bacteria and is also found in cell wall polysaccharides of green algae and higher plants. Foreign to mammalian glycobiochemistry, microbial polysaccharides contain the furanose ring form of d-galactose and also d-/l-arabinose indicated by an italic “f” derived from the heterocyclus furan. The α-anomer is prevalent for the pentose arabinose, e.g. in mycobacterial cell wall arabinogalactan and lipoarabinomannan. β1–5/6-Linked galactofuranoside is present in the arabinogalactan and the β1–3/6 linkage in lipopolysaccharides (from [5], with permission).

Figure 3. Illustration of phosphorylated (phosphated) and sulfated (sulfurylated) glycan “words”. 6-Phosphorylation of a mannose moiety (in the context of a mannose-rich pentasaccharide) is the key section of a routing signal in lysosomal enzymes (a), 4-sulfation of the GalNAcβ1–4GlcNAc (LacdiNAc) epitope forms the “postal code” for clearance from circulation by hepatic endothelial cells of pituitary glycoprotein hormones labeled in such a way (b), the HNK-1 (human natural killer-1) epitope (3-sulfated GlcAβ1–3Galβ1–4GlcNAc) is involved in cell adhesion/migration in the nervous system (c) and the encircled 3-O-sulfation in the pentasaccharide's center is essential for heparin's anti-coagulant activity (d). All sugars are in their pyranose form. Please note that the central glucosamine unit has N,O-trisulfation and that the 2-sulfated IdoA, given in the ¹C₄ conformation, can also adopt the hinge-like ²SO skew-boat structure (please see Figure 2; about 60% or more for the ²SO form in equilibrium depending on the structural context) when present within glycosaminoglycan chains of the proteoglycan heparin. 2-Sulfation of IdoA serves two purposes: favoring the hinge-like ²SO conformation and precluding re-conversion to GlcA (from [5], with permission).

In summary, their chemical properties qualify carbohydrates to provide the most versatile toolbox to generate biochemical signals. In addition to enabling the exceptional structural diversity, deliberating their abilities to contribute to molecular recognition is rewarding. Both directional acceptor/donor hydrogen bonds (via hydroxyl groups) and van der Waals interactions/stacking (via patches of polarized C-H bonds) invite a rendezvous with proteins [3, 4]. What has become a burgeoning research field, i.e. work on structure and functions of the lectin class of glycan receptors [3, 4], started back in 1860 with experiments on snake venom [10].

Translation of the Sugar Code by Proteins

Carbohydrate-binding proteins, which do not act as enzymes on the glycans (e.g. glycosyltransferases, glycosidases or sulfotransferases), are classified into three categories: sensor/transport proteins for free mono- to oligosaccharides, antibodies, and lectins. This separation is reflected in the current definition of the term ‘lectin', which are binding partners for glycoconjugate glycans [10]. Occurrence of lectins is widespread, ranging from viruses and eubacteria, plants and invertebrates to mammals [11–13]. In support of the given notion for the physiological importance of the third alphabet of life, the structural diversity of glycans built from the set of units shown in Figure 2 and their substituted derivatives (see Figure 3) should be matched by the development of a wide array of recognition sites. Indeed, the establishment of carbohydrate reactivity in at least 15 folding patterns of proteins in animals, as compiled in Table 1, signifies that the structural platform for reading and translating sugar-encoded information is elaborate, definitely not a singular invention for one particular fold.

Table 1. Overview of protein folds with lectin activity

type of fold

example for lectin

example for ligand

C-type

asialoglycoprotein receptor,

collectins, selectins

Fuc, Gal, GalNAc, Man, heparin tetrasaccharide

I-type (Ig fold)

N-CAM, TIM-3, siglecs

Man₆GlcNAc₂, HNK-1 epitope, α2,3/6-sialylated glycans

P-type

mannose-6-phosphate receptors (MR) and proteins with MR homology domain (erlectin, OS-9)

Man-6-phosphate, Man5,8GlcNAc₂

β-sandwich (jelly-roll)

a) galectins

b) calnexin, calreticulin

c) ERGIC-53

d) CRDa of Fbs1 in SCF E3 ubiquitin ligase and peptide-N-glycanase

β-galactosides

Glc₁Man₉GlcNAc₂

ManxGlcNAc₂

Man₃GlcNAc₂; mannopentaose

e) pentraxins

glycosaminoglycans, MOβDG, 3-sulfated Gal, GalNAc and GlcA, Man-6-phosphate

f) G-domains of the LNS family (laminin, agrin)

heparin

β-trefoil

a) fibroblast growth factors

b) cystein-rich domain of C-type macrophage mannose receptor

c) lectin domain in GalNAc-Tsb involved in mucin-type O-glycosylation

d) hemolytic lectin CEL-III of sea cucumber and lectin EW29 of earthworm

heparan sulfate

GalNAc-4-sulfate in LacdiNAc

GalNAc

Gal

β-propeller

a) 4-bladed: tachylectin-3

b) 5-bladed: tachylectin-2

c) 6-bladed: tachylectin-1

S-type lipopolysaccharide GlcNAc/GalNAc

KDO

β-prism I

secretory proteins zg16p/b

not defined

β-prism II

pufferfish (fugu) lectin

Man

β-barrel with jelly-roll topology

tachylectin-4, eel (Anguilla anguilla) agglutinin, Xenopus

Fuc

fibrinogen-like domain

a) ficolins

b) intelectins (mammalian, Xenopus)

c) tachylectin-5

d) slug (Limax flavus) lectin

GlcNAc

Galf, pentoses

N-acetylated sugars

sialic acid

link module

CD44, TSG-6, LYVE-1, aggregating proteoglycans

hyaluronic acid

hevein-like domain

tachycytin and spider (Selenocosmia huwena) neurotoxin; cobra venom cardiotoxin

GalNAc; heparin-derived disaccharide

(β/α)₈ barrel

(glycoside hydrolase family 18)

YKL-40 (human cartilage glycoprotein-39; chitinase-like lectin)

(GlcNAc)n

short consensus repeat

(complement control protein module)

factor H (complement regulator)

glycosaminoglycans, sialic acid

acarbohydrate recognition domain, bN-acetylgalactosaminyltransferases,
cplease see Figure 2; adapted from [13] and extended, with permission

On the level of the glycans, the selection process between non-cognate and cognate epitopes for a lectin is expected to be of exquisite specificity which ensures the precision of lectin functionality. Despite the abundant presence of glycoconjugates on and around cells (the term “glycocalyx” is emblematic for this situation), lectins are capable to target distinct counter-receptors to fulfil specific assignments, for example to allow leukocytes to arrest at sites of inflammation (aptly termed selectins, a group of C-type lectins; see upper part of Table 1) or to regulate the growth of inflammatory and tumour cells [4, 14, 15]. In the latter process cascade, the proto-type homodimeric galectin-1, a cross-linking device with β-sandwich folding, elicits anoikis induction/growth inhibition in neuroblastoma/carcinoma cells or T effector cells (responsible for onset of autoimmune dys-regulation) by complex formation with the α₅β₁-integrin or ganglioside GM1, respectively [16–19]. To explain the biochemical reasons why a certain glycoprotein/glycolipid serves as counter-receptor, depending on the cell type, is a central problem for functional glycomics. Strategic integration of various experimental approaches is required for this pressing issue to be resolved satisfactorily [4]. These efforts carry with them a promise for developing innovative medical applications. Toward this end, we first focus on the glycan side and systematically dissect the levels at which the glycan can regulate affinity in carbohydrate – protein (lectin) interactions. As a guideline for the following, Table 2 presents the overview of these parameters, which cooperate to turn a carbohydrate epitope into a privileged docking point for a lectin.

Six Levels of Affinity Regulation

The first level is defined by the selection process of the “letters” shown in Figure 2. Prior to reaching the current classification of lectins based on sequence homology and folding pattern (for an overview, see Table 1), a technically simple and robust method delivered such data for a proper way to allocate proteins to categories. It was based on the capacity of at least bivalent proteins to agglutinate cells, technically most easily erythrocytes. As reflected in the given definition of the term ‘lectin’ with its emphasis on sugar binding, the inhibition of this haemagglutination (or polysaccharide precipitation) by mono- or disaccharides led to a common system to assign (phytohaem)agglutinins to groups, mannose- or galactose-specific lectins, to name some [10]. Testing a panel of glyco-compounds accordingly will thus reveal the sugar specificity expressed in terms of the most potent compound as a measure of affinity. Historically, a respective milestone was the delineation that human AB0(H) blood-group determinants are based on sugars. In detail, the agglutination of human erythrocytes of blood-group 0 status by the eel (Anguilla anguilla) lectin was inhibited most by α-methyl-l-fucopyranoside, 16fold less so by its β-anomer [20]. Other sugars such as d-fucose, arabinose, glucose, rhamnose or xylose were only very weakly active or not at all [20].

Of course, the simplicity of this assay spurred activity to detect lectins in diverse sources and with various specificities [10]. For example, the agglutination by the first member of the galectin family (from the electric organ tissue of the electric eel Electrophorus electricus) is precluded by the presence of a Gal-Glc(NAc) disaccharide (the β1,4-linked lactose or N-acetyllactosamine but not α1,6-linked melibiose) [21]. With this information presented, the first level of affinity regulation is reached, in terms of mono- or disaccharides (Table 2). The agglutinin is characterized as lectin with specificity for such compounds. More closely looking into the family of galectins, the growing availability of various disaccharides solidified the notion for the importance of linkage points, as attested by affinity differences between β1,3/4-linked N-acetyllactosamine [22]. Even more important, gaining access to oligosaccharides, benefiting from the advances in synthetic carbohydrate chemistry [23], enabled to discern stringent structure-activity relationships and marked lectin affinity for distinct sugar epitopes, e.g. the blood group A determinant in the case of human galectin-3 [22]. Moving from mono- and disaccharides to more complex glycans takes affinity regulation to the second level (Table 2). Progress in preparative work on carbohydrates thus fuelled advances in our understanding of the size of contact area between a lectin and ligands. At the same time, the di- and oligosaccharides became objects of structural work themselves, by NMR spectroscopic analysis and by molecular modelling [24–26].

Table 2. Six levels of regulation of affinity for binding of a glycan to a lectina

1.

mono- and disaccharides (incl. anomeric position, linkage points and substitutions)

2.

oligosaccharides (incl. branching and substitutions)

3.

shape parameters of oligosaccharides

a.

shape of oligosacchride (differential conformer selection)

b.

differences in conformational flexibility between isomers

4.

spatial parameters of glycans in natural glycoconjugates

a.

shape of glycan chain (example: modulation of conformational equilibrium by substitutions not acting as lectin ligand such as core fucosylation or introduction of bisecting GlcNAc in N-glycans, influence of protein part)

b.

cluster effect with bi- to pentaantennary N-glycans or branched O-glycans (incl. modulation by substitutions, see a.)

5.

cluster effect with neighboring glycan chains on the same glycoprotein (e.g. in mucins)

6.

cluster effect with cellular glycoconjugates in spatial vicinity, e.g. in membranes with glycoproteins, glycolipids or complexes thereof, especially when presented in microdomains; modulation possible by dynamic remodeling of glycan chain, e.g. by enzymatic desialylation

aadapted from [13] and extended, with permission

These combined efforts revealed a remarkable property of many oligosaccharides: their limited flexibility that induces often the glycan to adopt only a few energetically privileged conformers, with implications for bio-recognition [4, 26]. A selection of preformed conformers for binding constitutes an entropic advantage in the thermodynamic balance sheet, when compared to a highly flexible ligand, and dynamic shifts in the shape profile have the potential to modulate affinity [4, 26]. The case study on galectin-1 and the pentameric lectin part of the cholera toxin (AB₅), which compete for binding to the pentasaccharide of ganglioside GM1, revealed selection of two different conformers, entailing the obvious perspective to exploit this disparity for the design of lectin-type-specific inhibitors [4]. A change of the shape, moving from the two dimensions of the structural formula to three dimensions in our concept, will affect the extent of complementarity with a protein in bio-recognition. In the given case, each lectin actually accommodates one from the three low-energy constellations how the sialic acid and the central galactose moiety are positioned. The α2,3-sialylgalactose can thus be considered to preferentially exist as three ‘keys', one of them fitting into a ‘lock’ termed galectin-1, another into a B-subunit of the bacterial toxin. If this linkage were in α2,6-position, now involving the C5-C6 bond of galactose (an additional degree of rotational freedom), the binding properties will be completely different, in terms of direct contact (shape) and of flexibility. In the terminology of the sugar code, these two isomers, i.e. the α2,3/6-sialylgalactosides, which decorate the branch ends of glycan chains, are code words sui generis. In summary, the shape of the ligand and the extent of its flexibility factor into affinity regulation determine the level three (Table 2). To see these principles of lectin binding at work, the reader is referred to a movie illustrating the properties of the binding partners and then the complex formation in the already mentioned case of galectin-3 and the blood-group A tetrasaccharide. Flexible docking on both sides is accomplished with the HADDOCK version 2.1 (see [27] for details on access information).

Up to this stage of affinity regulation, we have dealt with free saccharides, not glycan chains in natural glycoconjugates. Starting with the level four, these structural aspects will now be taken under scrutiny. They have a strong influence on the presentation of the carbohydrate ligand in its natural context, with increasing degree of complexity up to the dynamics of membrane microdomains. A crucial factor for affinity is the local density of ligand presentation. This topological parameter can be altered by several means. The first is the introduction of so-called core substitutions to N-glycans, i.e. the presence of core fucosylation (in α1,6-linkage in contrast to α1,2-linkage in blood-group AB0 epitopes) and the bisecting GlcNAc moiety [4, 28, 29]. The additions to N-glycans occur non-randomly and have a marked influence on glycoprotein functionality, e.g. in binding antibody-antigen complexes to Fcγ receptors (e.g. FcγRIII), where the core-fucose unit can impair the high affinity of antibodies seen without this modification [30]. Comprehensive molecular modeling for biantennary N-glycans has disclosed the enormous impact of these two substitutions on the conformational equilibrium of the respective N-glycan chain, which translates into affinity alterations for lectin binding [31]. Of potential clinical relevance, the rate of hepatic uptake of pharma-glycoproteins, a common route for their clearance from serum and thus governing their pharmacodynamics, can now be tailored rationally by selecting the cell system for recombinant production accordingly [31]. The substitutions thus work on the glycan chains like molecular switches, one aspect of level four (Table 2).

The second aspect on this level is attributed to altering the local density of lectin ligands by branching. The effectiveness of this structural parameter was first described by measuring the reactivity of mono-, bi-, and trivalent galactose-terminated glycans to the mammalian asialoglycoprotein receptor [32]. The numerical increase in valency resulted in a geometrical increase in affinity, an effect termed the glycoside cluster effect [33, 34]. Following this pioneering work, the natural role model has encouraged the respective design of glycoclusters with the aim to produce potent lectin inhibitors for medical applications [33–35]. The combination of the most suitable cluster design (in terms of affinity and discriminatory efficiency) with recruiting carbohydrate derivatives provides a perspective for rational optimization of inhibitor synthesis, an example for galectin-3 reported recently [36].

Reaching the same aim as by branching (level four b), the cluster effect can likewise be achieved by increasing the density of glycan chains on a scaffold, e.g. on a protein backbone of a mucin (level five). The resulting marked affinity enhancement for lectins can be explained by applying kinetic terms: the macroscopic off-rate of the lectin, when dissociating from a complex with a ligand, appears decreased at sufficient ligand density, likely coupled with an increase in the on-rate [37]. A lectin will thus switch places and oscillate between neighbouring sites instead of losing contact to the glycoconjugate. Spatial vicinity can be generated not only by cluster formation on the same scaffold (level five). Microdomains in membranes are ideal platforms for bringing certain candidates for high-affinity binding together, an example for level six. Indeed, the ganglioside GM1 introduced above becomes a counter-receptor in this constellation, as shown by the detrimental effect of cholesterol depletion of membranes to harm microdomain integrity, and tight association with glycoproteins (e.g. with the α₅β₁-integrin) can guide the post-binding signalling [38, 39]. The contact site itself, the pentasaccharide, can be made accessible on demand by dynamic glycan remodelling on the membrane.

Cell surface glyco-enzymes (glycosidases, glycosyltransferases), which are under strict control, can remove or establish contact sites. A graphic example is the up-regulation of a cell surface ganglioside sialidase on neuroblastoma cells, turning higher sialylated gangliosides into galectin-1-reactive ganglioside GM1, the signal to initiate cell growth inhibition [39]. In a broader context, reactivity to lectins can hereby be modulated differentially. Looking at siglecs it can be abolished by desialylation, while recognition features for certain C-type lectins or galectins are installed [14, 40]. This dynamic remodelling can be the prerequisite for cluster (microdomain) generation, therefore to high affinity, connecting the enzymatic machinery for performing structural alterations in situ with recognition by lectins (level six). After the completion of the description of the six levels listed in Table 2, it is mandatory to immediately mention that the intricate versatility of carbohydrate-protein interactions is also rooted in the structural design of lectins. As highlighted in Table 2, spatial parameters are salient, and it is not surprising that modular display of these sites matters, besides the folding pattern summarized in Table 1 and fine-structural aspects of the architecture of the contact site (see the movie for galectin-3 for details in a model case [27]). The family of C-type lectins, defined by a folding with two anti-parallel β-strands and two α-helices, is an illustrative example for the multitude of various types of display, from monomers to non-covalent/tandem-repeat-type oligovalent clusters [41].

Modular Display of Lectin Sites:
Regulation of both Affinity and Selectivity

Figure 4. Illustration of the strategic ways how carbohydrate recognition domains (CRDs) in animal lectins are positioned to reach optimal ligand selection (for example to separate self from non-self glycan profiles in innate immunity) and topological complementarity. From left to right, the CRD display in the three subtypes within the galectin family (chimeric, proto-type and tandem-repeat-type arrangements binding to a ganglioside or a branched complex-type N-glycan without or with terminal α2,3-sialylation), the presentation of CRDs (C-type or fibrinogen-like domain) in serum and surfactant collectins or ficolins connected to their collagenous stalks and the non-covalent association of binding sites in transmembrane C-type lectins by α-helical coiled-coil stalks (for example asialoglycoprotein and Kupffer cell receptors, the scavenger receptor C-type lectin, CD23, DC-SIGN or DC-SIGNR) are given. Similar to tandem-repeat-type galectins the C-type family of lectins also has a branch of members with this design, i.e. immulectins-1, -2 and 3. Next, the tandem-repeat display in the mannose-specific macrophage receptor (also found on dendritic cells, hepatic endothelial cells, kidney mesangial cells, retinal pigment epithelial cells and tracheal smooth muscle cells) and the related C-type lectin Endo180, an endocytic receptor for glycosylated collagen with eight domains, as well as in the cation-independent P-type lectin with 15 domains is presented. Capacity for sugar binding is confined to only few domains as depicted. The occurrence of lectin activity for GalNAc-4-SO₄-bearing pituitary glycoprotein hormones in the cysteine-rich domain, a member of the β-trefoil fold family with one (QxW)₃ domain in the N-terminal section of the macrophage mannose receptor (amino acids 8–128), which is linked via a fibronectin-type-II-repeat-containing module to the tandem-repeat section, is also included into the schematic drawing for these lectins with more than one type of CRD per protein chain. Moving further to the right side, the association of a distal CRD in selectins (attached to an epidermal-growth-factor (EGF)-like domain and two to nine complement-binding consensus repeats) or in the siglec subfamily of I-type lectins using 1–16 C2-set immunoglobulin-like units as spacer equivalents to let the CRD reach out to contact ligands and to modulate capacity to serve in cis- or trans-interactions on the cell surface is shown. The force-dependent alterations of the topological arrangement of the two distal domains in selectins accounts for catch bonds of selectins, a canonical immunoreceptor tyrosine-based inhibitory motif (ITIM) together with a putative tyrosine-based motif is frequently present in the intracellular portion of siglecs. C2-set domains linked to fibronectin-type-III repeats establish the extracellular section of the I-type lectins L1 and neural cell adhesion molecule (N-CAM). In the matrix, the modular proteoglycans (hyalectans/lecticans: aggrecan, brevican, neurocan and versican) interact a.) with hyaluronan (and also link protein) via the link-protein-type modules of the N-terminal G1 domain (and an Ig-like module), b.) with receptors binding to the glycosaminoglycan chains in the central region and c.) with carbohydrates or proteins (fibulins-1 and -2 and tenascin-R) via the C-type lectin-like domain flanked by EGF-like and complement-binding consensus repeat modules (kindly provided by H. Kaltner; from [13], with permission)

In Figure 4 it can be seen an overview of ways how carbohydrate recognition domains are arranged to let them spot their targets. Local density can be varied, and, remarkably, association with other domains can implement properties conducive for functionality (for detailed explanations, see legend to Figure 4). Covalent connections between the modules facilitate the generation of tandem-repeat-type effectors with identical or different domains. Non-covalent association, as depicted for the i) asialo-glycoprotein receptor mentioned above as role model for the glycoside cluster effect, ii) collectins which distinguish the glycan signatures of infectious organisms such as yeast and malignant cells from normal self-glycans [42] and iii) cross-linking homodimeric galectins such as galectin-1, is the alternative for bringing recognition sites for glycans into spatial vicinity (see Figure 4 and its legend for further information on the mentioned lectins). Of note, intra-family diversity in this parameter brings about functional divergence. Galectins-1 and -3, shown in the left part of Figure 4, can compete for the same counter-receptor but are fundamentally different in triggering post-binding signaling. Consequently, galectin-3 can block anti-tumuoral activities of galectin-1, in neuroblastoma cells [16] and in the anoikis induction in pancreatic carcinoma cells upon tumor suppressor p16INK4a restoration, to list examples with clinical relevance [43]. This competition has prompted the design of galectin-3-specific glycoclusters to neutralize its antagonistic action on galectin-1 [36].

Conclusions

In his thoughtful reflections on glycobiology [44], S. Roseman provides a simple answer to the question “why glycobiology has apparently lagged so far behind the other fields” (i.e. genomics or (functional) proteomics): “Glycoconjugates are much more complex, variegated, and difficult to study than proteins or nucleic acids”. The combination of structural analysis and synthetic chemistry with molecular modelling and the experimental monitoring of interactions with proteins (lectins) teach instructive lessons about the factors that can regulate the translation of the sugar code on the side of the glycans. The exquisite selectivity in physiological recognition and post-binding signalling, leading e.g. to regulation of growth and adhesion [3, 4], is attributed to six levels, from mono- or disaccharide binding to presentation of suited glycans as clusters in microdomains. The presented systematic dissection into these separate parameters has the purpose to give research a clear direction by sensitizing to relate e.g. arising structural information on glycan presentation by a glycoprotein to the potential impact of this constellation on lectin reactivity. Lectin-dependent cargo selection and routing in apical transport of glycoproteins substantiates this concept [45].The orchestration of changes in (glycoprotein) glycosylation and cell surface presence of a lectin effector by a tumour suppressor [17] sets a precedent for the intimate cooperation of both sides of glycan-protein interactions to achieve the required selectivity in terms of the binding partner (here for the α₅β₁-integrin) and thereby post-binding signalling (here caspase-8-dependent anoikis induction). The complexity of glycan display, based on the chemical properties of sugars, therefore ensures to generate the physiologically required diversity of sequence/shape/density signatures for a wide array of specific recognition processes.

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MIRAGE: Minimum Information Required for a Glycomics Experiment

William S. York* and René Ranzinger

Complex Carbohydrate Research Center, The University of Georgia,
315 Riverbend Road, Athens GA, 30602-4712, U.S.A.

E-Mail: *will@ccrc.uga.edu

Received: 31st January 2012/Published: 11th July 2012

Abstract

The interpretation, evaluation, and reproduction of glycomics and glycoproteomics experiments are impeded by the failure to provide scientists who consume the results of these analyses with sufficient information describing the methods used to obtain the analytical data. With the enthusiastic support of glycoscientists and journal editors, a new initiative to specify the Minimum Information Required for A Glycomics Experiment (MIRAGE) has been established to address the unique data reporting aspects of glycoanalytic experiments. MIRAGE aims neither to dictate or control the experimental techniques used in a glyco-analysis nor to establish a metric for judging the quality of an experiment. Rather, it merely enumerates the information (data and metadata) that should be provided when the results of a glycoanalytic study are submitted to a journal or database.

Introduction

Glycobiology is an emerging discipline that focuses on the biosynthesis, structure and biological functions of carbohydrates [1], which exist as pure glycans or as key structural and regulatory components of proteins and lipids. Our understanding of the roles of glycosylation in development and disease has increased dramatically in recent years, due in part to advances in analytical methods for identifying and quantifying very small amounts of complex glycans that are present in biological samples. These advances have facilitated research that revealed critical roles of glycosylation in development [2, 3] homeostasis, inflammation, vascular biology [4], neuromuscular disease [5] and a wide range of congenital disorders of glycosylation [6]. Advances in glycochemistry show great promise for the development of therapeutic and diagnostic tools [7], including prophylactic vaccines and cancer detection technologies [8].

Diversity in the biological functions of glycoconjugates (such as glycoproteins and glycolipids) is mirrored by the structural diversity of these complex molecules [9]. Glycan biosynthesis is an elaborate process involving the transfer of individual sugar residues from donor molecules such as nucleotide sugars to growing glycan chains [10]. These reactions are catalysed by glycosyltransferases that recognize both the donor substrate and the acceptor substrate (the growing chain) to produce structures that are often branched. The glycosyltransferases direct the formation of glycosidic linkages, connecting each individual monosaccharide at a defined location (i.e., linked to one of several available oxygen atoms on the acceptor residue). Formation of the glycosidic bond locks the oxygen forming the glycosyl linkage in either the “alpha” or “beta” orientation, which defines the anomeric configuration of the newly added monosaccharide residue. Glycosidic bond formation also locks the newly added monosaccharide residue in a specific ring form (furanosyl or pyranosyl) with either 5 or 6 atoms, respectively. Thus, in order to completely describe the structure of a glycan, the following features must be specified: (1) the identity of each monosaccharide residue; (2) the ring form (pyranosyl, furanosyl or open chain) of each monosaccharide residue; (3) the anomeric configuration of each residue; (4) the type and position of modifications of the monosaccharide (such as deoxygenation, double bonds, acetylation, sulphation); (5) the attachment site for glycosyl linkages between residues; (6) the residue sequence (and resulting molecular branching pattern). The extent to which each of these features can be established depends on the analytical and data processing methods employed. Therefore, a full description of the results of a glycomics analysis requires information regarding the specific methods used in gathering and processing the data [11].

Glycomics involves the identification and quantification of all detectable glycan structures in a sample derived from a particular organism, tissue, or cell type. Glycomics analyses are often focused on determining how the populations of various glycans change as a result of cell differentiation, tissue morphogenesis, disease progression or genetic manipulation. Due to the molecular and biological complexity of the glycosylation process, thorough reporting of the results of a glycomics experiment is highly challenging. The resulting data specifies the identity and quantity of relatively large, complex structures, whose precise molecular features are often inferred using prior knowledge, such as familiarity with a particular biosynthetic mechanism. Specifying the exact methods and assumptions that were used to assign and quantify reported structures will allow the biomedical scientist to appreciate the scope and depth of the analysis and to evaluate correlations between defined structural features and specific biological processes such as tissue development or disease progression. Of course, such analytical data is only useful if it is interpreted in light of the biological and experimental context in which the glycans were prepared for analysis.

Interpretation and reproducibility of glycomics data thus requires comprehensive meta-data, that is, information regarding the biological sample, sample handling, data acquisition and data processing. Standards specifying minimum meta-data sets have been established for several types of biomedical analysis, including microarray-based transcriptomics (MIAME) [12] and proteomics (MIAPE) [13]. MIAPE (Minimum Information About a Proteomics Experiment) consists of guidelines specifying “the minimum information that should be reported about a data set or an experimental process, to allow a reader to interpret and critically evaluate the conclusions reached, and to support their experimental corroboration” [14]. MIAPE specifies “neither the format in which information should be transferred nor the structure of any repository or document” [14] and is implemented as “a checklist of information that should be provided (for example about the protocols employed) when a data set is submitted to a public repository or when an experimental step is reported in a scientific publication” [14].

Taylor et al. [15] described the philosophical basis for the MIAPE standard:

“It has always been a matter of policy that the PSI should neither attempt to produce standard operating procedures specifying how particular techniques should be performed nor attempt to establish quality assessment benchmarks. We do not believe it is the job of this body to dictate to the proteomics community how it should perform experiments or analyses.”

Taylor and his co-authors also describe two fundamental criteria required for successful implementation of MIAPE: (1) sufficiency – “The ... guidelines should require sufficient information about a dataset and its experimental context to allow a reader to understand and critically evaluate the interpretation and conclusions, and to support their experimental corroboration.” and (2) practicability – “Achieving compliance ... should not be so burdensome as to prohibit its widespread use.”

The previously established standards, including as MIAME and MIAPE, are not independent, but have significant overlap. For example, much of the same information regarding the biological source of the analysed material is required for both microarray and proteomics analysis. Fortunately, a community of scientists has agreed to work together to coordinate diverse (but overlapping) checklists of the Minimum Information for Biological and Biomedical Investigations (MIBBI) [16]. Access to many of the domain-specific “minimum information” standards is available under the MIBBI umbrella (http://mibbi.org/).

Compliance with the MIAME and MIAPE standards is frequently stipulated as a prerequisite for publication of microarray and proteomics experiments, as including all of the data and metadata specified in these checklists helps ensure that the experiments can be understood, evaluated and reproduced. The same sets of information are required by databases that store microarray and proteomics data. Although the MIAME and MIAPE standards identify the specific data and metadata that is required to effectively describe the experiments, they do not specify how these data should be represented, and digital standards for the representation and exchange of these data is beyond the scope of MIAME and MIAPE. Nevertheless, such digital standards are required for the effective data handling by software applications. Complementary initiatives that establish standard file formats to represent the meta information describing these experiments, along with dictionaries and ontologies to maintain semantic consistency among databases, will provide more extensive access to database searches (e.g., to find experiments that were performed using the same setup parameters), automated comparison of experimental results and mining of experimental data in the light of the meta information.

MIRAGE

An international group of glycobiologists (Will York, Catherine Costello, Hans Kamerling, Jonathan Bones, Joe Zaia, Niclas Karlsson, and Stuart Haslam) was organized at the Consortium for Functional Glycomics Conference in Washington 2009 (http://glycomics.scripps.edu/CFGWorkshopApril2009.html) to address the minimum information issue in the glycomics field. A key goal of this working group was to work towards the establishment of a MIRAGE (Minimum Information Required for A Glycomics Experiment) standard that is similar to the MIAPE standard for proteomics data, but that addresses the distinctive challenges of reporting glycomics analyses. The initial discussions have been very well received by scientists in the glycobiology community, who recognize the urgent need for a standard like MIRAGE. International leaders in the development and application of new techniques for glycomics analysis and the development of infrastructure and tools for glycoinformatics have been joined by the editors of the most influential journals that publish glycomics and glycoproteomics research to express their enthusiastic support for a MIRAGE standard.

Similar to the MIAPE standard for proteomics, the MIRAGE standard should focus on the analytical technologies and data interpretation issues associated with glycomics analysis. It should thus describe all of the information that would be required to reproduce and process data that was used to characterize a group of glycan structures or an individual structure. In this context, MIRAGE should specify the techniques and assumptions used to deduce each structural feature (e.g., composition, linkage, anomeric configuration, ring form, etc.) of the reported glycan or glycoconjugate.

The practical limitations of high-throughput glycomics analysis have been discussed by members of the glycobiology community at several meetings, including Workshops sponsored by the Consortium for Functional Glycomics (http://glycomics.scripps.edu/CFGWorkshopApril2009.html, http://glycomics.scripps.edu/SubgroupWorkshop/AnnouncementOct2009.jpg) and the Warren Workshop for Glycan Analysis (http://www.biomedicine.gu.se/biomedicine/Charles_Warren_Workshop_III).

High-throughput methods (such as mass spectrometry) do not always provide explicit information that can be used to make complete, unambiguous structural assignments of all the isomeric structures present in an analyte, and assignments are often made on the basis of biological considerations. The community consensus is that glycomics data reporting standards (such as MIRAGE) should not preclude the publication of such assignments. Rather, the standard should facilitate identification of the assumptions (such as “biosynthetic rules”) that are invoked when explicit analytical data is not available. For example, when PNGase-F is used to release oligosaccharides from a mammalian glycoprotein, commonly accepted biochemical rules allow one to confidently propose that d-GlcpNAc (rather than just HexNAc) is the sugar residue at the reducing end of each of the oligosaccharides. By recognizing these issues, MIRAGE is intended to be a guide that improves the information content of a glycomics analysis report rather than an impediment to innovation or publication.

Figure 1. Typical experimental protocol for a glycomics analysis.

Due to the unique issues associated with glycomics analysis, a distinct MIRAGE standard (rather than an extension of MIAPE) is required. Many sample preparation issues are unique to glycomics (Figure 1). Furthermore, glycomics involves analysis of complex, branched molecules whose primary structures cannot (yet) be predicted in silico and that are often non-trivial to represent digitally or graphically. Another important aspect of glycomics is that it involves the analysis of molecules that have a high tendency for mass degeneracy.

Ideally, MIRAGE should allow each structural feature that is reported in a glycomics experiment to be individually identified along with the methods used to characterize or identify these features. It is important to note that each structure identified in a single glycomics or glycoproteomics analysis may be reported with a different confidence level. Special attention may be paid to particular glycans that are highly abundant or deemed especially relevant. Thus, the amount and type of analytical data used to make structural assignments can vary considerably within a single analysis. For example, a structure may be assigned solely on its mass (deduced from mass profiling data), ruling out alternative structures on the basis of glycosyl residue composition data and/or structural assumptions based on the biology and history of the sample. Other structures may be based on multi-stage tandem mass spectrometry (MSn), providing explicit information regarding the sequence, branching pattern and linkage pattern of the oligosaccharide. Different methods, such as NMR and High-Performance Liquid Chromatography (HPLC) can also provide information to assign structural details and/or quantify expression levels for individual glycans or glycoconjugates. Precisely identifying the method used for each structural and quantitative assignment makes it possible to reach informed conclusions when using these data to evaluate biological hypotheses. Thus, a key feature of MIRAGE is that it identifies analytical data and meta-data that, when provided, allows the depth of analysis to be evaluated for each reported structure.

The development and community-wide acceptance of a MIRAGE standard will be facilitated by reusing concepts and approaches that have been developed for other “minimum information” standards that are included in the MIBBI project. In keeping with the lessons learned during the development of these “minimum information” standards, MIRAGE should not dictate how experiments are performed or try to rank the quality of the experiment based on the provided information.

The importance of robust software for identifying, organizing and recording important metadata describing glycomics experiments cannot be overemphasized. Real-world implementation of the MIRAGE standard will be facilitated by making the collection of relevant metadata routine and consistent, especially if appropriate tools are available to help with this task. Furthermore, the data collection process will become easier once the analyst has done it a few times. Tools that read and parse data files generated by analytical instrumentation to automatically collect and record hardware configurations and data acquisition parameters are essential. User friendly and intuitive interfaces for template-based metadata entry will allow information collected for previous glycomics experiments to be reused, requiring the explicit entry of only those parameters that change from one experiment to the next. Templates describing frequently used analytical processes (experiments) can be shared, allowing a significant proportion of the required information to be specified by reusing information stored in these templates. The automatic generation of digital reports using well-defined data formats will facilitate the reuse and comparison of the data and meta-data. However, the development and implementation of such software tools is not part of the MIRAGE initiative. Rather, the defined checklists of meta information specified by MIRAGE will provide guidance for the software and database development.

Summary

The definition and establishment of MIRAGE will require effort from the glycomics community to achieve consensus regarding the critical information that should be reported and effort from individual glycoanalysts to collect, enter and transfer this information. Nevertheless, providing this minimum set of meta-information will bring significant benefits. Based on the enthusiastic support of members of the glycobiology community, including editors of several high-impact journals, we envision that the MIRAGE standard will be used to encourage authors to collect and provide information that allows deep understanding and reproducibility of a manuscript's content. This will facilitate evaluation of the depth of glycomics data and the comparison of data sets. We believe that it is critical to provide a uniform specification of glycan structural data, meta-data and annotations. By identifying and naming the most important data and meta-data describing glycomics experiments, MIRAGE will facilitate the improvement of databases for carbohydrate structures and the development of software tools for data mining and logical inference using glycomics data. This will increase the information of these databases and provide more robust computational methods to generate and evaluate hypotheses regarding the roles of glycosylation in development and disease.

In summary, the motivation, philosophy and benefits of a MIRAGE standard are described below.

Unique aspects of glycomics data acquisition, processing, interpretation and mining demand a new data-reporting standard – MIRAGE.

MIRAGE should borrow extensively from and overlap with MIAPE and other reporting standards.

MIRAGE will benefit glycoanalysts, glycobiologists and biomedical scientists in general in the evaluation and reproduction of published glycomics experiments.

MIRAGE will not implement software but provide the framework for developers of software and database projects by defining the set of information that should be stored and/or handled by these applications.

Bioinformaticians can facilitate implementation of MIRAGE by providing infrastructure for data processing, exchange, archiving and retrieval.

Realization of MIRAGE depends on leadership from respected members of the glycoanalytic field. A MIRAGE initiative led primarily by computer scientists is unlikely to be fully accepted by the scientific community, making it difficult to obtain funding to develop the infrastructure required for its effective implementation.

Outlook

As a result of the 2nd Beilstein Symposium on Glyco-Bioinformatics in 2011, a working group was formed with the mission to define the MIRAGE guidelines for reporting glycomics experiments. This group consists of informaticists and glycoanalysts (Sanjay Agravat, Daniel Kolarich, Masaki Kato, Joe Zaia, Matthew Campbell, Erdmann Rapp, Rene Ranzinger, Ryan McBride, Stuart Haslam, Weston Struwe, Will York). Development of MIRAGE will be overseen by a group of established leaders in the field of Glycomics. The Beilstein-Institut has expressed interest in assisting in the coordination and organization of this group, which will meet on a regular basis to define minimum information checklists for glycomics and glycoproteomics experiments. All results will be reported and made publicly available via the MIRAGE webpage (http://glycomics.ccrc.uga.edu/MIRAGE/). The group will work closely with the MIBBI project and other minimum information checklist initiatives.

Acknowledgements

The authors thank the members of the international group (Cathy Costello, Hans Kamerling, Jonathan Bones, Joe Zaia, Niclas Karlsson, and Stuart Haslam) who established the need for a MIRAGE standard and identified fundamental scientific and philosophical issues that should be addressed by this new reporting standard. The authors also thank the Consortium for Functional Glycomics (funded by the United States National Institute of General Medical Sciences) for supporting scientific workshops that resulted in establishment of this initial working group, and the Beilstein-Institut for supporting a workshop in the summer of 2011at which a formal MIRAGE initiative was established. The authors also wish to thank the NIH National Center for Research Resources for support of bioinformatics infrastructure development through the Integrated Technology Resource for Biomedical Glycomics (P41RR018502).

References

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Tailoring Enzymatic Reactions

Maria Ortiz-Soto and Jürgen Seibel*

Institute of Organic Chemistry, University of Würzburg,
Am Hubland, 97074 Würzburg, Germany

E-Mail: *seibel@chemie.uni-wuerzburg.de

Received: 7th October 2011/Published: 11th July 2012

Abstract

Carbohydrates remain as one of the most challenging fields of study mostly due to the immense catalogue of carbohydrate structures that can be obtained as a direct result of their non-template dependent synthesis. Owing their chemical nature, there exist a huge number of potential combinations for glycostructures with different complexity which makes their synthesis difficult and almost impossible in most cases using only a traditional chemical synthesis approach.

As glycans are involved in distinct physiological roles including energy store, infection, pathogenesis and cell signalling among other processes, studies dealing with carbohydrates synthesis and regulation have increase within the last decades. Furthermore, carbohydrates as well as other glycosylated compounds including antioxidants, drugs and different proteins find application in the food and cosmetic industry as well as in health care, thus controlled glycosylation is targeted in order to develop new potential therapeutic agents as well as functional foods ingredients.

A strategy combining protein and substrates engineering as well as classical chemical synthesis has been necessary in order to achieve the synthesis of tailor-made oligosaccharides and glycostructures. Different approaches are described in this review regarding the control of chemo-, stereo- and regio-selectivity of carbohydrate active enzymes that successfully allowed the synthesis of an expanded spectrum of new glycostructures including those synthesized from non-natural substrates.

Introduction

Among the four major classes of organic molecules in living systems, carbohydrates are the most abundant molecules in nature [1]. Carbohydrates are the primary energy-storage molecules in living organisms and play a crucial role in biological processes and microorganism's survival. Sugar polymers are involved in bacterial adherence, protection of microbial cells against desiccation, metal ion stress, antibiotics, toxic compounds, phagocytosis and phage attack [2–4]. In eukaryotic cells, complex carbohydrates are found covering the major part of the cell surface and are integral part of the extracellular matrix of epithelial cells [5]. Glycans are involved in many physiological and pathological events, including cell growth, migration, differentiation, tumour invasion, host-pathogen interactions, cell trafficking, and transmembrane signalling [6]. The expression of complex carbohydrates in glycoproteins, glycosphingolipids, proteoglycans and glycosylphosphatidylinositol-linked proteins is one of the most common post-translational modifications [6, 7].

Carbohydrates are one of the most challenging fields of study mostly due to the immense repertoire of carbohydrate structures that can be derived from its non-template dependent synthesis. While the synthesis of bacterial homo-polysaccharides usually requires the action of a single enzyme, eukaryotic glycosylation is far more complex involving dozens of genes. Glycosylation of proteins and lipids is carried out by a combined and ordered sequential action of glycosyltransferases and glycosidases in eukaryotic cell compartments such as the rough endoplasmic reticulum and Golgi apparatus [7]. The complex heterogeneity of carbohydrates is a direct result of several of their intrinsic characteristics: the ability of different types and numbers of sugar residues to form glycosidic bonds with one another, the structural characteristics of these molecules, the type of anomeric linkage, the position and the absence or presence of branching [1]. Due to these characteristics there exist a huge number of potential combinations for glycostructures with different complexity and properties.

Carbohydrates as well as other glycosylated compounds including antioxidants find application in the food and cosmetic industry as well as in health care. Increasing attention has been focused on diverse carbohydrates due to their nutraceutical properties. On the one hand natural and unnatural-occurring sugars are common ingredients in the so called functional foods. The world market for functional foods has been continuously growing during the last three decades, with an estimated size in 2003 of about US$ 33 billion, while the estimation for the European market exceeded US$ 2 billion in the same year [8]. On the other hand glycosylation of diverse molecules is targeted in order to develop new potential therapeutic agents including vaccines, glycoprotein therapeutics and glycosylated drugs. Glycosylation of bioactive and pharmacologically active compounds represent a challenge in the synthesis and diversification of carbohydrate-active molecules.

The synthesis of oligosaccharides and polysaccharides becomes a challenging goal for modern science as the high complexity of these molecules makes their production difficult by classical chemical synthesis [9]. Nevertheless, enzymatic and chemo-enzymatic synthesis of glycans using carbohydrate active enzymes appears as the most attractive option to overcome these drawbacks including selectivity. Thus, the production of tailor-made oligosaccharides and polysaccharide structures relies on the elucidation of the enzyme structural determinants driving chemo-, stereo- and region-selectivity, as well as on solvent and substrate engineering.

Beside Leloir-type glycosyltransferases that catalyse the synthesis of different carbohydrates using nucleotide-activated sugars as substrates, bacterial glycosyltransferases which catalyse the synthesis of various large glucose polymers from sucrose provide an alternative route for the synthesis of targeted glycostructures [10]. To date success in the field of enzyme specificity control has been achieved by a combination of substrate and genetic engineering using enzymes such as levansucrases from Bacillus subtilis, Bacillus megaterium and a glucansucrases from Streptococcus oralis among others [9–15].

Tailor-made Oligosaccharides and Fructo-oligosaccharides

Within the last 20 years, studies dealing with nutraceutical and functional foods have rapidly increased and so have the sales of products containing them. Several papers have been published so far describing the effects of oligosaccharides and glycosides in human health and nutrition, focus the crucial role that the degree of polymerization plays in sugars characteristics and physiological effects [16–18].

To date oligosaccharides included in several commercial products are obtained by controlled hydrolysis of large polymers usually extracted from plants and requiring additional purification steps, or by microbial synthesis using either β-d-fructofuranosidases or fructosyltransferases mostly from Aspergillus species.

The search for new carbohydrate synthesizing enzymes that allows the synthesis of specific groups of oligosaccharides as well as a broader range of glycosides has lately intensified pointing towards bacterial fructosyltransferases. These enzymes belong to the family 68 of glycoside hydrolases (EC 2.4.1.10) and synthesize fructose polymers with different linkage specificity and polymerization degrees using sucrose as natural substrate. Fructosyl-transfering enzymes are divided in levansucrases (LS) which synthesize a β2,6 linked polymer known as levan and inulosucrases which mainly form inulin, a β2,1 bound product. Although crystallographic structures of three levansucrases and one inulosucrase are now available including structures with different substrates, to date there is not enough information highlighting the differences either in the active site or in the nearest sub-sites that may support or influence linkage specificity in fructosyltransferases [15, 19, 20].

However, a model to explain the polymerization mechanism in polymer and oligosaccharides producing fructosyltransferases suggests that polymer synthesis obeys to a processive transglycosylation reaction, while the synthesis of oligosaccharides results from a nonprocessive mechanism controlled by specific amino acids in the catalytic domain [21]. Nevertheless, additional sub-sites beyond -1 (fructose residue) and +1 (glucose residue) where sucrose binding takes place were not identified. In spite of having the same active site architecture, B. subtilis and B. megaterium (SacB) LSs mainly synthesize high molecular weight fructans, whereas the homologous enzyme from G. diazotrophicus only synthesizes oligosaccharides with a minor amount of oligosaccharides.

Homann et al. [13] clarified the functional roles of different amino acids which are involved in the formation of different reaction products including those controlling the polymer molecular weight and turned the polymer synthesizing SacB from Bacillus megaterium into an oligosaccharide-producing enzyme, thus changing from a processive to a non-processive mechanism. Multiple sequence alignments of LS enzymes in combination with structural data of family 68 members allowed selection of the residues Trp94, Asp95, Val115, Leu118, Trp172, Ser173, Asn252, Arg256, Asp257, Glu350, Glu352, Arg370 and Tyr421 to explore the structural determinants which are responsible for the transfructosylation activity in B. megaterium SacB. Among the studied residues Asn252 was found to play an important role in the transfructosylation reaction, as the variant Asn252Ala completely stopped the polysaccharide production by reducing the coordination of a fructosyl unit in the +2 sub-site and being only able to forms short-chain oligosaccharides of up to three fructosyl units (nystose). Derived from biochemical studies of the constructed mutants and product structures determination, it was proposed that a high affinity in the sub-sites +2 and +3 is needed for binding the growing fructan chain in polymer synthesizing enzymes. Asn252 is strictly conserved in Gram-positive bacteria, whereas in Gram-negative bacteria this region shows variability. Additionally, it was the first example of an engineered FT bearing different product specificity without significantly changing its Km and kcat values.

Later on Strube et al. [15] demonstrated that polysaccharide synthesis in SacB is controlled by surface motifs beyond the sucrose binding site. Variants Y247A, Y247W, N252A, D257A, and K373A were characterized and their structures obtained at resolution between 2.0 and 1.75 Å, finding distinguishable sub-sites for polysaccharide synthesis. As the active site was found to remain unaltered by single mutations, changes in the transfructosylation products were only attributed to the direct effect of each mutation.

N252A, K373A and Y247A variants were found to eliminate the polysaccharide production after tetrasaccharides, hexasaccharides and decasaccharides synthesis respectively. These three amino acids form a platform for a possible stabilization of the acceptor fructan chain. The biochemical and the structural data show that Asn252 is located close to the sucrose binding site, whereas Lys373 and Tyr247 are clearly apart from the sucrose binding cavity of SacB. A deeper view into the role of the residue Lys373 revealed that the exchange of lysine in position 373 to arginine, leads to longer oligofructosides (9 fructosyl units). It was observed that although interactions between the functional amino groups of arginine in position 373 and the amino acid network as well as carbohydrate units are still possible, the interactions are reduced compared with the wild type enzyme. These changes stop the polymer formation. Additional mutants in the residue Y247 were also constructed, finding that SacB variants Y247A and Y247I form oligosaccharides consisting of up to nine fructosyl units, whereas variant Y247W forms the whole range of oligosaccharides compared with the wild type spectrum. These results indicate that an unpolar favoured stacking mechanism is possible with tyrosine as well as tryptophan in this location but not with alanine or isoleucine. Superposition of Y247W with D257A (the transition state stabilizing residue) confirms the key supporting role of Tyr247 in the oligo- and polysaccharide-forming “assembly line.” As previously reported, G. diazotrophicus levansucrase LsdA [22] forms mainly the β-(2,1)-linked trisaccharide 1-kestose, whereas the wild type SacB forms mainly polysaccharides. A structural comparison of both levansucrases enabled further insights into the functional role of Tyr247. A conformational difference in the surface motif Tyr247 was observed to be essential for polysaccharide synthesis in SacB, while the orientation of this motif in LsdA might block the polysaccharide chain, thus leading to the synthesis of short oligosaccharides of 3–5 carbohydrate units.

Figure 1. Potential pathway for the synthesis of oligosaccharides in B. megaterium SacB [13].

It was shown for the first time that amino acids outside the active site of a levansucrase have a well-defined and rationally explainable effect on the polymer formation activity (Figure 1). Due to the extensive structural data obtained from SacB variants Y247A, Y247W, N252A, and K373A any effect on the position of other amino acids was be excluded. Conformational analyses of variants Y247A, Y247W, N252A, and K373A revealed an unaltered active site architecture that was consistent with the kinetic parameters of these variants which were not significantly different compared with wild type SacB.

Based in these studies, it may be concluded that the identification of the sub-sites +2 to +9 enables now the synthesis of tailor-made short-chain fructo-oligosaccharides and opens the gate to similar studies on diverse levan and inulosucrases in order to fulfil the needs for functional carbohydrates to different applications.

Changing Linkage Specificity

It has been extensively reported that both the glycosidic bond specificity and carbohydrate degree of polymerization are two characteristics that strongly influence the carbohydrate fermentation during its transit through the digestive tract. Thus, the physiological effects, which are derived from this carbohydrates selective fermentation, are directly related with both properties underlying the importance of modulating sugar stereo- and regio-selectivity. Sanz et al. [23] studied the influence of both parameters on selective fermentation of glucose oligosaccharides by human gut bacteria. Glucobioses with α and β1,2 linkages showed to be particularly selective for bifidobacteria, and in general, α-glucosyl-glucose disaccharides showed more positive effects than β-isomers. trans-Galactooligosaccharides with linkages β1,6 and β1,3 have been shown to be selective for bifidobacteria, the β-galactosidases of which cleave such isomers faster than β1,4.

Streptococcus oralis dextranssucrase engineering has recently proved to be efficient for tailoring enzyme regio-specificity [9]. Glycosyltransferase R (GTFR) from S. oralis is a dextransucrase belonging to glycoside hydrolase family 70. Besides glucan production, GTFR catalyses the transglycosylation of different acceptor substrates, including alcohols and amino acids. Depending on the specific enzyme bacterial glucansucrases synthesize a variety of glucosidic linkages in their products, forming either α1,6-linked dextran, α1,3-linked mutan, α1,4 linked reuteran or an α1,3–1,6-alternating alternan. Two questions were recurrent about glucansucrases: how they control either oligosaccharide or polysaccharide synthesis and how their glycosidic linkage specificity is directed [9]. This information is required for the production of tailor-made saccharides.

Kralj et al. suggested that [14] variations in glucosidic linkage specificity appear to be based on relatively small differences in amino acid sequences in their sugar-binding acceptor sub-sites. This assumption was derived from mutagenesis of some amino acids of Lactobacillus reuteri 121 GTFA (reuteransucrase) probably involved in acceptor substrate binding. A triple mutant N1134S, N1135E and S1136V in a region immediately next to the transition state stabilizing residue Asp1133 converted GTFA from a mainly α1,4 to a mainly α1,6 synthesizing enzyme. Additionally, the mutation P1026V and I1029V in a region next to the nucleophile Asp1024 resulted in synthesis of a α-glucan containing only a very small percentage of α1,4 glucosidic linkages and increased percentage of α1,6 glucosidic linkages. Thus, in order to change the linkage specificity the NNS-motif of the reuteransucrase GTFA was changed by the SEV-motif that can be also found in the GTFR glucansucrase from S. oralis.

Figure 2. The triple mutant variant GTFR (R624G/V630I/D717A) effected a switch in the region-selectivity of the glycosylation from α1,6 to predominantly α1,3 linkage formation. The polymer synthesis was knocked out by the single mutation S628D [35].

The GTFR glucansucrase from S. oralis synthesizes a glucose polymer from sucrose which is mainly linked by α1,6-bonds and a minor amount of α1,3-bonds. Hellmuth et al. [9] have chosen the most conserved motif around the transition state stabilizer in glucansucrases for a random mutagenesis approach using this enzyme. The GTFR variant R624G/V630I/D717A showed a drastic switch in regio-selectivity, from dextran with mainly α1,6 linkages to a mutan-type polymer with predominantly α1,3 glucosidic linkages (Figure 2). It was proposed that R624G and V630I, both mutations near the transition state stabilizer, contribute to this change. Additionally, saturation mutagenesis at serine 628 residue switched the reaction towards the synthesis of short-chain oligosaccharides. Compared to the wild type enzyme, S628X variants exhibited tremendous changes in their short transglycosylation product spectra while polymer formation was completely stopped. The oligosaccharide yield of the prebiotic disaccharide isomaltose was dramatically increased from 1% to 47% and the yield of leucrose reached 64%. Recently, Pijning et al. [20] published for the first time the crystallographic structure of a glucansucrase. The structure of a 1031-residue fragment of GTF180 from Lactobacillus reuteri 180, both in the native state and in complexes with sucrose and maltose was described. Structural analysis, as well as docking studies allowed concluding that the enzyme sub-sites +1 and +2 are not very specific regarding the glycosidic linkage between the sugars, and can accommodate either α1,3, α1,4, or α1,6-linked glucose residues. To summarize, the linkage specificity and the oligosaccharides versus polysaccharide formation ratio could be controlled and engineered by site directed mutagenesis including the knowledge of one enzyme's structural features and functionalities.

Substrate-engineering – Substrate Directed Synthesis

As previously mentioned, different strategies should be addressed in order to expand the range of tailored products to meet the required specification for a targeted application in chemical, cosmetics, food or medical fields. While protein engineering allows the synthesis of a large variety of products with specific linkages and molecular weight, substrate-engineering expands the spectrum of new glycostructures synthesized from non-natural substrates. The synthesis of glycoethers and glycopeptides offers a potential strategy for the identification of new enzyme specificities and acceptor products which could not be inferred from sequence alignment.

Figure 3. (a) Substrate directed synthesis addresses the linkage selectivity; (b) Chemical nucleophilic substitution of the tosyl group with thio-sugars yields different glycoconjugates containing thioglycosidic linkages.

Seibel et al. [11] demonstrated new acceptor reactions using the S. oralis GTFR on substrate microarrays for glycosylation of immobilized oligosaccharides. The α-glycosylation by GTFR with sucrose and different alcohols and amino acid derivatives for the synthesis of glycoethers and glycosylated amino acids was also demonstrated. These syntheses are not easy to be obtained by chemical or enzymatic synthetic methods alone. Seibel et al. [12] also have synthesized new sucrose analogues using the B. subtilis NCIMB 11871 levansucrase, which may be used for the screening of new glycosyltransferase specificities and new variants of this class of enzymes, leading to further development of novel products. Later on, Hellmuth et al. [10] changed the chemoselectivity of glucansucrases GTFR from α1,6- to α1,2-, α1,3- or α1,4-linked glucose residue by introducing on tosyl-residue in position 6 of the acceptor molecule glucose. The new acceptor molecule most likely adapts a different orientation in the enzymes pocket. This explains the change in the linkage type. With this approach various complex glycoconjugates containing thioglycosidic linkages with the glycopyranosides (galactose, glucose, neuraminic acid) of choice were successfully constructed (Figure 3).

Thioglycosides are tolerated by most biological systems, but are less susceptible to acid/base or enzyme-mediated hydrolysis, contrary to the naturally occurring O-glycosides [24, 25]. In addition the allyl group may be modified by ozonolysis into an aldehyde group. The modified sugar thus obtained can now be attached to aglycons, such as peptides, natural products, drugs or solid supports, such as microtiter plates for example, by reductive amination [26] allowing the investigation of protein–carbohydrate interactions. The synthesized molecules may then be used in further experiments to identify carbohydrate-lectin and -selectin interactions of biological relevance. The combination of different acceptors and enzymes as well as chemical synthesis could lead to a powerful set of tools for glycosynthesis. Those studies will be expanded in future to other glucansucrases and glycosyltransferases for the synthesis of novel structures.

Metabolic Glyco-engineering

It has been extensively reported that disorders in protein glycosylation lead to multisystemic and severe diseases. Altered glycosylation patterns have been associated with many physiological and pathological events, including cell growth, migration, differentiation, tumour invasion, host-pathogen interactions, cell trafficking, and transmembrane signalling [6]. Additionally, modified cell surfaces through deglycosylation or by a false-escorted recognition processes can also lead to an increased risk of bacterial and viral infections [27]. Sialylation is a determinant factor in pathogens and toxins binding to animal cells via cell surface sialic acids (Sias) that are α2,3-, α2,6 or α2,8-linked to the glycan chains. For example, human influenza A/B viruses prefer α2,6- linked Sias, while undergoing a switch from α2,3-linkage preference during adaptation from animals to humans [28]. Recent data indicate that the biology of sialic acids involving around 60 genes shows more than 10 uniquely human genetic changes in comparison with our closest evolutionary relatives. For example, N-glycolylneuraminic (Neu5Gc) acid and N-acetylneuraminic acid (Neu5Ac) are the most common sialic acids found on mammalian cell types but Neu5Gc is claimed to be missing in normal human tissues [29].

Developing tools for investigating the cellular activity of glycans will help to elucidate the molecular basis for aberrant glycosylation in pathological processes including cancer. Through metabolic glyco-engineering, sugar-reporting groups can be displayed into the cell surface glycoconjugates, constituting a powerful method for imaging the localization, trafficking, and dynamics of glycans and isolating them for glyco-proteomic analysis [30–32]. Mahal et al. [30] reported a strategy for remodelling the cell surface by exploiting the substrate promiscuity of oligosaccharide biosynthetic pathways and using the natural metabolic processes of the cell to introduce a ketone group into cell surface–associated Sias residues. Authors proposed that engineered cell surfaces bearing unusual epitopes may be selective targets for drugs, radionuclides or imaging reagents. The viability of this approach was further demonstrated when engineered cells were coupled to biotin through selective conjugation to ketone groups, and selectively killed in the presence of a ricin A chain–avidin conjugate. In order to diversify the range of carbohydrates that may be displayed on cell surfaces, Oetke et al. [31] studied the ability of eukaryotic cells to uptake Sias from the extracellular space, finding that free sialic acid can be taken up and efficiently incorporated in a variety of human cell lines. Later on, Hsu et al. [32] reported the incorporation of alkynyl sugar monomers based on fucose and N-acetylmannosamine into fucosylated and sialylated glycans in several cancer cell lines, allowing the visualization of cell surface and intracellular glycoconjugates. The authors demonstrated that click-activated fluorogenic probes are practical tools for efficiently and selectively labelling alkynyl-modified glycans. Homann et al. [33] reported the synthesis and bioorthogonal metabolic incorporation of the sialic acid analogue N-(1-oxohex-5-ynyl) neuraminic acid (Neu5Hex) into the cell-surface glycocalyx of a human larynx carcinoma cell line (HEp-2) and its fluorescence labelling by click chemistry. The bioorthogonal modification, that is the introduction of hexyne, was carried out at the sialic acid acetyl residue at position C-5 which is reported to be prone to mammalian evolution processes.

HEp-2 cells were investigated because of their metabolic capability to incorporate 2-azidoacetylamino-2-deoxy-(1,3,4,6)-tetraacetyl-β-d-glucopyranoside (Ac4GlcNAz) by a diffusion process through the membrane of eukaryotic cells [34]. Neu5Hex was expected to enter the cell by the previously described pinocytosis processes or by a yet unknown internalization mechanism. It is proposed that Neu5Hex enters the nucleus and enhances the genetic feedback control of the GNE coding gene which blocks the synthesis of natural Neu5Ac [33] (Figure 4). The incorporation and cell surface presentation of the new substrate Neu5Hex was successful and the copper-catalysed [3+2] triazole formation (“click reaction”) proved very useful for the cell surface labelling because of its bioorthogonality (Figure 5). The metabolic labelling of human larynx carcinoma (HEp-2) could make possible the study of the influence of sialic acid in cell signalling and cell-cell interactions.

This method will provide useful information about their roles in cancer and ultimately can be potentially used for diagnostic and therapeutic purposes [32].

Figure 4. Metabolic pathway of Ac4GlcNAz and the genetic control of Neu5Ac synthesis by feedback inhibition [33]. The accumulation of Neu5Hex is proposed by its incubation with the target cell line as the synthesis of Neu5Ac is down-regulated. UDP: uridine diphosphate; GNE: UDP-N-acetylglucosamine 2 epimerase/N-acetylmannosamine kinase; ATP: adenosine triphosphate; PEP: phosphoenolpyruvate; CTP: cytidine triphosphate; PPi: pyrophosphate; DNA: deoxyribonucleic acid; mRNA: messenger ribonucleic acid.

Figure 5. Labelling of alkynylated neuraminic acid by azido-fluorescein [33].

Perspectives

In spite of the information concerning glycosylation disorders, the biochemical properties, as well as the structure-function relationship of the glycosyltransferases involved in the glycosylation process are, however, hardly understood.

The study of different human glycosyltransferases is currently in progress in our working group and is intended to contribute to the basic understanding of the biochemical and structural properties of the human glycosyltransferases regarding its modulation. In this sense structural studies of eukaryotic glycosyltransferases are also needed to add valuable information leading to the development of potential inhibitors and also to allow the synthesis of glycoproteins and glycoconjugates for therapeutic applications.

Conclusion

During the last years different approaches have been attempted in order to elucidate the structural determinants directly involved in the carbohydrate active enzymes selectivity. The main goal of this search is either modifying and/or expanding the functional range of target enzymes in order to control the synthesis of their products fulfilling the needs in understanding the nature's glycosylation repertoire and providing the chemical, food, cosmetics and pharmaceutical industries with sugars.

Besides protein engineering, the search for new functionalities has been extended to different fields including solvent and substrate engineering. This review of our science and others describes the production of tailor-made oligosaccharides and polysaccharide structures with potential application in health care and food industry that have been successfully achieved by combination of different strategies. It also describes the synthesis and cell surface display of sialic acid analogues for human larynx carcinoma labelling with potential application in diagnosis.

Acknowledgment

Maria Ortiz-Soto thanks the Deutscher Akademischer Austauschdienst (DAAD-A/09/78904) for postdoctoral financial support.

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Biophysical Studies on the Interactions of Bacterial Toxins

W. Bruce Turnbull

School of Chemistry and Astbury Centre for Structural Molecular Biology,
University of Leeds, Leeds, LS2 9JT, U.K.

E-Mail: w.b.turnbull@leeds.ac.uk

Received: 12th January 2012/Published: 11th July 2012

Abstract

Many diarrhoeal diseases such as cholera are caused by protein toxins that have an AB₅ hetero-oligomeric structure. The proteins comprise a single toxic A-subunit and a pentameric B-subunit that interacts with specific cell surface glycolipids. Inhibitors of such protein-carbohydrate interactions could provide prophylactic treatments for these debilitating diseases. In our work we aim to understand the binding interactions of multivalent inhibitors for bacterial toxins. Often a single biophysical technique is limited in the information it can provide, whereas a more complete picture can be constructed through an integrated approach using a broad range of biophysical methods. For example, the importance of protein and ligand dynamics in multivalent interactions is revealed when combinations of NMR spectroscopy, isothermal titration calorimetry, analytical ultracentrifugation and dynamic light scattering are used to study to different multivalent systems.

Introduction

A variety of enteropathogenic bacteria including Vibrio cholerae and some strains of Escherichia coli release protein toxins into the intestine of their host [1]. The toxins bind to specific glycolipid ligands on the surface of the cells that line the intestine. Following endocytosis and retrograde trafficking to the endoplasmic reticulum, the toxins are translocated into the cytosol where they initiate the biochemical events leading to potentially fatal diarrhoea [2]. Many of these bacterial toxins share a common AB₅ architecture in which a single toxic A-subunit is non-covalently associated with a cyclic pentamer of B-subunits [1]. While the B-pentamer is not itself toxic, it constitutes the carbohydrate-binding domain of the toxin and is thus essential for entry into cells. Therefore, inhibitors of these protein-carbohydrate interactions have potential as anti-diarrhoeal drugs.

Figure 1. (a) Cholera toxin B-pentamer (CTB) and (b) verotoxin B-pentamer (VTB) with their carbohydrate ligands GM1 and Gb₃, respectively. The carbohydrate structures highlighted in red indicate how many oligosaccharides bind to each protomer. The structures were prepared in PyMOL using Protein Data Bank files 3CHB.pdb and 1BOS.pdb.

Cholera toxin and verotoxin (also known as shiga-like toxin 1) are the most widely studied examples of AB₅ bacterial toxins. Although the B-subunits of these two toxins have essentially no sequence similarity [1], they have evolved to have the same protein fold and to function as lectins for glycolipids (Figure 1). They differ in the number and structure of the carbohydrates that they recognise: cholera toxin B-pentamer (CTB) has one binding per subunit for the branched pentasaccharide of ganglioside GM1 [3], while the verotoxin B-pentamer (VTB) has three binding sites per subunit for the trisaccharide portion of globotriaosyl ceramide (Gb3) [4]. More significant, however, is the difference in affinity of these interactions. The CTB-GM1 interaction is one of the highest affinity protein-carbohydrate interactions known with a dissociation constant (Kd) of 40 nM [5], whereas the highest affinity of the three binding sites on VTB has only a 1 mM Kd [6]. However, as all 15 of the binding sites on the VTB pentamer are arranged on the same face of the protein [4], it is possible for VTB to make many simultaneous interactions with Gb3 glycolipids in the cell membrane. Multivalent binding of this type is characteristic of most protein-carbohydrate interactions and can transform weak millimolar dissociation constants into sub-nanomolar avidities that are functionally useful [7–10]. In the case of VTB, it is clear that multivalency is absolutely essential for its function, but CTB also benefits from making multivalent interactions to enhance binding and facilitate the rate of endocytosis [11].

Figure 2. (a) cartoon representation of a pentavalent ligand binding to an AB₅ bacterial toxin; (b) Bundle's STARFISH ligand that can crosslink two VTB pentamers to form a sandwich complex.

Just as multivalency is important for biological function, it also provides a powerful strategy for inhibiting protein-carbohydrate interactions [7, 12]. If one could design a scaffold that could place a carbohydrate ligand into each protein binding site simultaneously (Figure 2a), then even if any individual oligosaccharide were to dissociate from the protein, the other ligand groups would hold it in close proximity to the protein surface, thus increasing the effective concentration of the oligosaccharide, and thus the probability of its rebinding to the protein. Multivalent inhibition can operate by several mechanisms including: chelation of binding sites; statistical increases in effective concentration; and aggregation mechanisms [8]. Dissecting multivalent inhibition mechanisms is thus a challenging pursuit, but can be achieved by using a combination of complementary biophysical techniques as illustrated by the following two case studies.

Multivalent Binding Influenced by Protein Dynamics

One of the most celebrated examples of multivalent inhibition is Bundle's “STARFISH” ligand for VTB (Figure 2b) [13]. The molecule comprises a pentavalent core derived from a modified glucose residue with five flexible arms, each of which bares two copies of the Gb3 trisaccharide. STARFISH was over a million times more active as an inhibitor of VTB adhesion than the parent monovalent Gb3 trisaccharide, thus demonstrating the power of the multivalent inhibition strategy.

The ligand was designed to crosslink two of the Gb3 binding sites in each of the five VTB subunits. However, a crystal structure of the VTB-STARFISH complex revealed that the carbohydrate ligand groups occupied only the highest affinity of the three binding sites on each protomer [13]. The remaining five Gb3 ligand groups instead engaged a second VTB pentamer to make a ternary sandwich complex (Figure 2b). While this could be interpreted as an artefact of the crystallisation procedure, subsequent studies suggested that the dimer of pentamers also formed in solution [14]. Although the inhibitor was very potent, the structural studies raised doubts about whether the divalent Gb3 groups at the end of each of the STARFISH arms were capable of simultaneous binding to sites 1 and 2 on the VTB surface. However, an NMR shift-perturbation experiment using ¹⁵N-enriched protein confirmed that a divalent Gb3 ligand (Figure 3c) could indeed interact with both sites 1 and 2 on VTB [15].

Figure 3. (a) ITC data for a titration of (c) divalent Gb3 ligand into a solution of VTB. The binding stoichiometry values (n) represent the number of divalent ligands binding to each protomer of VTB. (b) Signals from a ¹H-¹⁵N HSQC NMR titration of divalent Gb₃ into a solution of ¹⁵N-enriched VTB; the data show a significant increase in signal intensity for the Leu39 amide bond upon addition of the ligand.

Intrigued by the apparent differences in complexation for the divalent and decavalent ligands, we sought to measure the binding thermodynamics of the divalent ligand using isothermal titration calorimetry (ITC). This technique provides direct access to binding affinities, enthalpies and also to binding stoichiometries, in ideal cases. However, fitting the ITC data to a standard one-site model suggested that 4.5 divalent ligands bound to each VTB protomer (i.e., 22.5 divalent ligands per VTB pentamer) [16]. As such a high binding stoichiometry is not feasible in this system; the sigmoidal shape of the curve (Figure 3a) must be attributed to a different phenomenon.

Closer inspection of the shift-perturbation NMR data revealed that a number of the HSQC signals became more intense upon addition of the ligand (Figure 3) [16]. Such observations can be indicative of changes in protein dynamics upon binding. If a protein can interconvert between two different conformations, the observed NMR signal will be dependent on the rate of the interconversion process. In the fast exchange regime, the conformers interconvert many times during the timescale of the NMR experiment, giving rise to a single sharp signal in the NMR spectrum which represents the average of the different states. Conversely, when the interconversion process is slow, two separate signals will be observed; however, they will be weighted according to the population of the species present, and so may also give rise to essentially a single sharp signal in cases where one conformer is dominant. At intermediate exchange rates, the signals become broadened and thus lower in intensity. We considered it likely that the sharpening of the signals in the NMR titration data corresponded to a shift from intermediate to slow exchange upon binding the ligand. A relaxation dispersion experiment was used to confirm that the changes in line shape were associated with a chemical exchange process that disappeared upon addition of the ligand [16]. Furthermore, the exchange phenomenon was most pronounced for residues at the interface between the protomer subunits.

So what is the nature of the chemical exchange phenomenon? Most X-ray and NMR spectroscopy structures of VTB show the pentamer to have a symmetrical doughnut shape [4, 17]; however, the original crystal structure of the protein displayed a helical shape, reminiscent of a lock washer, in which two adjacent protomers were displaced by two amino acid residues along a β-sheet interface (Figure 4) [18]. Interconversion of the lock washer and symmetrical conformers of the protein would likely give rise to exchange broadening in the initial NMR spectra, but if the divalent ligand bound selectively to only the symmetric conformer, then the exchange process would be lost upon formation of the complex.

Figure 4. (a) “Lockwasher” and (b) “doughnut” conformations of VTB (from Protein databank files 1BOV.pdb and 1BOS.pdb, respectively). (c) Binding model for a divalent ligand binding to a pentavalent protein: K₁ is for binding to an isolated site, K₂ for binding to a site that is already occupied and K₃ for binding between two occupied sites; however, K₁ followed by K₃ is equivalent to K₂ followed by K₂ and so only two association constants are required to describe the system.

This hypothesis has thermodynamic consequences as loss of protein dynamics will incur an entropic penalty. However, as the pentamer will become increasingly rigid as the VTB binding sites fill up, then the system will pay a lower entropic penalty when the final ligand binds than it did for the first ligand. In other words, the system should display positive cooperativity. In order to test the hypothesis, we implemented a more sophisticated model for fitting the ITC data, in which all possible complexes (1:1, 1:2, 1:3, etc.) were considered explicitly (Figure 4) [16]. By making an assumption that any cooperative effects would only arise though interactions between nearest neighbour protomer subunits, it was possible to simplify the model to two stepwise association constants with corresponding enthalpy changes. K₁ represents the first ligand to bind to a pentamer, while K₂ corresponds to the average stepwise association constant once all binding sites have been filled. The ITC fitting results confirmed that the system displayed weak positive cooperativity and that the entropy change became more favourable (actually less unfavourable) as the titration proceeded (Table 1). However, the improvement in the entropic term was largely offset by a substantial decrease in the favourable enthalpy term as the pentamer became rigidified. This observation indicates that flexibility in the pentamer is entropically favourable, but the symmetric conformer is enthalpically favoured.

Table 1. Thermodynamic parameters for divalent Gb3 binding to VTB using the model outlined in Figure 4.

In summary, NMR spectroscopy indicated that the divalent ligand could bind to the protein as originally designed; however, it was only with a combination of NMR, X-ray crystallography and ITC that the importance and mechanism of protein dynamics for attenuating the divalent binding was revealed.

Multivalent Binding Influenced by Ligand Dynamics

Similar pentameric and decameric inhibitors have been reported by Fan and Hol [19, 20] for the B-pentamers of cholera toxin (CTB) and the closely related E. coli heat-labile toxin (LTB) which is 80% identical to CTB [1]. While these valency-matched inhibitors form the expected 1:1 and 1:2 complexes with their protein targets, we have found that a series of multivalent inhibitors with mismatched valencies prepared by Pieters, Visser and Zuilhof [21], inhibit CTB/LTB adhesion by aggregative mechanisms [22].

Figure 5. Chemical structures of monovalent and multivalent GM1-based inhibitors.

Compounds comprising two or four GM1 oligosaccharides attached to flexible scaffolds (GM1–2 and GM1–4, respectively; (Figure 5) were found to be up to 20,000 times more potent as inhibitors of CTB or LTB adhesion than monovalent ligand GM1–1 when compared in an ELISA assay [21, 22]. However, we found only marginal affinity enhancements when each ligand was studied by ITC. It was as if each GM1 group was behaving independently of one another in the ITC experiment, even though the divalent and tetravalent ligands were much more potent in the inhibition assay. Upon closer inspection of the solutions recovered from the ITC, it was noticed that the samples had become hazy during the titration. Therefore the mixtures were studied further by analytical ultracentrifugation (AUC) and dynamic light scattering (DLS), which are all techniques better suited for the study of aggregation phenomena.

In a sedimentation velocity AUC experiment, the protein solution is spun rapidly in a specialised centrifuge that has an optical system to record the UV-absorbance as a function of distance across the sample cell [23]. As the UV absorbance at 280 nm corresponds to the protein concentration, it is possible to follow the process of protein sedimentation in real time while the sample is spinning in the centrifuge. Application of the Svedberg and Lamm equations to the time-dependent data in the SEDFIT program provides a simple depiction of the relative concentrations of different sized species present in the mixture [24]. The LTB pentamer gave rise to a single peak in the c(S) plot (Figure 6a) that corresponds to a particle of around 60 kDa as expected [22]. Addition of enough monovalent ligand GM1–1 to fill all of the binding sites did not change the AUC profile. However, when sufficient divalent ligand GM1–2 to fill only 10% or 20% of the binding sites was added, a second species corresponding to a dimer of LTB-pentamers started to accumulate. If the GM1–2 concentration was increased to 0.5 GM1 equivalents per binding site, then the signal decreased dramatically; by this point in the titration, most of the protein had become incorporated into aggregates of sufficient size that they were rapidly sedimented to the edge of the cell, beyond the observable window in the sample cell. Curiously, if the GM1–2 concentration was high enough to fill all of the LTB binding sites, then essentially all of the protein that remained in solution was in the form of dimers of pentamers. The AUC signal also decreased as the concentration of tetravalent ligand GM1–4 increased (Figure 6b); however, in this case, no dimers of pentamers were observed at any ligand concentration.

Figure 6. Sedimentation velocity analytical ultracentrifugation experiments of LTB pentamers in the presence of varying concentrations of GM1–1, GM1–2 and GM1–4.

While AUC was well suited for studying the discrete species in solution, it gave little information on the formation of the larger aggregates. In contrast, dynamic light scattering (DLS) is usually dominated by the larger species present, and it can also provide more useful information on the rate of aggregation. When DLS was used to study LTB in the presence of GM1–2 and GM1–4 (0.1 equivalents of GM1 groups per LTB binding site), aggregates started to form within 10 minutes [22]. The GM1–2 based aggregates formed more rapidly and were around 10 times larger than those derived from GM1–4. Therefore, both AUC and DLS demonstrated that the divalent and tetravalent ligands showed different solution properties. When one considers that there is very little difference in the distances separating the oligosaccharide groups in these two ligands, the different aggregation properties must be dependent on the valencies of the two compounds.

Why does the divalent ligand give rise to dimers of pentamers, but the tetravalent ligand does not?

It was surprising that dimers of pentamers formed at low ligand:protein ratios as the ligands are sufficiently long to crosslink adjacent binding sites in the LTB pentamer. Presumably, intrapentamer binding results in rigidification of the flexible linker. If the resulting loss of conformational entropy is greater than the entropic penalty for forming a ternary complex, then the dimer of pentamers will predominate (Figure 7a). Although the tetravalent ligand could also form dimers of pentamers, in order to maximise the binding interactions they would typically involve chelating interactions, which will pay penalties for loss of conformational entropy. Alternatively, if GM1–4 were to crosslink two LTB pentamers using only two of its GM1 oligosaccharides, then the other two would be preorganised to interact with other pentamers and thus lead to larger aggregates (Figure 7b). As the aggregation kinetics were slower for GM1–4, it is likely that the additional interactions in the higher valency system can overcome the conformational entropy penalty to some extent and thus the 1:1 complexes may predominate at lower ratios of ligand:protein.

Figure 7. Dimerisation and aggregation of bacterial toxins by divalent and tetravalent ligands.

While ITC provided valuable information in the VTB-Gb3 system [16], here it only highlighted a discrepancy between the inhibitor potency in the ELISA assay and trends in binding affinity [22]. However, AUC and DLS provided complementary evidence for aggregation mechanisms and revealed for the first time that the nature of these aggregation processes can be valency-dependent.

Conclusions

Multivalent inhibitors may operate by a variety of mechanisms to enhance their potency relative to the analogous monovalent carbohydrate ligands. While individual biochemical or biophysical techniques may be sufficient to measure potency, unravelling the underlying inhibition mechanisms may require a combination of techniques. Often designs for multivalent ligands are based on static models derived from X-ray crystal structures, but dynamics of both the ligands and their multivalent protein targets can have a substantial impact on the potency of the compounds.

Acknowledgements

The author thanks The Royal Society, EPSRC, the Wellcome Trust, AstraZeneca, BBSRC and ERC COST Actions D34/0001/05 and CM1102 for financial support, and his co-authors on references [16] and [22] for assistance in preparing figures. WBT holds a Royal Society University Research Fellowship.

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Predicting the Biological Repeating Unit of Bacterial Lipopolysaccharides: Common Structural Motifs in Regular Polysaccharides from Different Genera

Roland Stenutz

IsoSep AB, Dalkärrsvägen 11, SE-146 36 Tullinge, Sweden

E-Mail: roland.stenutz@isosep.com

Received: 11th October 2011/Published: 11th July 2012

Abstract

The cell surface of most organisms is covered by carbohydrates linked to lipids or proteins. In gram-negative bacteria there are generally two kinds of carbohydrate molecules covering the cell surface; lipopolysaccharides (or occasionally lipooligosaccharides) and capsular polysaccharides. Whilst the capsular polysaccharides, when present, are very long and loosely attached to the cell surface the lipopolysaccharides are anchored in the cell membrane and protrude perpendicular to it. Thus, in absence of a capsule, the surface presented to other microbes and the immune system is dominated by the ends of the lipopolysaccharide molecules. Here we show that structures presented to the environment are shared among a wide range of different bacteria and can often be predicted from the chemical structure of the polymer.

Introduction

The importance of lipopolysaccharides (LPS) for the survival of bacteria in their natural surrounding has long been recognised. There is however no consensus regarding their function in vivo and presumably they have several roles such as protection against bacteriophages, surface adhesion and resistance towards a variety of other environmental stresses [1]. Lipopolysaccharides are some of the most variable molecules produced by bacteria. The number of components in bacterial polysaccharides is staggering; a 1990 review counts 85 different glycosyl residues (excluding substituents) [2], which leads to an enormous number of possible structures. Despite this the diversity identical or similar structures are encountered more often than would be expected. Even when the limitations imposed by biosynthesis are taken into account there are structural themes that are more common and wide spread than might be anticipated. In the case of E. coli approximately 15% of the known O-antigen structures are identical to structures from other genera and common fragments causing serological cross reactivity are relatively abundant [3].

The Biological Repeating Unit

Lipopolysaccharides can be divided roughly into three parts; lipid A (endotoxin) – the part that attaches the LPS to the membrane, the O-antigen that consists of 10–20 repeating units and the core – a highly conserved oligosaccharide that connects the two (Figure 1). In order to make sense of the available immunological and structural data it is advantageous to align the repeating units of the O-antigen. The obvious way to do this is by aligning the biological repeating units (BRU). Not only does this allow internal epitopes to be recognized but the terminal residues, which are responsible for strong cross-reactivities between different serogroups, are also identified.

Figure 1. Schematic of a lipopolysaccharide

Unfortunately, there is little experimental data on the BRUs. It is however known that biosyntheses of the BRU commences with the transfer of a glycosyl phosphate to undecaprenylphosphate [1] (the synthesis of other polysaccharides, such as teichoic acids [4] and capsular polysaccharides [5], also starts with the same carrier). The glycosyl residue is most often d-GlcNAc but other 2-acetamidodeoxy sugars and d-galactose [6] are occasionally used. While preparing a compilation of E. coli O-antigen structures we made several other observations that allowed an increasingly detailed set of rules to be derived. It should be noted, however, that most of these rules have exceptions and they are applicable only to polysaccharides that are made by the polymerisation of pre-formed oligosaccharide blocks (Wzy-dependent pathway) and not to those made by the ABC transporter-dependent pathway. In E. coli, both O-antigens, with few exceptions [3], as well as type 1 (former IA) and 4 (former IB) capsular polysaccharides (CPS, K-antigens) [7], enterobacterial common antigen (ECA) [8], and colanic acid (CA) [9] (Scheme 1) are formed by this pathway.

Colanic acid (CA, M-antigen) [19]

Enterobacterial common antigen (ECA)[18]

Scheme 1. Colanic acid and the enterobacteria common antigen – two cell surface polysaccharides common to most E. coli strains

Synthesis of Polysaccharides by the Wzy-dependent Pathway

In the Wzy-dependent pathway the repeating unit is built by the sequential transfer of glycosyl residues from sugar nucleotides to an undecaprenyl pyrophosphate linked sugar residue (Figure 2). In E. coli, as well as in many other bacteria, this first residue is linked to the lipid by WecA (undecaprenyl-phosphate α-N-acetylglucosaminyl 1-phosphatetransferase) in the Enterobacterial Common Antigen (ECA) synthesis and is a d-GlcNAc or d-GalNAc residue [1].

Figure 2. Formation of the biological repeating unit on the cytosolic side of the inner membrane

Salmonella on the other hand often uses d-Gal derived from the M-antigen synthesis as the first residue. Thus, only certain sugar residues are allowed in the first position of the repeating unit. Additional glycosyl residues are attached in a sequential manner. The first being added to the 3 position (rarely the 4 position) and the completed lipid bound oligosaccharide “flipped” to the periplasmic side of the membrane. This oligosaccharide is the biological repeating unit of the O-antigen. The protein responsible for the translocation is called “flippase” (Wzx). On the periplasmic side the oligosaccharides are joined by a polymerase (Wzy) causing the polysaccharide to become elongated from the “reducing” (lipid bound) end – hence the name of the pathway (Figure 3). Finally the O-chain is transferred to the lipid-A bound core by a ligase (WaaL) and exported to the outer surface of the outer membrane. Occasionally the oligosaccharide is modified in the periplasm by glycosidases and acetylases encoded by temperate phages [10]. Phages may also disrupt the normal polymerisation and change the linkage between BRU:s [11].

Figure 3. Polymerisation and decoration of O-antigen on the periplasmic side of the inner membrane

Note that the initial transfer of the glycosyl phosphate to the undecaprenylphosphate as well as the final transfer of the lipid bound polymer to the core oligosaccharide is achieved by enzymes encoded outside the O-antigen gene cluster, as are the glycosyltransferases and acetyltransferases that effect the modification of the polymer in the periplasmic space. It has however been reported that a branch is added by a glycosyltransferase in the O-antigen gene cluster [12]. The O-antigen gene cluster, which is most often, but not always, located on the chromosome contains all the genes necessary to make the needed nucleotide sugars (except UDP-Glc, UDP-Gal, and UDP-GlcNAc which are present in sufficient quantity anyway [13]), the glycosyltransferases for the formation of the linkages, and the proteins required for translocation (Wzx), polymerisation (Wzy) and for controlling the polymer length (Wzz). An elegant illustration of the biosynthesis is provided by an attempt to express an exopolysaccharide of Streptococcus thermophilus in Lactococcus lacti [14]. The polymer produced after transfer of the gene cluster had the proximal d-GalNAc residue replaced by d-Gal and the branch was absent (Scheme 2). Both the proximal residue, defined by the transfer of the first residue to the undecaprenyl phosphate carrier, and the decorating d-Gal residue are expected to be the result of enzymes encoded in other regions of the chromosome.

Streptococcus thermophilus Sfi6 (original)

Lactococcus lacti MG1363

Scheme 2. Transferred O-antigen gene cluster
The proximal d-GalNAc is replaced by d-Gal and the branching residue is lost. [14]

Alignment of E. coli O-antigens

At first we simply aligned the sequences with d-GlcNAc at the right most position. It soon became apparent that d-GalNAc could replace d-GlcNAc and that extension at the 3-position was highly preferred. Also, branches at other positions than that joining the next BRU were often d-Glc or d-Gal residues – a situation similar to that in Shigella flexneri. In S. flexneri these additional glycosyl residues are added on the periplasmic side of the inner membrane by glycosyl transferases encoded by temperate phages [10]. Since it is rather uncommon to find O-antigens with more than one d-GlcNAc/d-GalNAc residue this alone was sufficient to assign the BRU in >60% of the structures. The results of the alignments have been published [3].

Rules derived from E. coli O-antigens

Using our knowledge of the E. coli O-antigens it became possible to derive a set of rules to guide the assignment of BRUs in cases when there was more than one possible “initiating” residue. The rules also allow polysaccharides containing amino sugars but synthesised by the ABC-transporter pathway to be recognised as such (Figure 4).

Figure 4. Common structural features in the first lipid bound residues.

Rule 1– The first residue in the BRU is a 3-linked β-d-GlcNAc

On examining the structures of all the known E. coli O-antigen structures it was observed that the by far most common residue at the first position in the BRU is d-GlcNAc (72%). Occasionally this residue is α-linked or a d-GalNAc residue (25%) and in a few serogroups the linkage is to position 4 in d-GlcNAc. There are two structures (3%) where d-FucNAc replaces d-GlcNAc. It has been claimed that the occasional use of d-GalNAc instead of d-GlcNAc is due to a lack of selectivity in WecA, the enzyme that transfers the first residue to the lipid carrier [15]. Recent results however suggest that the lipid bound d-GlcNAc might be transformed by an epimerase [16]. This would allow the WecA enzyme to be selective, yet still provide a mechanism by which other lipid linked aminosugars may occupy the first position in the repeating unit. It is possible that both alternatives might be operative in different serogroups.

Rule 2– The BRU is four or five residues long

The length of the repeating units does not vary greatly. The most common lengths for E. coli O-antigens are four (58%) and five (29%) and this appears to be common in other bacterial polysaccharides, too. Exopolysaccharides, e.g. those formed by lactic acid bacteria, tend to have longer repeating units [17]. It should also be noted that the enterobacterial common antigen (three residues [18]) and colanic acid (six residues [19]) fall outside this range. Many carbohydrate epitopes, e.g. the blood group antigens, are three to four residues in size. Thus, a repeating unit consisting of four to five residues with one conserved residue and three to four variable residues may be optimal for interaction with proteins.

Rule 3– Branches consisting of d-Glc, d-Gal or d-GlcNAc residues are added after polymerisation

Many O-antigen structures are branched. One way in which branches are formed is in the polymerisation of the repeating units. These branches are easily recognised since they can only occur at residue that is linked to the first residue on the next BRU, e.g. d-GlcNAc or d-GalNAc. There are several repeating units where there is more than one branch point, or where the branch point is located on a residue not linked to the next BRU. Most of these branches are d-Glc (50%), d-Gal (17%) or d-GlcNAc (20%) that may be added on the periplasmic side of the inner membrane by glycosyl transferases encoded by temperate phages. The genes coding for the transfer of these sugars to a lipid carrier, their translocation to the periplasmic side of the inner membrane and transfer to the growing polymer are located outside the locus for the main O-antigen genes [10]. It seems that this mechanism is restricted to nucleotide sugars readily available from housekeeping. The inactivation of these genes does not hinder LPS formation and has been suggested as a mechanism by which Shigella spp. may undergo phase variation [20]. The heterogeneity frequently claimed to exist in LPS seems to be linked to these residues or other substituents added after polymerisation of the oligosaccharide blocks. There is evidence that in some cases this ‘heterogeneity’ is not caused by non-stoichiometric substitution but by the presence of different polysaccharide chains of well-defined structure which occasionally can be separated [21, 22].

Rule 4– Rare glycosyl residues are likely to occur as the last residue in the BRU

E. coli O-antigenes are made up of 25 different glycosyl residues, some more common than others. The common residues d-Glc, d-GlcNAc, d-Gal, d-GalNAc, d-Man and l-Rha each constitute more than 10% of all residues. Together they account for 77%. The remaining 19 residues make up the remaining 23%. Often these rare glycosyl residues are at the end of the BRU. This makes them particularly exposed. At the last position of the BRU they account for approximately 50%, i.e. they are twice as frequent as would be expected. Since the last residue in the BRU is also expected to be the most exposed one it is not surprising that it should also show the greatest variation.

Rule 5– O-antigens with repeating units violating the above rules are derived from the ABC-transporter dependent pathway

There are two other known pathways for O-antigen synthesis; the ABC-transporter dependent pathway and the synthase dependent pathway. The latter has so far only been encountered in Salmonella borreze [23]. In the ABC-transporter dependent pathway the polymer is formed on the cytosolic side of the inner membrane by addition of one glycosyl residue at a time. The completed polysaccharide is then translocated by an ABC-transporter. Often the number of different residues is small and the backbone short. Slightly less than 10% of the E. coli O-antigens are made by this pathway [3].

Correlation of Structure with Pathotype

Since there is a correlation between serotype and pathotype it is obvious to seek similarities in the corresponding O-antigen structures. Serogroups containing pathogenic strains often harbour strains belonging to more than one pathotype and may contain commensal strains as well. Mimicry of host glycans has been proposed to be related to virulence in several genera of bacteria but in E. coli mimicry seems to be marginal or even coincidental. The impression is strengthened when the rather low similarity of the antigens to mammalian glycans is contrasted with the elaborate mimicry and phase variation encountered in the lipooligosaccharides of Helicobacter pylori and Campylobacter jejuni [24]. However spontaneous serotype conversion has been observed in Shigella and may be related to the persistence of infection [25].

Mimicry of blood group antigens seems to occur in some serogroups of the EHEC (enterohaemorrhagic E. coli) pathotype. The blood group mimicked is H (B in O86) which is the most common blood group globally but also the simplest structure. When examining a larger set of O-antigens related to EHEC one finds that α-6-deoxy-l-hexoses (α-Col, α-l-Fuc, α-l-Rha) are located at the terminal position in most structures (65%) and that amino sugars are quite common (77% contain more than one). The lack of fidelity, only the outermost part of the BRU is involved, makes it unlikely that any lasting protection from the immune system is achieved but this does not rule out a role in morbidity, e.g. by induction of autoimmune responses against similar antigens. Bacterial infections are known to play a role in autoimmune disorders such as Guillain-Barré syndrome (GBS) or Miller Fisher syndrome (MFS) [26]. Of the approximately 70 know O-antigen structures of E. coli only five display terminal blood group antigens and two more contain terminal α-Neu5Ac.

LPS is often claimed to be a virulence factor, but this is often based on the avirulence of strains deficient in LPS – a condition known to make the bacteria sensitive to serum and impair the function of the outer membrane. It has been proposed that glycosylation increases the virulence of Shigella by changing the conformation of the polysaccharide making the LPS layer more compact and thereby better exposing virulence factors on the cell surface [27] but convincing proof that the actual structure of the O-antigen plays a role in virulence has yet to be presented. Recent results on the correlation between serogroup and the presence of toxins and adhesins are also starting to cast doubt on the correlation between pathology and serology. Thus in one study only 23% of the “pathogenic” E. coli strains identified by serology did also have the genes necessary to produce toxins although in some serogroups the correlation was much better (e.g. 70–80% in O111 and O119) [28]. The role of LPS structure in disease is further obscured by the presence of O-antigen gene clusters on plasmids that also carry virulence factors such as adhesins and toxins.

Serological Cross Reactivity and Conserved Structural Motifs

There is extensive cross-reactivity between different strains of E. coli but also between E. coli and other genera. In many cases the O-antigens are identical or near identical. The similarity between these polysaccharides becomes apparent only when the terminal residues of the biological repeating unit are considered. If the branches, assumed to be added by phage encoded transferases, are removed and the first residue of the BRU is disregarded, one finds that there are some motifs that are shared between many more serotypes and genera.

In the case of E. coli O123/S. enterica O:58 [29] and E. coli O17, O44, O73, O77 and O106/S. enterica O:6,14 [30] analysis of the O-antigen gene clusters suggest that they originate from a common ancestor (14×10⁷ yrs ago). In the case of S. sonnei the O-antigen cluster located on the plasmid seems to be acquired fairly recently (8–10×10³ yrs ago) from P. shigelloides O17 [31]. The original chromosomal O-antigen cluster (E. coli O7 or O62[32]) was subsequently lost. Thus there seem to be both ancient structures as well as some of recent origin. Much of the diversity of the O-antigens seems to be related to phage induced changes on highly conserved structures. Three structural motifs that are wide spread are the Man₃-motif of Salmonella thompson (Scheme 3), Rha₃-motif of Shigella flexneri Y (Scheme 4), and the Col-Gal-GlcNAc-motif of Escherichia O55 (Scheme 5). No doubt many more such “clusters” of similar structures are still waiting to be discovered.

Salmonella cerro [36], Citrobacter freundii O23 [37]

Salmonella thompson [38]

Yersinia pseudotuberculosis O:2c [39]

Enterobacter cloacae O10 [40]

Escherichia coli O17 [41]

Scheme 3. O-antigens containing a α-d-Man-(1→2)-α-d-Man-(1→2)-β-d-Man motif.

Shigella flexneri Y [42]

Pseudomonas solanacearum ICMP 8072 [43]

Serratia marcescens O10 [44]

Klebsiella K45 [45]

Acinetobacter baumannii O10 [46]

Salmonella arizonae O62 [47]

Scheme 4. Bacterial polysaccharides containing a α-l-Rha-(1→2)-α-l-Rha -(1→3)- α-l-Rha motif

Whilst studying a wider range of repeating unit polysaccharides it also became apparent that several other amino sugars could replace d-GlcNAc as the first residue of the BRU. In E. coli we found d-GalNAc as well as d-FucNAc but in other genera d-QuiNAc, d-FucNAc4NAc and d-QuiNAc4NAc (Bacillosamine) can take its place. Thus the common requirement seems to be a d-hexosamine with equatorial substituents at position 2 and 3. In CPS and EPS d-GlcNAc is generally replaced by d-Gal or d-Glc. Extension proceeds at an equatorial position, hence a few cases of 4-linked d-GlcNAc are encountered but all other residues link through position 3.

Escherichia coli O55 [48]

Salmonella arizonae O50 [49]

Vibrio cholerae O139 [50]

Aeromonas trotta [51]

Pseudoalteromonas tetraodonis IAM 14160T [52]

Scheme 5. O-antigens containing a α-Col-(1→2)-β-d-Gal-(1→3)-β-d-GlcNAc motif

Summary

The structures of the repeating units of E. coli lipopolysaccharides where aligned to give the best agreement with known biosynthetic pathways of the biological repeating units. During this process additional rules could be derived that allowed extension to other genera and types of polysaccharides made by the polymerase dependent pathway. The comparison of the BRUs of polysaccharides from different genera of bacteria showed that common motifs are fairly widespread. Although O-antigens are considered to be very variable genetic studies show that some BRUs originate from common ancestors several million years ago. Mimicry of host glucans is often claimed for polysaccharides but mimicry is rare and many backbones appear to be older that the hosts that bacteria inhabit. It would appear that there are other evolutionary forces at work, perhaps microbe-microbe rather than microbe-host interactions.

Although there are many reports of heterogeneity in lipopolysaccharides the fidelity of O-antigen synthesis seems to be high and under strict temporal and/or spatial control. Despite the fact that several enzymes in the pathway seem to be quite promiscuous the resulting polymers are often well defined.

The alignments allow easy identification of common epitopes and can be used to determine the biosynthetic pathway for a given structure. In some cases it is also possible to determine the reliability of published structures. As more structures are compared it will hopefully be possible to rationalize more of the polysaccharide structures and perhaps derive additional rules. Amongst other, it was observed that some residues show a high preference for certain positions in the BRU, e.g. some residues are always found in terminal positions whereas others never are.

Whilst exploring carbohydrate structures extensive use was made of glycomics databases; CCSD (accessed through the interface at http://www.boc.chem.uu.nl/static/sugabase/carbbank.html) [33], GLYCOSCIENCES.de (http://glycosciences.de) [34], and BCSDB (http://www.glyco.ac.ru/bcsdb3/) [35]. Better integration, not only of available structure and genetic data is needed but also a much more multidisciplinary approach to glycoscience in general. Much remains to be discovered, in particular with regard to the genetic data that is starting to appear. In a not too distant future one can envision the both the possibility to tailor bacterial polysaccharides for industrial or medical applications as well as a better understanding of the biological role of lipopolysaccharides.

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Bioinformatics Analysis of the Glycome Guides Automated Oligosaccharide Synthesis

Daniel Kolarich¹ and Peter H. Seeberger¹,2,3,*

¹Max-Planck Institute for Colloids and Interfaces, Am Mühlenberg 1,
14476 Potsdam, Germany,

²Free University of Berlin, Arnimallee 22 14195 Berlin, Germany and

³The Burnham Institute, La Jolla, CA, U.S.A.

E-Mail: *peter.seeberger@mpikg.mpg.de

Received: 6th January 2012/Published: 11th July 2012

Deciphering the Glycocode

MIT Technology Review considers glycomics to be one of the ten technologies with the capacity to change the world [1]. Every class of major biomolecule identified so far either has a carbohydrate as a major constituent (e.g. 2-deoxyribose in DNA) or occurs also in a glycosylated form. Glycoproteins and glycolipids are key molecules involved in cell-cell interaction or cell-signalling, proteoglycans and glucosaminoglycans are crucial components of the animal extracellular matrix whereas certain carbohydrate polymers are important energy storage molecules [2, 3]. With the exception of DNA, the biosynthesis of the carbohydrate portion of biomolecules is not a template driven process but the result from concerted actions of numerous glycosyltransferases and glycosyl-donor synthesizing enzymes [2]. These biosynthetic pathways provide the cell with the possibility to fine-tune particular features of glycosylated biomolecules without modifying the actual activity. The capacity to completely reverse IgG function has been shown for minor modifications in the IgG N-glycan structure. The addition of a single sialic acid molecule is able to convert IgG from being a pro-inflammatory into an anti-inflammatory agent [4] and the addition of a core fucose residue the very same N-glycan can inhibit initiation of antibody-dependent cell cytotoxicity by obstructing its binding to the FcγRIIIa receptor [5] without actually changing the actual binding properties towards its antigen.

In contrast to protein sequences, glycan structures cannot be predicted from a template, which makes robust and solid methods for determination of the glycome a central necessity. To date, no methods are available that allow capturing the entire glycome. Due to the vast diversity of different glycosylated molecules, the lack of adequate bioinformatic tools and available satisfactory data repositories, as they are known for genomics and proteomics, it remains questionable whether this goal can be achieved in the near term. Thus, current glycomics approaches naturally focus on a particular class of glycosylated molecules like protein-bound glycans, glycolipids or proteoglycans, to name a few. Due to their diverse nature, different approaches are required in order to enable their analysis, characterisation and sequencing. Nevertheless, many of the glycosylated biomolecules are just present in relative low amounts, and targeted purification for studying their biological role is often impossible. A comprehensive automated synthesis allows access to larger quantities of defined glycans of biological relevance [6, 7]. This capacity provides central means required in deciphering the glyco-code of glycan sequences.

Decomplexing the Glycome for Automated Synthesis

The biosynthetic nature of the glycome provides the cell with the capacity to store and transmit exponentially more information in glycan sequences compared to the genome or proteome (Table 1). Compared to the linear structure of DNA or protein sequences, glycans can occur in branched structures. Furthermore the different monosaccharide building blocks can be linked together in different ways, resulting in substantially different biomolecules of similar sequence but different biological properties. This is best illustrated by comparing starch and cellulose: both polymers consist of 1–4 linked glucose monosaccharide building blocks, but differ in the stereochemistry of the linkage: α-1,4 linkages dominate in starch whereas β-1,4 linkages are the major type of connection in cellulose, resulting in two very different types of biopolymers. Similarly, comparably small differences in glycosylation and linkage can have significant impact on the biological activity of mammalian biomolecules as pointed out above.

Despite the immense variety of theoretically possibly conformations a hexasaccharide could have (Table 1), a substantially smaller number of different types of linkages are found in mammalian oligosaccharides [8]. Using the database information stored in GLYCOSCEINCES.de database (www.glycosciences.de/) the structural diversity of mammalian carbohydrates was explored and the most common monosaccharide building blocks found in mammalian carbohydrates were identified [8]. About three quarters of structures found in the database show some type of branching at least once. Three monosaccharide building blocks, namely glucosamine, galactose, and mannose contribute to about 75% of all building blocks found, and the major terminating residues comprise alpha-linked sialic acid, alpha-linked fucose, and beta-linked galactose. Interestingly, glucose, which is from the quantitative point of view the most abundant monosaccharide found in mammals, just plays a very minor role in glycoconjugate glycan structures. This data indicates that there is a clear division in storage and glycoconjugate monosaccharides.

Table 1. Number of theoretically possibly structures of biopolymers (Reprinted with permission from Werz et al [8]. Copyright 2007 American Chemical Society”).

Oligomer size

Numbers of different oligomers

Nucleotides

Peptides

Carbohydrates

1

4

20

20

2

16

400

1360

3

64

8000

126080

4

256

160000

13495040

5

1024

3200000

1569745920

6

4096

64000000

192780943360

In the context of automated synthesis the most important result is, however, the finding that a manageable number of just 36 building blocks can be used to construct about 75% of the structures available in the database (Figure 1) [8]. This encouraging data demonstrates that automated synthesis has the capacity to produce significant amounts highly defined glycan structures that enable glycobiologists to decipher the messages hidden within mammalian glycan structures.

Building Blocks vs. Glycospace

Figure 1. Number of building blocks required for synthetic access to mammalian carbohydrates. Percentage of accessible mammalian carbohydrates correlated to the number of building blocks in the context of the different classes analysed (glycolipids and N- and O-linked glycans) and correlated to the number of building blocks. (Reprinted with permission from Werz et al. [8]. Copyright 2007 American Chemical Society”).

Similar encouraging results were obtained using a bioinformatics assessment of monosaccharide building blocks present in bacterial sugars [9]. Data on bacterial sugars deposited in Bacterial Carbohydrate Structure Database (BCSDB) (csdb.glycoscience.ru/bacterial/) and GLYCOSCIENCES.de (GS) has been statistically evaluated focusing on pathogenic bacteria with regard to five major parameters:

monosaccharide units abundance,

disaccharide pairs,

carbohydrate modifications,

presence and use of sialic acids and

class-specific monosaccharides.

Not surprisingly, bacteria showed to be more diverse in the monosaccharide building blocks, and significant differences in the use of these has been found for different classes of bacterial species [9]. Nevertheless an infinite number of about 25 monosaccharides allows to build up about 71% of the bacterial glycome known so far.

Glyco-bioinformatics – Quo vadis?

The findings of these two straightforward studies point out the fundamental necessity for adequate and openly accessible data repositories. Glyco-bioinformatics has been characterized by the existence of multiple disconnected and incompatible islands of experimental data, data resources and specific applications, managed by various consortia, institutions or local groups. These resources rarely provided the necessary communication mechanisms that would allow for the efficient combination and comparison of these data. However, combinations and comparison of data is a crucial point as it enables researches to perform broader bioinformatic studies in a Systems Biology context on glycoconjugates.

Databases clearly need data, but what type of data needs to be stored? Should quantity be more important than quality of the data? Clearly a highly accurate database consisting of a very limited number of entries will hardly be beneficial, but likewise will a large database with inadequately curated data be of little value. Another issue to be considered is specific for glycoconjugates. In contrast to DNA, which can be easily categorised into different species, this is more challenging for glycoconjugates since similar glycans can occur across different species. User and data friendly solutions have to be developed to address these and many additional challenges that are peculiar for glycoconjugates.

A promising new development in this context is UNICARB-DB (www.unicarb-db.org) [10] and for more detailed information the contribution from Packer et al. in this volume of the proceedings should be consulted [11]. Equally important in this context is also the information on how the data in glycomics experiments is obtained. Particular reporting guidelines are in place in the related fields of genomics and proteomics, [12, 13], and recently a similar initiative called MIRAGE (Minimum Information Required for A Glycomics Experiment, initiated by Prof. Will York) has formed out of this 2nd Beilstein Symposium on Glyco-bioinformatics. For more information on that the contribution from Prof. York in this proceedings issue is referred to [14].

Conclusions

Glycomics research urgently requires robust, reliable and accessible glyco-bioinformatics solutions that can be utilised by researches from the many diverse fields closely related to the synthesis, analysis and biological investigation of glycoconjugates. The developments in robust and user-friendly databases, the constant deposition of newly acquired data and novel bioinformatic tools will boost subsequent research in glycoconjugate systems biology. These developments will provide a solid basis that will allow researchers to understand the diverse interaction networks glycoconjugates are involved in. For that to happen, the joint efforts initiated in recent years and also within the course of the 2nd Beilstein Symposium on Glyco-bioinformatics need to be continued.

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Glycoarrays on Gold Surfaces

Peter Both, Robert Šardzík, Martin Weissenborn, Anthony Green,
Josef Voglmeir and Sabine Flitsch*

School of Chemistry & Manchester Interdisciplinary Biocentre,
The University of Manchester, 131 Princess Street, Manchester M1 7ND, U.K.

E-Mail: *sabine.flitsch@manchester.ac.uk

Received: 16th December 2011/Published: 11th July 2012

Abstract

Self-assembled monolayers (SAMs) on gold have become widely used as a platform for studying chemical and biochemical reactions, for studying biomolecular interactions and for the development of nanoscale devices. We have used this platform to study the solid-supported synthesis of carbohydrates and glycopeptides using both chemical and enzymatic methods. An attractive feature of the technology is the opportunity for miniaturisation and in situ analysis using mass spectrometry, SPR and fluorescence spectroscopy. Applications for the synthesis of complex oligosaccharides and glycopeptides to generate glycoarrays and their application in biology and medicine are discussed.

Introduction

The sequences of oligo- and polysaccharides in cells and tissues are not directly encoded in their genomes, but are determined by the expression and substrate specificity of a large set of ‘glycoenzymes', which catalyse the formation or hydrolysis of glycosidic bonds (Figure 1). These enzymes are involved in the biosynthesis of glycan structures by controlling regio- and stereoselectivity of glycosylation, a process which is highly dynamic. The understanding of the activity and substrate specificity of these glycoenzymes is a key to determining and understanding the ‘glycome', the set of carbohydrate structures in a biological system.

Figure 1. The Glycome presents the set of carbohydrate sequences in a cell or organism. The sequences are determined by glycoenzymes such as glycosyltransferases and glycosidases which control formation and hydrolysis of specific glycosidic linkages.

Glycoenzymes can often be identified in genomes for their characteristic conserved polypeptide sequence motifs and over 100,000 members are currently catalogued in the CAZy database (www.cazy.org/). Most of these glycoenzymes are highly specific for generating or cleaving a defined saccharide sequence, including selectivity for monosaccharide units as well as regio- and stereochemistry of linkage. This precise substrate specificity needs to be determined for each enzyme by biochemical studies, since computational methods are currently not able to make accurate predictions on substrate recognition. Thus, there is a major drive in the glycosciences to find biochemical methods that can allow us to go beyond sequence determination to obtain structural and functional information on glycoenzymes.

One of the emerging technologies in the glycosciences involves microarrays containing diverse carbohydrate probes (Glycan arrays) which have been used to study carbohydrate-protein interactions. Such microarrays have been generated from either natural or synthetic carbohydrate samples (Figure 2) and have been interrogated by fluorescently labelled protein for binding. Over the past five years, our laboratory and others have applied this glycan array technology to the study of glycoenzymes [1]

Figure 2. Carbohydrate arrays have become important tools for the investigation of protein/carbohydtae interactions in a high-throughput manner.

Methods and Results

Biochemical reactions are generally studied in solution, but for microarray studies, such reactions need to be transferred to solid phase. Although immobilised enzymes have been used frequently in biotechnological applications, the study of enzymatic modifications of immobilised substrates (Figure 3) has been less well explored [2]. Extensive fundamental studies of enzyme-catalysed reactions on diverse solid supports have shown that issues of substrate accessibility [3–13], reaction equilibria [14–17] and stereospecificity [18–21] need to be considered when choosing solid supports. Porous supports such as copolymers of polystyrene or polyacrylamide with polyethylene glycol are generally not suitable as supports for efficient enzyme catalysis because of poor yields. Such polymer supports also offer little opportunity for in situ molecular analysis of reactions.

Figure 3. Classically, enzymes have been in used solution (upper panel) or immobilised on solid phase (middle panel). Applications on microarrays require the substrate to be immobiled during the reaction (lower panel).

Given these considerations, a solid support consisting of self-assembled monolayers (SAMs) on gold (subsequently referred to as ‘gold platform') [22, 23] was chosen as a more suitable system to study glycoenzymes on arrays (Figure 4). Such arrays can easily be derivatized with structures containing an amine functionality such as peptides, glycopeptides or glycosyl ethanolamines. The SAM surfaces can be used with both organic solvents and aqueous buffers and have been optimised for biocompatibility [22]. Peptides can be coupled directly through their amino-termini or amino side chains, whereas glycosyl aminoethanols are easily prepared by short synthetic routes [23].

Figure 4. The ‘gold platform’ presented in this paper consists of self-assembled monolayers linked via thiols to gold surfaces.

A particular advantage of the gold platform is the possibility of in situ analysis of surfaces through MALDI-TOF mass spectrometry, a technology termed SAMDI [24, 25] (Figure 5). Thus, any chemical or enzymatic transformation can be monitored directly on the surface without the need for cleavage. The surface can also be re-used for further chemical or enzymatic reactions since only small fractions of the analyte are sampled by MALDI-TOF MS analysis. Figure 5 shows as an example the mass spectra taken from array spots before and after glycosylation using a β-1,4-galactosyltransferase. The glycan probe shows several peaks due to thiol dimerization to generate homo- and hetero-disulfides, but all peaks can be assigned unambiguously to either starting material or product. The MALDI-TOF mass spectrum of the product illustrates that the glycosylation can be high-yielding – in this case no starting material was observed in the spectrum, suggestion quantitative conversion. We have investigated a diverse range of glycosyltransferases on the gold platform such as GlucNAc-, GalNAc-, Neu5Ac- and Fuc-transferases (Figure 6, unpublished results) and have found that this platform provides accessible acceptor substrates to all of these enzymes [26–28]. A further advantage of the gold platform is compatibility with other spectroscopic methods, In particular surface plasmon resonance spectroscopy [29], which also needs attachment of ligands to conducting surfaces such as gold, in order to measure carbohydrate/protein interactions [29].

Figure 5. The enzymatic galactosylation of a surface-linked GlcNAc can be monitored using MALDI-TOF mass spectrometry with trihydroxy acetophenone as a matrix. The spectrum shows monomers, homo- and heterodimers of the functionalised alkyl thiols.

Figure 6. In collaboration with Prof Iain Wilson(BOKU, Austria), we have investigated a number of fucosyltransferases on the array platform. Varying degrees of glycosylation were observed with recombinant enzymes.

One of the most intriguing questions in protein glycosylations concerns the choice of glycosylation site on a polypeptide sequence. Whilst the site of N-glycosylation is highly conserved and predictable for tripeptide motifs (Asn-X-Thr/Ser), consensus motifs for O-glycosylation are poorly understood and need to be determined by biochemical studies for each glycosyltransferase. Microarrays of peptide acceptor substrates are very useful for this purpose, because of high-throughput and miniaturisation. Particularly attractive for these studies is spot-synthesis of peptides directly on an array, because it allows for the fast and cheap synthesis of tailor-made peptide libraries to investigate specific peptide substrates around a known lead structure.

A common problem with spot synthesis, however, is quality control. Peptide synthesis is not always reliable and on chip synthesis can be difficult to monitor. Given the in situ analysis capability of the gold platform, we have developed a methodology that allows us to do spot synthesis with concomitant analysis. Figure 7 shows the individual steps involved, which are fully compatible with Fmoc-protected amino acids used in many automated peptide synthesis systems [30, 31]. Hence, all the building blocks needed are commercially available. We have shown that the in situ analysis using MALDI-TOF MS allows for tight quality control after every step. Thus, poor couplings can easily be detected and if necessary be repeated before proceeding with synthesis. MALDI-TOF analysis can also give us information about the purity of the final peptide, and in particular detect any truncated side products.

Figure 7. Solid phase peptide synthesis on ‘gold array’ (SPOT) using Fmoc technology.

This spot synthesis was used to make a glycopeptide array to probe the substrate specificity of ppGalNAc T2, an isoform of a family of over 20 human glycosyltransferases involved in the first steps of mucin glycosylation (Figure 8). A peptide library around a natural polypeptide sequence was generated and it was found that glycosyltransferase activity was highly dependent on the nature of the two residues next to the threonine glycosylation site [30]. Interestingly, placing a proline on either site dramatically increased the activity of the enzyme and made the peptide a much better substrate than the natural sequence studied.

Figure 8. Glycopeptide synthesis appears to proceed to completion on the array surfaces as indicated by MALDI-TOFMS analysis of starting material and product.

Another application of our platform is in the analysis of genetic orders of glycosylation [32, 33]. The glycosyltransferase POMGnT catalyses the glycosylation of an unusual class of O-mannosyl glycans (Figure 9), which have been isolated as a major component of mouse brain, but so far have only been identified on a single protein, α-dystroglycan (α-DG), which has been found in muscle and brain tissue. Defective glycosylation of α-DG leads to congenital muscular dystrophies and a number of patients have been diagnosed with mutations in the POMGnT gene. Our technology has allowed us to study clinical mutants of POMGnT for activity against peptides derived from α-DG. Interestingly, one of these clinical mutations did not seem to affect enzyme kinetics, whereas other clinical point mutation abolished activity. Some correlation between enzyme activity and severity of disease was found, although this requires further investigation.

Figure 9. POMGnT1 is involved in the biosynthesis of unusual O-mannosyl glycans found in the brain. Mutations in the gene encoding for POMGnT1 can lead to congenital disorders of glycosylations, in particular muscular dystrophies.

It is interesting to note that the glycosylation of peptides using this enzymatic approach appears to proceed with excellent yields. Although quantification of reaction yield using mass spectrometry is difficult, the analysis of the gold surface by MALDI-TOF MS as shown in figures 8 and 9 gives a very clean spectrum in which only the product peak is visible, suggesting near quantitative yields of reaction. Thus, the solid-phase enzymatic synthesis of glycopeptides is possible. Such enzymatic routes are particularly interesting for the generation of linkages that are chemically more difficult to achieve, such as α-GlucNAc and α-GalNAc linkages as shown in figures 8 and 9 respectively.

The results shown so far demonstrate that mass spectrometric readout on gold arrays is very useful for following chemical and enzymatic reactions in a label-free manner. Because of the high resolution of MALDI-TOF MS even at high molecular weights, it should be possible to follow multiple reactions on the surface in parallel (multiplex analysis), provided that there is a difference in molecular weights between individual reaction sets. For proof of principle studies, we have immobilised mixtures of three Muc1 derived peptides on gold arrays (Figure 10, unpublished results). In previous studies we had shown that peptides Muc1fr1 is a good substrate for ppGalNAc T2 and complete glycosylation is observed when incubated with enzyme and UDP-GalNAc. Under the same reaction conditions, Muc1fr2 is only partially glycosylated and for Muc1fr3 we could not observe any glycosylation. The same results were obtained in peptide mixtures, suggesting that the methodology allows us to investigate several potential substrates in single spots. These investigations are currently extended to more complex mixtures.

Figure 10. The resolution of the platform readout by mass spetrometry allows for the investigation of multiple substrates in one spot as shown here for pairs of peptides derived from the Muc1 repeat sequence of mucins, which are interrogated as substrates for the mucin transferase ppGalNAc T2.

Some of the most important terminal monosaccharides on cell surfaces of humans and higher organisms are the sialic acids. A number of glycosyltransferases which generate sialosides are available and it was of interest to study these enzymes on gold surfaces. However, the mass spectrometric analysis of sialic acids can present problems, since sialosides are often not stable under MALDI-TOF conditions, and hydrolysis products are observed. This can be overcome by methylation of sialic acids prior to analysis; in particular formation of the methyl ester significantly increases the signal intensity. For this purpose, we tested a number of methylation protocols for suitability on gold surfaces and found that these significantly improved the signal and allowed us to demonstrate that sialylation had indeed taken place. For example, the transsialidase from Trypanosoma cruzi was able to transfer sialic acids in an α2,3 manner from the glycoprotein fetuin onto the gold surfaces containing lactose acceptors to generate α2,3 sialyllactose. In collaboration with the group of Paul Crocker at the University of Dundee we could show that CHO cells, engineered to display sialoadhesin on their surfaces, selectively recognised gold surfaces displaying α2,3 sialyllactose over lactose (Figure 11 [34]).

Figure 11. Sialic acids (Neu5Ac) are important components of cell surface oligosaccharides. The transsialidase TcTS from Trypanosoma cruzi can transfer sialic acids from glycoproteins such as fetuin to immobilised lactosides. The resulting sialolactosides are recognised by cell surface sialoadhesin receptors (collaboration with Prof Paul Crocker, University of Dundee)

Conclusions

An array platform consisting of gold surfaces covered with self-assembled functionalised monolayers has proven to be chemically and biologically robust and versatile. Many chemical and biochemical reactions can be studied on this surface and can proceed with very high efficiency, as measured by in situ mass spectrometry. In the past few years we have explored the application of this technology to glycosylation reactions and have found that it is suitable for many applications, in particular exploring enzyme specificity and activity. We believe that this technology is ideally suited to address the challenges of glycomics – high-throughput investigation of glycoenzyme activity and investigation of carbohydrate-protein interactions. The platform has the additional advantage that it is suited to a number of analytical techniques such as mass spectrometry and surface plasmon resonance spectroscopy, which makes it suitable for the development of diagnostic devices.

Acknowledgements

We are grateful to the EPSRC, BBSRC and FP7 for funding. SLF is the recipient of a Wolfson Merit Award of the Royal Society.

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Glyco-Bioinformatics today (August 2011) – Solutions and Problems

René Ranzinger* and William S. York

Complex Carbohydrate Research Center, The University of Georgia,
315 Riverbend Road, Athens GA, 30602, U.S.A.

E-Mail: *rene@ccrc.uga.edu

Received: 24th April 2012/Published: 11th July 2012

Abstract

Glyco-bioinformatics is an emerging sub-field of bioinformatics, aiming to develop tools, databases, web services and workflows to facilitate research in the field of glycomics. Although glyco-bioinformatics is still in its infancy a large set of web applications, stand-alone applications and databases have been developed recently. Most of the programs are available via the Internet and can be used freely by glycoscientists. In the first half of this chapter we give a non-comprehensive overview of the tools that have been developed for the different sub-fields of glyco-bioinformatics. In the second section we discuss fundamental problems that hinder rapid progress in the field and identify milestones to be achieved in order to overcome these problems.

Introduction

Bioinformatics is a multidisciplinary enterprise that spans the interface of biology and computer science. The power of genomics and proteomics to provide insight into the molecular bases of life stems directly from the application of bioinformatics tools, including algorithms, databases and programs that have been developed over the last decades. Glyco-bioinformatics is a sub-field of bioinformatics, aiming to develop similar tools to facilitate research in the field of glycomics. Glycomics has been overshadowed by its more famous siblings proteomics and genomics, emerging only recently to reveal the structural basis for many of the critical roles that complex carbohydrates play in biological development, cell function and the progression of disease. In contrast to the well accepted, traditional bioinformatics that is widely used in proteomics and genomics, glyco-bioinformatics is still in its infancy. One of the main obstacles to the development of effective glyco-bioinformatics is the fact that the molecular structures of carbohydrates are more complex than those of nucleic acids and proteins. Two amino-acids or two base-pairs are almost always connected by a single type of linkage, while a pair of monosaccharides can be linked by several different linkages. For example, two hexose residues can be linked by 1–2, 1–3, 1–4 or 1–6 bonds. Thus, a single monosaccharide can have several other monosaccharides attached to it such that carbohydrates, in contrast to polynucleic acids and proteins, are branched structures rather than linear chains. Furthermore, the number of distinct monosaccharide residues is much larger than the number of amino-acids and base pairs. Although some have argued that that less than 20 monosaccharides (d-Glc, d-GlcNAc, d-Man, l-IdoA, d-GlcA, d-Gal, d-GalNAc, d-Sia, l-Fuc, d-Xyl, each with two different anomeric states [1]), are required to generate the mammalian glycome, the number of natural monosaccharides increases drastically if we include the glycomes of other taxonomic groups such as plants, fungi and bacteria. This complexity and diversity are the main reasons that algorithms developed for genomics and proteomics cannot simply be reused for glycomics. These issues also give rise to problems with the analysis and interpretation of glycomics data, which are reflected in the slow rate at which such tools have been developed.

Although glyco-bioinformatics has not been as widely received as bioinformatics in other fields, several databases and tools for interpretation and analysis of glycomics data have been developed over the last decades. The first half of this chapter provides a non-comprehensive overview of the available tools and databases, which are illustrated by way of example. The second half of the chapter outlines obstacles and problems in the field of glyco-bioinformatics and suggests milestones that should be achieved in order to make the tools developed in this field more generally accessible and applicable to the diverse biological research that will be performed in the coming years.

Glyco-Bioinformatics Today

Although glyco-bioinformatics is still in its infancy, a sizeable collection of applications, databases and tools are available in this area. The next section of this chapter provides a non comprehensive overview of the existing tools.

Sequence representations and sequence formats

In contrast to genes and proteins, carbohydrates are often branched molecules that cannot easily be abstractly represented and displayed as simple character sequences. Several different representational schemes have been developed for the display of glycans in publications and in databases.

Figure 1. O-glycan specified by GlycomeDB ID 14282 illustrated using several different representation schemes. (A) CFG cartoon representation – color, (B) CFG carton representation – gray scale, (C) Oxford representation, and (D) textual representation using IUPAC names. The legend below the structures shows the different monosaccharide symbols and names used for each scheme together with the full IUPAC name for each monosaccharide.

Figure 1 shows an overview over the most commonly used glycan representation schemes. One of the earliest ways to represent carbohydrates is as an image that specifies each monosaccharide using its IUPAC short name (see Figure 1D) [2]. But these images have proven to be inconvenient when representing a large set of carbohydrates such as those produced in modern high throughput experiments. Therefore, representation schemes that replace the monosaccharide names with colored geometric figures (so called cartoon representations) have been developed. The CFG scheme (Figure 1A and 1B) was developed by the Consortium for Functional Glycomics for use by their web database and can be found in many publications [3]. The representation scheme developed by the Oxford GlycoBiology group (Figure 1C) uses a different set of black and white symbols [4] with the anomer and linkage positions indicated by the line style and angle of the bond, respectively, rather than by annotating it with characters and numbers. Several variations of these cartoons using different colors or shapes to represent monosaccharides can be found in publications and web pages.

Figure 2. O-glycan specified by GlycomeDB ID 14282 using two explicit chemical representation schemes.

Figure 2 illustrates the same structure shown in Figure 1 but using two different chemical representation schemes that are often used by analytical and synthetic chemists. In contrast to the cartoon representations, structural details (individual atoms and their stereochemistry) are explicitly displayed rather than represented as symbols or names.

However, images of carbohydrates are not suitable as a primary encoding for storing carbohydrate structures in databases or software applications, since it is quite difficult to program a computer to extract monosaccharide and linkage information from such images. In software applications and databases, genes and proteins are usually specified as linear sequences using one character of the English alphabet to represent each residue. In contrast, carbohydrates are usually branched molecules that consist of a larger variety of residues (monosaccharides and their modifications) that cannot be fully represented by the 26 characters in the English alphabet. Software and database developers have therefore been required to create new sequence formats and digital representations for carbohydrates. This unfortunately led to the development of several different sequence formats, many of which are used only in a single application or database.

Figure 3. O-glycan specified by GlycomeDB ID 14282 using different systematic sequence encodings. (A) LINUCS, (B) BCSDB encoding, (C) CarbBank sequence format, (D) sequence format of the GlycoBase(Lille) database, (E) GlycoCT format, (F) KCF format and (G) Linearcode® encoding.

Figure 3 shows several digital sequence formats that are currently in use. The problem of branched carbohydrate structures has been solved using three different approaches, which have been applied in different sequence formats. The first solution is to represent the carbohydrate as several lines of text, mirroring the textual representation often used in scientific journals (see Figure 1D). The sequence format used in the CarbBank database [5, 6] (see Figure 3C) is an example of this. The second solution is to linearize the carbohydrate by putting the branches in parentheses. Examples for this are LINUCS [7] (see Figure 3A), the format used by the BCSDB database [8] (see figure 3B), or the Linearcode® [9] (Figure 3G). In all presented cases, the format definition includes rules for sorting the branches with the aim of generating a unique character sequence for each carbohydrate. The third solution is a connection table approach wherein the residues are listed one by one and the bonds connecting these residues to each other are specified subsequently. Examples are the GlycoCT encoding [10] (Figure 3E) and the KCF format [11] (Figure 3F). The sequence format of the GlycoBase(Lille) database [12] (Figure 3D) is similar but encodes the linkage path to the reducing end before each of the residue names. There are also XML based formats that were created to take advantage of the extensibility and flexibility provided by XML as a data exchange language. Two examples, are shown in Figure 4A (CabosML [13]) and 4B (Glyde-II [14]).

In addition to the formats shown here, other notations, such as the three notations recommended by IUPAC [2] and several variations of this format have been used in scientific publications.

Figure 4. O-glycan specified by GlycomeDB ID 14282 in the XML based sequence format CabosML (A) and Glyde-II (B).

Carbohydrate structure databases

The first large publicly available database for carbohydrate structures was the CarbBank database developed and maintained in the 1980s and 1990s. This resource contains approximately 50,000 records with more than 23,000 structures from more than 13,000 publications, although no new structures have been added since funding was discontinued in the mid 1990s. Several subsequent database initiatives, such as GLYCOSCIENCES.de [15], CFG [16] and BCSDB, began by using some or all the structures from CarbBank as the initial information content. Currently active carbohydrate structure database projects are shown in Table 1 along with the URL of the database webpage, the sequence format used to encode carbohydrate structures and the number of structures in each database. Each of these resources also stores a set of meta-information that is associated with each structure. Table 2 provides an overview of this information.

Table 1. List of carbohydrate structure database projects. For each database the name of the project/database, the web page (URL), the used carbohydrate sequence format with the reference to Figure 3 in parentheses and the number of carbohydrate sequences in the database in the used sequence format (as of February 2012) is shown.

Database name

URL

Sequence Format
(Illustrated in Figure)

Number of Sequences

CarbBank

http://www.genome.jp/dbget-bin/www_bfind? carbbank

CarbBank format (3C)

23402

CFG database

http://www.functionalglycomics.org/

Linearcode® (3G)

9201

GLYCOSCIENCES.de

http://www.glycosciences.de/

LINUCS (3A)

23367

KEGG

http://www.genome.jp/kegg/glycan/

KCF (3F)

10978

EUROCarbDB

http://www.ebi.ac.uk/eurocarb/

GlycoCT (3E)

13471

GlycoBase(NIBRT)

http://glycobase.nibrt.ie/glycobase/show_nibrt.action

GlycoCT (3E)

7365

GlycoBase(Lille)

http://glycobase.univ-lille1.fr/base/

GlycoBase (Lille) format (3D)

248

BCSDB

http://csdb.glycoscience.ru/bacterial/

BCSDB format (3B)

11565

Table 2. Meta-information archived in carbohydrate structure databases.

Database

Biological source information

Structural Provenance Information

Other information

CarbBank

Species, organ, tissue, disease, aglycon

Literature References

CFG database

Species, organ, tissue, cell type, disease

MS, Glycan array

Literature References

GLYCOSCIENCES.de

Species

NMR

Literature References

KEGG

Pathway

EUROCarbDB

Species, organ, tissue, disease

NMR, HPLC, MS

Literature References

GlycoBase(NIBRT)

Species, organ, tissue, disease

HPLC

Literature References

GlycoBase(Lille)

Species

NMR

Literature References

BCSDB

Species, strain, sero group

NMR

Literature References

The available databases differ not only in the numbers of structures each contains, but also in the amount of additional information that is stored. All of the databases listed in Table 2 (with the exception of KEGG) provide the species information. Several of the databases provide a more detailed specification of the biological source, allowing the organ, the tissue or the cell type of the source sample to be described along with the disease-state of the source tissue. The BCSDB allows the strain and sero group of the bacterial source of the carbohydrate to be recorded. Most of the databases identify the experimental data that was used to characterize the carbohydrate structure and its interactions with other molecules. This data is either extracted from the literature (GLYCOSCEICNES.de, BCSDB) or is provided by the scientist that performed the experiment (CFG, GlycoBase [NIBRT], GlycoBase [Lille]). Finally almost all of the databases provide references to publications in which the carbohydrates were described or the experimental data was published.

Carbohydrate related databases

In addition to the databases that store carbohydrate structures, there is a large set of databases that store carbohydrate related information of interest to glycobiologists.

Table 3. Databases with information related to glycobiology.

Database

URL

Information Content

CAZy

http://www.cazy.org/

Enzymes and Carbohydrate Binding Modules

BRENDA

http://www.brenda-enzymes.org/

Enzymes

CFG Glycosyltransferases database

http://www.functionalglycomics.org/glycomics/molecule/jsp/glycoEnzyme/geMolecule.jsp

Enzymes

CFG Glycan Binding Proteins

http://www.functionalglycomics.org/glycomics/molecule/jsp/gbpMolecule-home.jsp

Lectins

CancerLectinDB

http://proline.physics.iisc.ernet.in/cancerdb/

Lectins

LectinDB

http://proline.physics.iisc.ernet.in/lectindb/

Lectins

UniProt

http://www.uniprot.org/

Proteins and Glycosylation position

O-GlycBase

http://www.cbs.dtu.dk/databases/OGLYCBASE/

Proteins and Glycosylation position

KEGG Pathway

http://www.genome.jp/kegg/pathway.html

Pathways

GlycoEpitope

http://www.glyco.is.ritsumei.ac.jp/epitope/

GlycoEpitopes

Table 3 shows some examples for databases with information related to glycobiology, such as descriptions of carbohydrate active enzymes (e.g., CAZy [17] and the CFG Glycosyltransferases database) or enzymes in general (e.g. BRENDA [18]). The KEGG pathway collection contains visual renderings of complex metabolic pathways that include references to carbohydrate structures and the enzymes that are involved in their metabolism. Several available databases store information about carbohydrate binding proteins, including both lectins and carbohydrate binding modules. These include the CFG Glycan Binding Proteins database, the LectinDB [19], the CancerLectinDB [20] and CAZy. A few databases (such as the GlycoEpitope database) contain information about carbohydrate motifs that are bound by proteins and antibodies. Some of the established proteomics databases such as UniProt [21] and O-GlycBase [22] contain other information that is relevant to glycobiology, such as predicted or experimentally verified glycosylation sites in glycoproteins.

Integration and standardization efforts

Although a considerable amount of structural and biological information for complex carbohydrates is available in the databases described above, most of these resources are isolated islands of data with little or no connection to other data sources. Nevertheless, carbohydrate structure databases have a significant amount of overlap due to the fact that many of the structures they contain were initially derived from the CarbBank database. In addition, cross-referencing of the CFG database and GLYCOSCIENCES.de was implemented to provide links from the CFG to GLYCOSCIENCES.de structure pages. Cross-database search methods were also established [23] to allow the BCSDB and GLYCOSCIENCES.de to be simultaneously searched using either's Web interface. However, beyond these few established interconnections, the databases are not connected to each other or to databases in other domains.

Scientists interested in finding all of the information available for a particular glycan structure have thus been forced to search for that structure several times using the web interface of each database separately. This is not an easy task, since the Web interfaces and the notations used by each database to describe structures and their searchable properties are quite different. Therefore, GlycomeDB [24, 25] was developed to provide a single access point for all of the structures in the carbohydrate structure databases described above. GlycomeDB integrates these databases and other information resources by specifying a single identifier (index) for each unique structure that they contain. GlycomeDB uses this index to maintain and map references for each structure to the data resources in which it is described, providing an unparalleled ability to identify an extensive set of specific database entries for each structure. Creation of a single index for each unique structure requires a single, consistent format to represent structures described in the different resources. Therefore, all structures and their monosaccharides are translated to GlycoCT format [10] and archived using customized data importers written for GlycomeDB along with tools, such as MonosaccharideDB (http://www.monosaccharidedb.org/), that allow monosaccharide representations to be translated from diverse namespaces to the GlycoCT namespace.

Another integration approach is implemented by the JCGGDB (http://jcggdb.jp/index_en.html), which provides a search interface comprising almost all glycosciences databases in Japan. This resource facilitates searching not only for carbohydrate structures in different databases but also for terms and keywords in various carbohydrate-related databases, including those holding information about lectins, antibody binding, glycolipids, glycoproteins and other aspects of glycoscience. The result of a JCGGDB search is a list of hyperlinks to resources containing information related to the user query.

Analytical tools

In addition to the databases described above, a large set of web applications and stand-alone applications has been developed to assist in the interpretation of experimental data.

Table 4. List of analytical tools. For each tool the name, the project URL, the type of processed data and a short description is given.

Name

URL

Data Type

Description

CcpnNMR Analysis

http://www.ccpn.ac.uk/software/analysis

NMR

Annotation assistance for NMR data

Caspar

http://www.casper.organ.su.se/casper/

NMR

Annotation of chemical shifts, spectra simulation

GlyNest

http://www.glycosciences.de/database/nmr/

NMR

Matching of shifts with stored NMR data, spectra simulation

GlycoMod

http://web.expasy.org/glycomod/

MS

Composition analysis

GlycoPeakfinder

http://www.glyco-peakfinder.org/

MS

Composition analysis

GlycoWorkbench

http://www.glycoworkbench.org/

MS

Spectrum annotation with user defined structures or structures from a database

Most of the available tools have been developed to assist in the interpretation of NMR data and mass spectrometry data. For NMR tools there are two different approaches: 1) annotation of chemical shifts based on previously recorded experimental data or 2) simulation of a theoretical spectrum of a structure that can be compared with experimental data. These approaches are followed by the web based applications Caspar [26] and GlyNest [27]. A similar tool is available as part of the BCSDB database. The CcpnNMR Analysis application is a large stand-alone software suite for displaying, analyzing and interpreting NMR data of diverse types of molecules, including carbohydrates.

Another set of tools has been developed to assist in the interpretation of mass spectrometric data. Three main approaches are used: 1) de novo composition analysis, where each peak is annotated with a carbohydrate composition or the composition of a fragment. This approach is implemented in the web applications GlycoMod [28] and GlycoPeakfinder [29]. Both tools allow possible structures corresponding to an identified composition to be located in a structural database (GlycoSuiteDB [30] or GLYCOSCIENCES.de, respectively). 2) Annotation of the spectra with user defined structures or structures retrieved from a database. This strategy is used in the stand alone application GlycoWorkbench [31], which has access to several of the carbohydrate structure databases shown above. Each of the structures is matched against the spectrum and scored. There are also commercial tools available following the same strategy (e.g. SimGlycan® [32] or ProteinScape [33]). 3) Annotation of spectra with theoretical fuzzy structures (cartoons) generated for each observed monosaccharide composition using implicit biosynthetic rules rather than selection from a database. This strategy is implemented in the Cartoonist applications [34, 35] developed by the CFG.

3D structure tools

Due to the importance of molecular geometry in mediating the biological functions of glycans and glycoconjugates, 3D structural analysis of these molecules has emerged as a key research area in glycobiology. Nevertheless, carbohydrate moieties of glycoconjugates are still often neglected in crystallographic or spectroscopic analyses of the 3D structures of these complex molecules, having either been removed by chemical or enzymatic treatment before the experimental data is recorded or simply ignored when processing the data. Failure to fully elucidate the 3D structure of the glycan moieties often stems from their molecular flexibility, which confounds the interpretation of the experimental data. Thus, primary 3D structure databases such as the PDB [36] often contain truncated carbohydrate structures, which are reflected in carbohydrate structure databases whose contents are extracted from the PDB. These include GLYCOSCIENCES.de, which uses pdb2linucs [37] to extract this information and the GlycoConjugate Data Bank [38]. However, complete 3D structures are required as starting points for molecular dynamics simulations of glycoconjugates. Therefore programs have been developed to generate energetically feasible 3D structures for carbohydrates in silico. These tools include Sweet [39] and the Glycam molecular structure builder (http://glycam.ccrc.uga.edu/ccrc/biombuilder/biomb_index.jsp). The hypothetical 3D structure can then be attached to protein structure models, either manually or by using other programs such as GlyProt [40], for use as starting points for molecular dynamics simulations or docking studies. Other resources such as GlycoMapsDB [41] and GlyTorsion [42] provide conformational maps and/or statistical analyses of glycan geometry, which can be used to evaluate and compare the 3D models.

Glycosylation prediction and analysis tools

As mentioned above, several databases contain information about the glycosylation sites of proteins, either directly stored as meta-information for the protein (e.g. Uniprot, O-GlycBase) or as part of the primary data/structure (e.g. PDB). This information has been analyzed and used to develop tools for predicting the glycosylation sites of other proteins. These tools include NetNGlyc and NetOGlyc [43], which predict glycosylation sites for N- and O-glycans, respectively, using artificial neural networks (ANNs) trained with glycosylation-site information gleaned from databases. Similar tools are available for other types of glycosylation, such as C-mannosylation [44], GPI-anchors [45] or O-GlcNAc substitution [46]. Several other data processing and mining strategies have been used in addition to ANNs for this purpose. An example is EnsembleGly [47], which implements support vector machines for glycosylation site prediction. However, users should be aware that techniques such as ANNs and support vector machines do not provide predictions that are 100% correct, often generating false positive or false negative results. The predictive accuracy of these tools depends on the specific algorithm that is implemented and the range of structural diversity for the molecules included in the training set.

Table 5. Tools for the prediction of glycosylation sites and analysis of glycosylation.

Name

URL

Description

NetNGlyc

http://www.cbs.dtu.dk/services/NetNGlyc/

Prediction of N-glycosylation sites using neuronal networks.

NetOGlyc

http://www.cbs.dtu.dk/services/NetOGlyc/

Prediction of O-glycosylation sites using neuronal networks.

Big-PI Predictor

http://mendel.imp.ac.at/gpi/gpi_server.html

Prediction of GPI modification sites.

EnsembleGly

http://turing.cs.iastate.edu/EnsembleGly/

Prediction of glycosylation sites using support vector machines.

GlySeq

http://www.glycosciences.de/tools/glyseq/

Statistical analysis of amino acid sequences that are glycosylated, based on information from PDB.

GlyVicinity

http://www.glycosciences.de/tools/glyvicinity/

Statistical analysis of amino acids that are physically close to carbohydrates, based on information from the PDB.

Other web applications such as GlySeq and GlyVicinity [42] provide statistical analysis of the amino acid sequences in the neighborhood of the glycosylation site or which are close in 3D space to the sugars. This information, which was extracted from glycosylated proteins in the PDB, can be used as the basis for other glycosylation-site prediction tools.

Obstacles and Challenges in Glyco-Bioinformatics

As the previous chapter section has shown, a basic set of databases and applications is now available to assist in glycomics research. The number of such tools will likely grow as analysts develop new high-throughput analytical methods, which require considerable software support. In addition, the mining and integration of data from several different resources will become more important for discovering relationships between diverse aspects of glycobiology, including molecular structure, cell compartmentalization, metabolism, biological development and disease. However, several obstacles currently slow the development of software and other glyco-bioinformatics tools for these purposes. This chapter section attempts to identify some of these obstacles from the point of view of the authors.

Sequence representations and sequence formats

The existence of competing graphical representation schemes for carbohydrate structure and the non-uniform usage of these schemes has long been considered a problem by many glycobiologists. Adoption of a single graphical representation scheme (or at least one scheme for each scientific journal) would be preferable. However, it is difficult to get glycoscientists to agree on a single scheme, because each of the competing graphical representations has its own advantages. Due to their complexity, explicit chemical representation schemes (Figure 2) are not appropriate to describe a large number of carbohydrates identified in a high-throughput experiment. Generating images of spectra annotated with structural assignments rendered using this kind of representation is even more problematic, so cartoon structures are often used for this purpose. On the other hand, cartoon representations are typically limited to carbohydrate structures composed of small, well-defined sets of biologically relevant monosaccharide residues and rarely provide the level of structural detail required to fully describe intermediates in the chemical synthesis of a complex carbohydrate. A reasonable compromise may be the selection of one explicit representation scheme for depicting chemically diverse, detailed structures and one cartoon representation for biological structures composed of common monosaccharide residues. In any case, this decision is up to the journal editors and the research community; the duty of glyco-bioinformaticians is to provide the tools required to generate and process structural representations using the schemes that are selected by the community. It should be noted that, so far, no tools that translate carbohydrate structures from one of the digital sequence formats directly to a chemical representation (Figure 2) are available.

From the view point of bioinformatics, the existence of the different graphical representation schemes is not a major problem. As pointed out above, images are not suitable as a primary digital format for storing and exchanging carbohydrate structural information. However, images that conform to an individual user's requirements can be generated automatically from digital sequence information, which is usually encoded as highly formatted text (e.g., GlycoCT and XML). Several tools and databases, including GlycoWorkbench [31], GlycomeDB [24] and EUROCarbDB [48], already allow users to choose among several different graphical representation schemes to generate images from such digitally encoded sequences. The major problem is that each of the various databases and tools uses its own textual description of carbohydrate structure rather than using a common standard, even though there is an agreement within the glycobiology community that such a standard is needed. Although an XML-based format for GLYcan Data Exchange (GLYDE-II) has been selected as the standard [49], this format is so far supported only by a few databases and applications.

Two impediments to the implementation of GLYDE-II exist.

Algorithms designed to search or manipulate structures (e.g. substructure searches and composition searches) are almost always tied closely to the sequence format used by the program or database. Thus, adoption of GLYDE-II as the internal format for structural representation would require a large portion of the internal logic to be rewritten.

No currently available software application or source code supports the translation of all of the various sequence formats (see above) to GLYDE-II and back. Only a part of the translation functionality is available in the existing program code in GlycomeDB and MonosaccharideDB.

Thus, there is a critical need to rapidly develop freely available software that translates the different sequence formats into each other. Although it is not practical to replace the internal encoding of most databases and data-processing tools with GLYDE-II, translation software would allow all the tools to read and write into the standard format for data export, import and exchange. Developers of new projects would probably benefit by considering GLYDE-II (or one of the other existing formats) for structural representations rather than reinventing the wheel by creating yet another sequence format.

Meta-information and standards

Recently, interest in maintaining and organizing meta-information for the annotation of glycan sequences has grown since this type of information provides the basis for knowledge discovery when data mining strategies are implemented. Meta data includes information about the biological source of a glycan, pointers to relevant database resources and literature references describing the glycan, its characterization, biosynthesis and biological properties. Each carbohydrate structure database stores a different set of meta-information related to the carbohydrate structures that it contains. The first large carbohydrate structure database initiative, CarbBank, attempted to collect several types of meta data for each glycan structure, including bibliographic information, biological origin (species, tissue, organ type, cell line, disease) and an identifier (such as a protein ID) for any aglycon attached to the reducing end. A large subset of CarbBank records contains most of this information, although it is not present in every entry. Most of the database projects that succeeded CarbBank took a step back and reduced the amount of meta-information that they stored. For example, description of the biological origin might be limited to species only or bibliographic information might not be included. Unfortunately, many of these databases lack a common vocabulary or syntax to describe the information (such as the identity of the source species) that has been retained. Sometimes, identifiers that are specified in publicly available ontologies or dictionaries (e.g. NCBI taxonomy) are used but in other cases local dictionaries have been created or free text fields are utilized. This makes it very difficult to integrate or compare data that is retrieved from more than one database.

Solving these problems requires general agreement regarding a minimal set of meta-information that should be stored in a glycan structure database to provide a universally informative data set. Development of such guidelines will require input from members of the glycobiology community with diverse expertise in order to ensure that they foster datasets that address current and future research needs of the community. On the other hand, there is also a critical need for input from computer scientists who should agree, for example, on digital formats for the storage and exchange of this information. As described above in the context of carbohydrate structures, efficient data integration across different databases requires selection of a single standard representation for exchange of each type of data that has to be stored. Adaptation of the agreed-upon standards by databases and software applications will create many advantages, including facile exchange of readily parsable information among these resources, which is required for the development of effective approaches for data retrieval, browsing and mining.

Experimental data and procedures

The glycobiology community has come to recognize the considerable advantages of archiving primary experimental data along with meta data that is associated with each glycan structure. Such primary data, which include NMR and mass spectra that were used to elucidate molecular structures, can be reused as fingerprints that allow those structures to be identified in biological samples. Primary data of this type is rarely stored in carbohydrate databases, and most of the available spectral information is in the form of highly processed lists of parameter values, such as chemical shifts and lists of ion abundances. This data reduction is not always desirable, as the raw data is usually a richer source of information for feature extraction and other data mining approaches or for re-evaluation of the experimental results. A major impediment to the collection and organization of primary experimental data is that they are often represented using proprietary, device-specific formats that can only be processed by customized software applications. As is the case for the meta-information, community agreement is needed to identify the most valuable types of experimental data that should be archived and the standard digital formats that should be utilized to store and export the data.

In addition to the data itself, it is important to record information such as sample preparation protocols and device setups that were used to generate the data. This information is required to generate transparent results that can be easily understood and reproduced by other researchers. In some of the existing resources these parameters are documented as short text blocks or provided indirectly by reference to the Methods section of a published manuscript. However, these approaches by themselves rarely provide information that is sufficiently detailed to allow complete reproduction of the data set, especially by novices in glycoanalytics.

Two aspects should be considered when storing such information:

sufficiency – the information should be sufficiently detailed to allow the experiment to be understood and reproduced

practicability – the minimum information required to achieve consideration 1 should be included, since the collection of large amounts of such information is often impractical, inefficient and time consuming.

For example, the level of detail specified by the EXACT ontology [50] may lead to the storage of too much information, which is difficult to collect and support. Minimum information checklists based on these considerations have been implemented in other research areas (e.g. MIAPE [51] for proteomics and MIAME [52] for microarray analysis). Once the checklists specifying the minimum information describing an experiment are defined, it is important to standardize the digital format for this information. This will not only facilitate automatic checking for compliance with the checklists but can also be used to exchange this information among different systems.

It should be noted that currently accepted checklists are not designed to store information at the level of detail required by non-experts to reproduce the experiment. Therefore, it would be very useful to supplement these checklists with references to freely available textual descriptions of experimental protocols that can be used to perform identical experiments in a different laboratory.

Data integration

The data integration and cross referencing efforts that have started with GlycomeDB and JCGGDB bring considerable benefit to the community, bringing several different data sources together and allowing information to be explored simply by following hyperlinks to different resources and web pages. We consider continuation of this work and integration of all carbohydrate databases into a network of interconnected resources to be a high priority. The next obvious step to this end is to link the different data resources not only to GlycomeDB but also to each other, developing an easily accessible and comprehensive glyco-bioinformatics infrastructure. As envisioned by the Japanese JCGGDB initiative, data integration efforts should extend beyond glycomics resources, providing access to tools and databases (such as Lipidbank or UniProt) that support genomics, proteomics and lipidomics research. Together, this infrastructure should establish methods to identify glycoconjugates that consist of specific carbohydrate structures along with the specific proteins and/or lipids to which they are attached. Implementation of this functionality should facilitate searches to find glycoconjugates that contain a moiety (selected from a glycan, protein or lipid database) connected to moieties that may be fully described in orthogonal databases. Success in this area clearly demands collaboration and cooperation between informaticians who develop databases with diverse information content. Again, this will require the adoption of standard descriptors and exchange formats that allow communication between these data resources.

What is out there?

Not all of the existing glyco-bioinformatics programs and databases have been described in the scientific literature. This makes it difficult for bench scientists and software developers to determine whether a software application or database that addresses a specific informatics need already exists. This lack of information has led to the development of many programs with redundant functionality, essentially reinventing the wheel over and over again. To improve the visibility of the available software applications, several review articles (e.g. [53–55]) have been published. In addition, the Web pages of large research initiatives such as the CFG (http://www.functionalglycomics.org/static/consortium/links.shtml) and EUROCarbDB (http://www.eurocarbdb.org/links) often provide links to computational tools that support their scientific domain of interest. Often, descriptions of the tools in review papers or on Web pages are rather short, making it difficult to evaluate the usefulness or relevance of the tools that are listed there. To make matters worse, the URL of the Web page providing access to a tool may change, leading to dead links that give the mistaken impression the software project no longer exists. Since the review papers and on Web pages are often written by a “power user” who may not have been directly involved in the development of the tool, the technical description may be incomplete, inaccurate or simply irrelevant. Finally, review papers and Web links tend to focus on databases, web applications or stand alone applications. Other classes of informatics tools such as Web services and workflows, which may constitute more efficient ways of solving various problems, are usually not described since they are more difficult to identify, understand and invoke.

For the reasons described above, we are developing a Web-based, open access system that allows individuals to register software applications, Web services, workflows, databases and programming libraries that they have developed to support research in the glycobiology domain. The information provided by this GlycomicsPortal is entered directly by registered software developers, making it more likely that it is accurate and up to date. Methods that facilitate revision of this information ensure that URL changes do not result in dead links. A Web based search implemented by the Portal can be used by bench scientists and theoreticians to find tools and databases that provide or process data critical for the success of their research. The system is also of interest to software developers, who can use it to find existing tools, programming libraries and Web services that they can integrate into their own informatics systems.

General considerations for glyco-bioinformatics projects

The success of glyco-bioinformatics in a rapidly changing research environment will benefit immensely by the unconstrained, open exchange of programs, functionality and source code. Many software applications with nearly identical functionality have been developed from scratch because individuals did not have access to previously written source code or fully developed tools that address their current needs. This reinvention of the wheel has resulted in a considerable waste of human talent and time. There are several approaches that a software developer can use to make tools available to the community, allowing others to access their work and benefit from it. One often-used strategy is the development of freely available Web based applications and Web databases, or to provide programs for free download. Alternatively, Web services allow other software developers to remotely invoke computational resources using standardized interfaces. Perhaps, the most effective way of sharing the results of software development is to make the source code of a program freely available. This not only facilitates the reuse of functionality that required extensive effort to implement and test, but also provides an opportunity to extend and optimize the programs providing this functionality. Although this may seem like a no-win situation for a software developer who provides the source code for the benefit of others – nurturing of a culture that encourages sharing of these resources will eventually benefit everyone. Progress in this area will require the leadership by funding agencies, who increasingly demand the free and open dissemination of software developed as a result of projects that they sponsor.

Acknowledgments

The authors wish to thank the NIH National Institute for General Medical Sciences for support of bioinformatics infrastructure development through the Integrated Technology Resource for Biomedical Glycomics (8P41GM103490).

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Representing Carbohydrates by Pseudoreceptor Models for Virtual Screening in Drug Discovery

Max Pillong and Gisbert Schneider*

Swiss Federal Institute of Technology (ETH), Department of Chemistry and Applied Biosciences, Zürich, Switzerland

E-Mail: *gisbert.schneider@pharma.ethz.ch

Received: 9th January 2012/Published: 11th July 2012

Abstract

Carbohydrates provide a rich source of structural diversity that could be increasingly useful for innovative drug design. We suggest a representation of monosaccharides based on their pharmacophoric properties (pseudoreceptor model) to enable quantitative similarity searching with the aim to identify sugar bioisosters and functionally equivalent scaffolds for synthesis. We present a bioinformatical comparison of carbohydrate structures based on pseudoreceptor models. A similarity matrix was computed for 19 monosaccharide structures. As an outcome of this preliminary analysis, one might consider both glucose and deoxyribose as ‘universal’ sugars with regard to their receptor interaction potential. Potential applications of pharmacophore feature representations of carbohydrate structures in bioinformatics are discussed. A recent case study is reviewed that led to the identification of aminoglycoside scaffold replacements with antibacterial potential by pseudoreceptor-based virtual screening of a large compound library.

Introduction

One of the early and pivotal steps in drug discovery is the identification of structurally novel chemical entities (NCE) that exhibit a desired effect on a pharmaceutically relevant target molecule. Computer-assisted molecular design and ‘virtual screening’ technology is being increasingly used for this purpose with carbohydrate moieties offering a potentially substantial increase in structural diversity of candidate compounds [1]. Cipolla et al. recently affirmed that “[the] polyfunctionality of carbohydrates stimulate[s] their use as scaffolds for the generation of libraries by combinatorial decoration with different pharmacophores.”[2] As a prerequisite for success in this endeavour, accurate representations of molecular structure and computation of relevant properties is essential. Current virtual screening approaches like, e.g., automated ligand-receptor docking or pharmacophore similarity searching typically rely on heuristic or empirical methods for conformer generation and property estimation, offering the advantage to computationally sieve through several millions of compounds [3]. As a result, prioritized virtual hits are suggested for biochemical activity determination. This strategy has been successfully applied to many hit finding exercises in early-phase drug discovery, by complementing experimental high-throughput screening as a source for new pharmacologically appealing compounds [4]. While mere hit retrieval seems feasible by virtual screening the next step in the drug discovery pipeline, namely automated computer-assisted hit-to-lead optimization, requires further attention and method development [5].

Potentially different representations of molecular structures, their dynamics and properties are required for each step of chemical optimization [6]. Therefore, it will be necessary to develop and adapt appropriate molecular ‘descriptors’ of compounds of pharmaceutical interest. Although the role of carbohydrate moieties for ligand-receptor interactions has been realized and partially addressed in computational medicinal chemistry [7, 8], specifically in the field of lectin-sugar interactions [9] and natural product analysis [10, 11], carbohydrates have rarely been explicitly considered for virtual screening or computer-assisted drug design, and consequently our understanding of apt descriptors is limited. Current bioinformatical analyses of biologically relevant polycarbohydrate structures, e.g. glycan sequences, usually exclude modelling of potential receptor interactions [12]. Here, we suggest a ‘pseudoreceptor’ representation of monosaccharides as a conformation-sensitive descriptor for molecular modelling [13]. Recently, we successfully applied this theoretical concept to finding small synthetic inhibitors of bacterial protein biosynthesis by taking aminoglycosides as query compounds for virtual screening of large compound libraries [14].

Carbohydrate-protein Interactions and Pseudoreceptor Modelling

Sugar-protein interactions are of critical importance in a wide range of biochemical processes and functions. Complex formation is largely mediated by weak interactions, which give rise to specific association between the guest (carbohydrate) and host (macromolecular receptor) molecule. The hydroxyl groups of carbohydrates can act as both hydrogen-bond donors and acceptors, and form multiple hydrogen-bridges with a receptor structure. Tight hydrogen-bond networks have indeed been identified from crystal structures of carbohydrate-protein complexes (Figure 1A). In addition, partially positively charged carbons of carbohydrate ring systems can interact with π-electron systems and form CH-π interactions between carbohydrate ligands and their macromolecular receptors, typically via arene host systems (Figure 1B) [15]. In proteins, the amino acid side chains of Trp, Phe and Tyr are frequently found to recognize carbohydrate ligands [16, 17]. Using density functional theory (DFT), the average energy contribution of such an interaction has been estimated to be approximately -2.5kcal×mol⁻¹ [18, 19]. Its length is similar to the average hydrogen-bond length (approx. 3 Å) observed in drug-protein interactions [20], with an optimal distance for the CH-π dispersion interaction of approximately 3.7 Å [18].

Figure 1. Examples of carbohydrate-protein interactions. (A) Hydrogen-bond formation between maltoporin (LamB) and Fru-αdGlc (PDB [21] ID: 1af6 [22]); (B) CH-π interaction between human carbohydrate binding protein S-Lac lectin (L-14-II) and lactose (PDB ID: 1hlc [23]).

Figure 2. Cartoon representation of the CH-π interaction between the aromatic residue W65 of S-Lac lectin and partially positively charged carbon atoms of lactose (PDB ID: 1hlc). Dashed lines are for illustration only. The right panel represents an energy-minimized conformation of lactose with computed partial charges color coded in blue (positive), white (neutral), and red (negative); color intensity corresponds to the absolute charge value. Charges were computed with MOPAC (http://openmopac.net/) with PM6-DH+ [24] and the COSMO implicit solvation model [25].

Generally, such polar interaction patterns are appealing features for designing drugs with high binding potency and selectivity. If one is interested in comparing the interaction potential (‘pharmacophoric potential’) of carbohydrates with other drug-like compounds by computational means, these features have to be adequately represented as numerical, typically vectorial, molecular ‘descriptors'. Mathematical descriptor comparison enables virtual screening for compounds with similar properties but different architecture (‘scaffold-hopping', bioisosteric replacement) and compound clustering.

One such descriptor is termed ‘pseudoreceptor', which may be seen as an idealized receptor pocket that is constructed around a ligand conformation or a conformation ensemble. The idea is to place virtual receptor atoms (potential pharmacophoric points, PPP) by applying geometric constraints for receptor-ligand interactions (Figure 3). For example, the geometry of a hydrogen-bridge is defined by the distance r between the donor and acceptor base atom, and the angles α and ω (Figure 3A). Similar definitions are applied for arene-arene, lipophilic and ionic interactions, to name just the most prominent examples [26, 27]. For rapid similarity analysis and database searching the spatial constellation of PPPs may be converted to a linear vector representation. We here used the PRPS pseudoreceptor model [28] and limited the number of PPPs to –OH mediated hydrogen-bridges only, owed to the fact that PRPS in its current version does not consider CH-π interactions.

Briefly, PRPS models were translated into an alignment-free correlation vector representation. Such a pseudoreceptor-derived correlation vector encodes the distance-based frequency of pairs of pseudoatom features present in the model (Eq. 1). Pseudoatom pairs with a distance up to 15 Å (in 1 Å increments) were annotated. This resulted in a vector giving the number Freq of pseudoatom pairs with features x and y at distance d.

, where
(1)

the Kronecker delta δ computes to 1 whenever a pair of the pseudoatoms i and j exists at distance d. Similarity analysis of monosaccharides was performed by computing the Euclidean distance between their respective pseudoreceptor correlation vectors. This general idea of property encoding for molecular similarity analysis was introduced by Moreau and Broto [29], and has later been adapted to various applications ranging from structural modelling [30] and database searching [31] to molecular de novo design [32].

Figure 3. Principle of pseudoreceptor generation. (A) Geometric constraints for placing potential hydrogen-bond donor points around a ligand carbonyl oxygen atom, (B) Potential hydrogen-bond acceptor (red) and -donor (blue) points placed around a hydroxyl group of glucose, (C) A pseudoreceptor model of glucose.

Similarity of Monosaccharides Based on Pseudoreceptor Models

Representing bioactive compounds by their pharmacophores provides the means to sort them according to their interaction potential with macromolecular receptors. We performed such an analysis for 19 selected monosaccharides that are abundant in natural products and form carbohydrate components of bacterial cell walls and glycosylated proteins [33–35]. Sugar conformations were used as presented in Figure 4. Prior to PRPS pseudoreceptor generation we subjected all compounds to energy relaxation to eliminate potentially remaining strain. Then, each structure was represented as a vector coding for the corresponding pseudoreceptor model. Subsequent hierarchical clustering of the vectors using Ward's method [36] led to the tree presented in Figure 5A. It is apparent that due to different receptor interaction potential caused by varying numbers of hydroxyl groups the C₅ carbohydrates (orange circles) are separated from C₆ carbohydrates (blue circles). Glucose and deoxyribose occupy prominent positions in the tree, located between the bulk of C₅ and C₆ sugar molecules. One might therefore consider both glucose and deoxyribose as ‘universal’ monosaccharides with regard to their receptor interaction potential expressed by the pseudoreceptor model. The most distant cluster pairs are (mannose, gulose) and (xylose, arabinose). One might also speculate that while mannose and gulose (xylose and arabinose, respectively) seem exchangeable, a substitution of, e.g., mannose by xylose is structurally or functionally more severe. Such predictions require rigorous chemical and biological testing.

Figure 5B presents a matrix of pairwise similarities between the 19 monosaccharides, again highlighting a potentially special role of glucose and deoxyribose. Such a similarity table could serve as a basis for quantifying the similarity of glycans with regard to potential receptor interactions. In analogy to amino acid substitution matrices [37] this matrix could be used for generating glycan alignments, which would be complementary to existing similarity schemes based on sugar types, as e.g. produced by the software KCaM (KEGG Carbohydrate Matcher) [38, 39].

We furthermore see a requirement for consideration of CH-π interactions in pharmacophore modelling tools such as our PRPS pseudoreceptor approach. Their critical importance in specific carbohydrate-protein association has been recognized but not fully harvested by bioinformatics and computer-assisted molecular modelling and design. It is likely that the dendrogram and similarity matrix presented in Figure 5 will be subject to partial modification when CH-π interactions are considered as PPPs. In this regard our present study should be considered as preliminary only. More detailed computational investigations are necessary to determine the impact of CH-π interaction modelling on the outcome of molecular similarity analyses.

Figure 4. Repertoire of monosaccharides used in this study.

Figure 5. Clustering of carbohydrates represented by PRPS pseudoreceptor models. (A) Ward clustering; (B) hierarchical tree and similarity analysis (heat map coloring from white to red indicates greater similarity). Carbohydrates considered: β-d-allopyranose, β-d-altropyranose, α-d-arabinofuranose, β-d-desoxyribofuranose, β-d-fructofuranose, α-d-galactopyranose, β-d-glucopyranose, β-d-gulopyranose, β-d-idopyranose, α-d-lyxopyranose, α-d-mannopyranose, β-d-psicopyranose, β-d-ribofuranose, α-d-ribulofuranose, β-d-sorbopyranose, α-d-tagatopyranose, α-d-talopyranose, α-d-xylofuranose, β-d-xylulofuranose.

“Scaffold-hopping” Using an Ensemble Pseudoreceptor

In a recent drug discovery project [14], we performed pseudoreceptor-based virtual screening to find replacements of aminoglycoside antibiotics that are able to block in vitro transcription and translation. This proof-of-concept study demonstrates the applicability of pseudoreceptor representations of carbohydrate derivatives to performing ‘scaffold-hops', i.e. finding isofunctional compounds with different architecture [40]. The pharmacophore model matched an alternative chemotype, namely the vanilloid derivative 1, which possesses lower structural complexity and greater synthetic accessibility than the reference aminoglycosides.

The pseudoreceptor was constructed for eight aminoglycosides bound to their 16S RNA target (ribosomal A-site) (Figure 6). Crystal structure conformations of the ligands were aligned to form a conformational ensemble. Then, a database of screening compounds was converted to PRPS vectors (single compound conformations were computed), and compounds were sorted by decreasing similarity to the reference pseudoreceptor model. From the top-ranking candidates, ten compounds were selected, ordered and tested in an in vitro transcription/translation assay, five of which exhibited inhibitory activity. The most potent hit 1 is a vanilloid derivative (Scheme 1), with striking similarity to capsaicin (methyl vanillyl nonenamide from chili pepper), which is also known as an antibiotic agent [41]. It is of note that aminoglycoside antibiotics, in turn, also block vanilloid receptor 1 (TRPV1) [42]. These observations corroborate the ensemble pseudoreceptor as a common feature that allows for scaffold replacements, which is relevant not only for the replacement of sugar moieties in case of poor synthetic accessibility, but in particular for identifying carbohydrates and carbohydrate derivatives as building blocks in medicinal chemistry.

Figure 6. Ensemble pseudoreceptor model (left) generated from the alignment of eight aminoglycosides bound to the ribosomal A-site (right). PDB identifiers: gentamicin C1A (2et3) [43], paromomycin (1j7t) [44], tobramycin (1lc4) [45], neomycin (2et4) [43], neamin (2f4s) [46], kanamycin A (2esi) [43], geneticin (1mwl) [47], amikacin (2g5q) [48].

Scheme 1. Bioactive compound 1 found by pseudoreceptor screening as a replacement for aminoglycoside antibiotics. Compound 1 exhibits close structural similarity to capsaicin.

Conclusions

‘Fuzzy’ (permissive) pharmacophore representations of molecular structures allow for retrieving alternative chemotypes with lower structural complexity and greater synthetic accessibility than the reference compound(s). This concept was pursued to group monosaccharides according to their pharmacophore similarity. Structures were represented by simplistic pseudoreceptor models, and a monosaccharide similarity matrix was obtained that might be suited as a basis for property-based alignments, virtual compound screening and the design of bioactive compounds. The pseudoreceptor approach used in this preliminary study complements the many existing tools for pharmacophore-based hit and lead structure identification [49]. Without doubt, other molecular representations that allow for abstraction from the atomistic chemical structure and molecular constitution will also be suitable for this purpose, possibly even outperform our specific PRPS implementation. We motivate the use of property- and pharmacophore-based descriptors for carbohydrate modelling, specifically glycan analysis, to identify potential targets (macromolecular receptors) and consider molecular similarity not only by comparing sugar types but also by taking into consideration functionally relevant features. Ideally, virtual screening similarity metrics based on structural fingerprints are combined with metrics that are based on functionally relevant molecular features and properties [50–52]. For further progress in this field, CH-π interactions should be added to the set of pharmacophore feature types to more adequately model carbohydrate-target interactions.

Acknowledgements

G.S. is grateful to the Beilstein-Institut for a travel grant and invitation to attend the 2nd Beilstein Symposium on Glyco-Bioinformatics held in Potsdam in 2011. ETH Zürich and the Deutsche Forschungsgemeinschaft (FOR1406, TP4) are thanked for financial support. The Chemical Computing Group Inc., Montreal, Canada, provided a research license of MOE.

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Glycomimetics: Tools for Investigation of Functional Diversity in the Carbohydrate Regime

Thisbe K. Lindhorst

Otto Diels Institute of Organic Chemistry, Christiana Albertina University of Kiel,
Otto-Hahn-Platz 3–4, D-24098 Kiel, Germany

E-Mail: tklind@oc.uni-kiel.de

Received: 2nd May 2012/Published: 11th July 2012

This account is dedicated to the memory of Willi von der Lieth

Abstract

Glycomimetics are valuable tools in glycobiology, suited to address the queries of glycomics. Since in glycomimetics the natural structural features of oligosaccharides have been altered in various ways, the nomenclature that is used to systematically describe structures and properties of naturally occurring sugar structures cannot be applied. An appropriate nomenclature is desirable. Moreover, it is necessary to understand the conformational properties that are displayed by – especially – multivalent glycomimetics. Molecular dynamics simulations using explicit solvent molecules are suited to obtain an impression of the conformational space occupied by various multivalent glycomimetics such as the glycodendrimers and so-called octopus glycosides. Unexpected similarities on one hand and discrepancies on the other hand have been shown by extensive modelling and can be correlated with the results of biological testing.

Introduction

There are three basic classes of biopolymers, the oligonucleotides, the proteins, and the carbohydrates. Among these three, the carbohydrates hold the greatest potential for structural diversity. The number of possible oligosaccharide structures exceeds that of possible peptides and oligonucleotides, respectively, by a nameless order of magnitude [1]. There is a number of principle differences between oligonucleotide, oligopeptide, and oligosaccharide structures. Most importantly, oligosaccharides occur in branched forms, whereas oligonucleotides and oligopeptides can only form linear chains (cf. Figure 1). Moreover, configurational alterations are found frequently in the carbohydrate regime for all stereocenters of the carbohydrate ring including the anomeric linkage. Strikingly, that in case of the sugars, nature alters biochemical function by small configurational changes, whereas the configuration of the strereocentres in oligonucleotides as well as oligopeptides remains untouched in mammalian organisms.

Figure 1. Comparison of homotrimer structures, a trinucleotide (1, comprising three 2'-deoxythymidine monomers), a tripeptide (2, comprising three L-alanine monomers), and a trisaccharide (3, comprising three d-mannose momomers). The red asterisks highlight the potential for configurational variation as it is typical for the oligosaccharides, light red asterisks are highlighting different anomeric configurations, namely α or β, respectively.

Most of the complexity of the oligosaccharides is found as part of a thick layer of glycoconjugates that covers every eukaryotic cell, called the glycocalyx. The glycocalyx comprises a multitude of glycoproteins, glycolipids, GPI anchors, and proteoglycans, i.a. [2, 3]. The glycocalyx must be considered as a cell organelle that has an essential function in cellular communication. To unravel the secrets of glycocalyx function is the major task of the “glycomics”. As the “omic naming” suggests, the field of the glycosciences deals with large numbers, reminding us of the genomics and the human genome project, as well as the proteomics. While the proteome of an animal is much more difficult to define than its genome, it is even more demanding to handle the glycome of a living organism. Understanding the glycome means considering unknown functional principles and this is supported by a combination of imagination and know-how; and, additionally, it requires clear standards of nomenclature and data processing.

Carbohydrates and Communication

A modern statement about the carbohydrates is that they have a key function in cellular communication. Reams of experimental data have given us certainty that the molecular recognition processes happening at the sweet cell surface interface are essential in all cell-cell interactions. A plethora of complex glycoconjugates is participating in this sophisticated control of biological response. There is a large number of parameters that are modified to create structural diversity within this carbohydrate regime. Interestingly, tiny structural variations can make a big difference within this nano-dimensioned network of molecules, such as in the case of blood group differentiation.

Variation of configuration, anomeric linkage, ring modification, hydroxyl group masking, charge, hydrophilicity, branching, and conformational properties are the instrumentation for a fine-tuning of carbohydrate-mediated biological response. How would we ever be able to handle such a complicated matrix of function parameters? How can we deal with the size of the numbers of functional states? How can we communicate about the questions addressed in our experiments and about their results? And how would we, at all, define experiments that allow us to retrieve causalities from specific alterations?

It is essential for the advancement in the difficult field of the glycomics, to be able to learn from one another; to interconnect experimental data; to conclude from related findings. An essential prerequisite for this all is a complete as possible survey of data and a precise nomenclature to pave the ground for unequivocal communication.

Glycoconjugates and Glycomimetics

To cope with the structure-function relationships of carbohydrates, a number of different approaches can be chosen. From a synthetic point of view, carbohydrate and glycoconjugate structures can be assembled according to the natural example molecules and interrogated with a library of lectins in solution as well as on surfaces, for example [4, 5]. In another approach, however, only some specific parts of a complex natural molecule are selected and conjugated such that particular functional questions can be addressed. The resulting artificial glycoconjugates have been named “glycomimetics”, regardless of how much they resemble the nature of the original molecule. Thus, a complex glycoconjugate can experience a structural abbreviation until a structure is obtained in which only some essential aspects of the natural prototype are conserved. Such an approach allows the investigation of isolated structure-function relationships with the expectation to receive experimental data which are easy to interpret. In addition, glycomimetics are often much more facile to make than the natural examples [6–9]. Thus, it turns out that the glycomimetics offer a variety of advantages for glycomics studies (Figure 2).

☹

Resemble natural structure

☺

Ease of synthesis

☺

Easy variation of structural features

☺

Stability and biodegradibility

☺

Potential for carbohydrate drugs

☺

Easy functionalization and labelling

☺

Tool box character of structures

☺

Easy and systematic variation of multivalency

☺

Systematic study of entropic and spatial parameters

☺

Focused study of structure-function relationships

☺

Study of non-natural modifications

Figure 2. Selected advantages of glycomimetics.

Multivalent Glycomimetics

A central characteristic of carbohydrate-protein interactions is their multivalency. Multivalency of molecular interactions is an important principle in biochemistry that typically takes place in a supramolecular environment of interaction partners. The biophysical details of multivalent molecular interactions can be very different as well as their structural basis [10, 11]. Multivalency effects can lead to stronger binding as well as more specific binding, and to fine-tuning of biological response such as in the case of receptor signalling [12]. To investigate the details of multivalency effects as well as to find means to utilize multivalency in a medicinal context, for example, multivalent glycomimetics are valuable tools [6–9, 13, 14].

Typical multivalent glycomimetics are the glycodendrimers, in which a non-carbohydrate hyperbranched dendrimer core is decorated in its periphery with essential carbohydrate moieties [6]. In addition, also other multivalent molecules have been utilised as scaffolds for the preparation of multivalent glycomimetics, for example carbohydrates themselves. The latter approach has led us to the introduction of various carbohydrate-centred cluster glycosides [15–18], which have been named “octopus glycosides” as they resemble the multi-armed look of an octopus (Figure 3) [19].

Figure 3. Structural formula of a trehalose-centered cluster mannoside of the octopus type (left, 13 in Figure 5), resembling the look of an octopus as sketched on the right.

There is a favourable potential in designing multivalent glycomimetics. Firstly, their valency can be nicely modified and modulated, and in addition, they can be designed as homo-multivalent glycomimetics, bearing only one type of carbohydrate, and – more difficult to realize – multivalent glycomimetics of a mixed type can be synthesised [20]. Moreover, multivalent glycomimetics, such as the glycodendrons can include a focal point in their structures, which is suited for an orthogonal modification of the multivalent molecule (Figure 4).

Figure 4. Structures and architectural potential of multivalent glycomimetics. Glycodendrimers (left) can be constructed as homo-glycodendrimers exposing only one type of carbohydrate, whereas glycodendrimers of a mixed type carry different carbohydrates. In contrary to glycodendrimers, glycodendron molecules (right) include a functional group at the focal point (FP) of the molecule which can be utilised for immobilisaton or further functionalisation, for instance. X: Oxygen in case of glycosidic linkages, or another heteroatom or ligating moiety such as a peptide or a thiourea linkage, respectively.

Molecular Dynamics of Multivalent Glycomimetics

Multivalent glycomimetics often show pronounced multivalency effects in interaction with lectins [10, 14]. We have a long-standing experience in testing multivalent glycomimetics used as inhibitors of bacterial adhesion [21, 22]. Bacterial adhesion to the glycosylated surface of their eukaryotic target cells is mediated by long adhesive organelles called fimbriae. Important virulence factors among the enterobacteriaceae are the type 1 fimbriae, which display the lectin FimH at their tips with specificity for α-d-mannosides. Thus, we have dedicated much of our research to the design and synthesis of multivalent cluster mannosides and other mimetics of high-mannose type oligosaccharides [7, 23–27]. A key criterion for the biological function of all mannose clusters is the conformational availability of the exposed mannose residues for binding to the respective lectin carbohydrate binding sites. Thus, we have performed molecular dynamics (MD) simulations of glycoclusters and glycodendrimers to obtain an impression of their conformational features [28].

A number of 14 synthetic multivalent glycomimetics were investigated in MD simulation studies including explicit water molecules. The studied molecules fall into two groups. In one case, small, basically PAMAM (polyamidoamine)-based glycodendrimers with carbohydrate valencies between 3 and 8 have been selected (compounds 1–9, Figure 5) [29]. These expose either α-d-mannosyl or β-GlcNAc residues [30–33] which are attached to the dendritic core by thiourea-bridging. The second group comprises cluster mannosides, in which α-d-mannosyl residues were O-glycosidically linked to a pentaerythritol scaffold (compound 10, Figure 5) [34] and various carbohydrate scaffolds (octopus glycosides, compounds 11-14, Figure 5) [27], respectively.

Figure 5. Structures of the investigated glycoclusters and glycodendrimers, 1 to 9.

Figure 5. continued, structures 10 to 14.

Table 1 provides an overview about the key structural features and differences, respectively, of the multivalent glycomimetics 1–14 (Figure 5), which were computed in MD simulations.

Table 1. Key structural features of glycoclusters 1–14 (cf. Figure 5).

Scaffolded
carbohydrate unit

Linkage tpye

Scaffold
(core molecule)

Valency

1

α-d-mannosyl

thiourea

TRIS*

3

2

β-d-GlcNAc

thiourea

TRIS*

3

3

α-d-mannosyl

thiourea

PAMAM#

4

4

β-d-GlcNAc

thiourea

PAMAM#

4

5

α-d-mannosyl

thiourea

PAMAM#

6

6

β-d-GlcNAc

thiourea

PAMAM#

6

7

α-d-mannosyloxyphenyl

thiourea

PAMAM#

6

8

α-d-mannosyl

thiourea

PAMAM#

8

9

β-d-GlcNAc

thiourea

PAMAM#

8

10

α-d-mannosyl

O-glycosidic

PE+

4

11

α-d-mannosyl

O-glycosidic

glucose

5

12

α-d-mannosyl

O-glycosidic

melibiose

8

13

α-d-mannosyl

O-glycosidic

trehalose

8

14

α-d-mannosyl

O-glycosidic

raffinose

11

*TRIS: tris(aminoethyl)amine; #PAMAM: different scaffold molecules of the polymamidoamine dendrimer type; +PE: pentareythritol derivative.

For each glycocluster extreme, rather “artificial” structures and typical average conformations were determined (cf. Figure 6). Firstly, maximal elongated structures of the PAMAM-based glycodendrimers were obtained applying additional forces in the simulation. This is of interest in order to estimate the maximal possible distances between carbohydrate moieties as an extreme fit for a multivalent interaction with multiple lectin carbohydrate binding sites. Also the optimally stretched structures with the maximal possible extension of sugar residues was used to define the size of the water box required for the MD simulations. Secondly, closest packing conformations were formed during MD simulations when they were calculated in vacuo (without including explicit solvent molecules). Strikingly, the glycodendrimers adopt a completely different structure in vacuo than in water. In vacuo the structure is dominated by intramolecular interactions, leading to densely packed structures. Finally, conformations were deduced from MD simulations with explicit water molecules and only these can be regarded as realistic structures as present in water.

Figure 6. Three extreme conformations of the tetravalent PAMAM-based GlcNAc glycodendrimer 4. Top left: closest packing; bottom left: maximal elongated structure; bottom right: realistic conformation in water. Green: carbon; red: oxygen; blue: nitrogen; yellow: sulfur; (hydrogen atoms not shown). Copyright (2002), with permission from Elsevier.

The investigated multivalent glycomimetics are highly flexible molecules mainly due to their spacer moieties. The rotational barriers of the spacer bonds are sufficiently low so that many different conformations can be adopted. A number of structural features were deduced based on statistics (cf. Table 2), and evaluation of the statistics of the defined descriptors was accomplished with the conformational analysis tool (CAT) program developed by Martin Frank [35]. For example, in order to get an estimate about the size of the computed molecules, the radius of gyration was determined in each case. The Rg values of all 14 investigated glycoclusters range between 6.9 and 13.5 Å. The comparison of the two different classes of investigated glycoclusters reveals interesting differences. The Rg values of the carbohydrate-centred clusters 11-14 range from 7.1 to 9.4 indicating rather compact structures in comparison to the PAMAM-based glycodendrimers, which have a considerably larger radius of gyration (Table 2). Length of carbohydrate-equipped spacers can be estimated in each case by the mean distances between the centre of the cluster and the centre of the respective pyranose ring (Table 2). These values range from 7.5 to 19.1 Å and largely depend on the chemical nature of the spacer moieties.

Table 2. Structural properties of the investigated mutlivalent glycomimetics based on statistics.

Scaffolded
carbohydrate unit

(number of branches)

Number of bonds per branch

Rg* [Å]

Mean value

center-sugar distance [Å]+

1

α-d-mannosyl (3)

6

7.4

8.5

2

β-d-GlcNAc (3)

6

6.9

7.6

3

α-d-mannosyl (4)

10

10.6

13.1

4

β-d-GlcNAc(4)

10

11.0

12.9

5

α-d-mannosyl (6)

13

11.4

13.8

6

β-d-GlcNAc (6)

13

11.7

13.5

7

α-d-mannosyloxyphenyl (6)

18

13.5

19.1

8

α-d-mannosyl (8)

17

12.0

17.2

9

β-d-GlcNAc (8)

17

10.7

13.6

10

α-d-mannosyl (4)

6

7.1

7.5

11

α-d-mannosyl (5)

6 (7)#

7.1

7.8

12

α-d-mannosyl (8)

6 (7)#

8.7

9.4

13

α-d-mannosyl (8)

6 (7)#

8.7

9.2

14

α-d-mannosyl (11)

6 (7)#

9.4

9.7

# For the spacers branching out from C5 of the core sugar ring, the C5-C6 bond is counted in and this number is given in brackets.

* Radius of gyration.

+ Mean distances of n branches of the respective mutlivalent glycomimetic between the center of the cluster and the center of the respective pyranose ring (Man or GlcNAc, respectively).

To obtain an impression of the globular shape and dynamic properties of the investigated multivalent glycomimetics, 1000 snapshots of 1ns long MD simulations including explicit water molecules were overlaid. Three atoms in the core region of each molecule were used to orient all archived conformations in space in the same way. The centres of the pyranose rings were defined as “pseudo atoms” and a distinct colour was assigned for each pyranose moiety of the respective cluster. The calculated positions of the pseudo atoms were converted into a PDB format which can be visualised using RASMOL [36], for example. The core of each computed molecule was positioned in the middle of a cube with an edge length of 40 Å and thus, size, orientation and conformational flexibility of the investigated glycodendrimers and glycoclusters can be compared (cf. Figures 7, 8, and 9). MD simulations of the PAMAM-based glycodendrimers 1–9 led to unexpected results. It turned out that glycoclusters with higher valency do not necessarily occupy much more conformational space than smaller glycoclusters. In particular, tetravalent, hexa-, and octavalent analogues appear not to be too different from each other (Figure 7). The octavalent GlcNAc glycocluster 9 occupies even less conformational space than its hexavalent analogue 6 (cf. Table 2). This situation is, again unexpectedly, different in case of the hexa- and octavalent mannose glycoclusters 5 and 8. Cluster 8 expands further in space than 5, however, interestingly, separates one mannose residues, that is split from the core conformational space and does not interact with the rest of the molecule.

The difference between mannose and GlcNAc clusters can be explained by comparison of the exposed sugar residues. GlcNAc has six atoms more than mannose, namely an additional N-acetyl group. The water accessible surface increases from 162 Å² for α-mannose to 217 Å² for β-GlcNAc. This leads to considerable differences in the conformational behaviour of the respective glycoclusters, which can best be demonstrated by the trivalent clusters 1 and 2. Whereas the α-mannosyl-terminated compound exhibits a rather torus-like distribution of conformations, the β-GlcNAc cluster populates an almost perfect spherical distribution.

Figure 7. Accessible conformational space of PAMAM-based glycodendrimers decorated with α-d-mannosyl residues (1, 3, 5, and 8) or β-d-GlcNAc residues (2, 4, 6, and 9), respectively. 1000 snapshots from 1ns long MD simulations including explicit water molecules are overlayed in each case. The core region of each conformation is consistently fixed in space and the centers of the carbohydrate residues are displayed as differently coloured spheres. Results are displayed in 40×40 Å squares and thus the occupied conformational space can be visualized and compared. Copyright (2002), with permission from Elsevier.

Overall, it turned out that the three-dimensional shape of the investigated molecules is significantly influenced by inter- and intramolecular interactions. Often, two or more carbohydrate units may form short-lived clusters of two or more terminating sugar units. The tendency to form sugar clusters within one molecule is more pronounced in the higher branched glycodendrimers, mainly in the case of the octavalent glycodendrimers 8 and 9, as well as for the carbohydrate-centred glycoclusters 11–14 (cf. Figure 9). The possibility of establishing intramolecular interactions favours the occurrence of intramolecular sugar clusters and moreover, it generally supports the tendency to form more packed than elongated structures.

It was especially interesting to see the difference between the two hexavalent mannose clusters 5 and 7. While in case of 5 α-d-mannosyl residues are linked to the PAMAM core via thiourea bridging, in case of 7 p-(α-d-mannosyloxy)phenyl units are attached to exactly the same core via the same linkage type. In spite of this, the two clusters occupy very different conformational space (Figure 8). The conformational space that is occupied by 5 is rather globular, whereas 7 leaves out large areas in space. In the latter case, the conformational behaviour is apparently dominated by intramolecular interactions between branch pairs of the multivalent molecule, triggered by ππ interactions of the phenyl residues.

Figure 8. Accessible conformational space of PAMAM-based glycodendrimers 5 and 7 (displayed in 40×40 Å squares).The conformational availability of the p-(α-d-mannosyloxy)phenyl residues (PheMan) in the periphery of 7 is clearly restricted due to intramolecular ππ interactions. Copyright (2002), with permission from Elsevier.

The conformational features of mannose clusters 5 and 7 are reflected in their properties as inhibitors of mannose-specific bacterial adhesion. It is known that p-phenyl α-d-mannoside is a much more potent inhibitor of mannose-specific bacterial adhesion than methyl α-d-mannoside that is in the range of two orders of magnitude less potent [37]. Thus it was expected that clustering of p-(α-d-mannosyloxy)phenyl residues would lead to a very potent inhibitor of bacterial adhesion as this compound combines the favourable effects of multivalency with the high affinity displayed by the p-(α-d-mannosyloxy)phenyl unit. However, these expectations were never fulfilled [22] and this finding was only understood after the conformational properties of cluster 7 were elucidated. The MD simulations with this mannose cluster clearly demonstrated that due to the predominant intramolecular interactions ruling the conformational space of the molecule, the p-(α-d-mannosyloxy)phenyl units are not well available for lectin binding.

None of the molecules 1–14 forms an ideal sphere. The shape of the populated conformational space of the PAMAM-based glycodendrimers rather has significant aspects of an ellipsoid. However, in case of the carbohydrate-centred octopus-glycosides spherical distributions can be favoured over ellipsoidal ones (Figure 9).

Figure 9. Accessible conformational space of the PE-based cluster mannoside 10 and octopus mannosides 11–14 as displayed in 40×40 Å squares. Copyright (2002), with permission from Elsevier.

The comparison of the carbohydrate-based octopus mannosides 11–14 shows that they occupy very similar conformational space and show quite similar sizes (cf. Table 2), which is rather unexpected. In addition, regardless of how many sugar residues are branching out from the glycoclusters, MD simulations have shown that a closed outer shell of carbohydrate units does not exist. Structural similarities among the octopus glycosides are again reflected in biological testing. When evaluated as inhibitors of type 1 fimbriae-mediated bacterial adhesion to a surface of immobilized polysaccharide mannan, their inhibitory potential proved very similar, but reflecting the different valency of the various cluster glycosides not at all (Figure 10) [27].We had originally expected that the nature of the scaffolding carbohydrate would make a significant difference in the biological properties of these glycoclusters, which is not the case. MD simulations have shown that this can be understood based on the similarities of the occupied conformational space in all cases.

Figure 10. Relative inhibitory potencies (RIP) of octopus glycosides 11–14 when tested as inhibitors of type 1 fimbriae-mediated bacterial adhesion. Values are referenced to the inhibitory potency of the standard inhibitor methyl α-d-mannoside [27].

Conclusions

Multivalent glycomimetics such as glycodendrimers and octopus glycosides are valuable tools in glycobiology. They can be used to probe and manipulate multivalent interactions of carbohydrates with lectins, such as in inhibition of saccharide-specific bacterial adhesion. Typically, when the structures of multivalent glycomimetics are altered, biological consequences are expected. Often, quantitative structure-activity relationships can be deduced and understood conclusively; in other circumstances, however, this was not the case. In any case, it is essential to consider the conformational space that can be occupied by a particular multivalent glycomimetic in order to understand its interactions with multiple carbohydrate binding sites. For the highly symmetrical and flexible structures of typical hyperbranched glycomimetics, as discussed in this account, NMR analysis does not provide means for conformational analysis. Also X-ray analysis would not reveal such information. Thus, MD simulations are extremely important and useful in this important area of glycobiological research. MD simulations have allowed understanding how scaffolding units as well as the nature of spacers influence the conformational features of a respective multivalent glycomimetic. This can assist our understanding of structure-activity relationships and can eventually lead us to a better target-oriented design of multivalent lectin ligands.

Acknowledgement

I am grateful to my colleagues and co-workers for their continual contributions and support. Additionally, I acknowledge financial support by the DFG and the BMBF.

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Linking Glycomics Repositories with Data Capture

Matthew P. Campbell¹, Frédérique Lisacek²,
Marc R. Wilkins³, Pauline M. Rudd⁴, Daniel Kolarich⁶, Catherine A. Hayes⁵,
Niclas G. Karlsson⁵ and Nicolle H. Packer¹*

¹Biomolecular Frontiers Research Centre, Macquarie University, Sydney, Australia

²Proteome Informatics Group, Swiss Institute of Bioinformatics, Switzerland

³Systems Biology Initiative, University of New South Wales, Sydney, Australia

⁴NIBRT Dublin-Oxford Glycobiology Lab, University College Dublin, Ireland

⁵Medical Biochemistry, University of Gothenburg, Sweden

⁶Max Planck Institute of Colloids and Interfaces, Department of Biomolecular Systems, Berlin, Germany

E-Mail: *nicki.packer@mq.edu.au

Received: 9th December 2011/Published: 11th July 2012

Abstract

As in proteomics, the speed of the advances in glycomic discovery is dependent upon the development of a specific bioinformatic knowledgebase that links various forms of glycoanalytical data to repositories of known glycan structures. The UniCarbKB project (www.unicarbkb.org) is a partnership of leading international research groups come together in an effort to develop and provide an informatic framework for the storage of high-quality structural glycan collections including informative meta-data and annotated experimental datasets. UniCarbKB seeks to advance the integration of data capture and management within the glycomics discipline.

Introduction

About 25 years ago glycobiology began to emerge as a field focused on the functional aspects of glycans. The term Glycobiology, coined by Professor Raymond Dwek FRS (Director of the Oxford Glycobiology Institute), entered the Oxford English Dictionary in 1988, “to emphasize the importance of oligosaccharides (sugars, carbohydrates) in their biological context in the hope that this would reveal their functions”. Glycans, both in form of polysaccharides or glyco-conjugates (bound to proteins and lipids), are the most abundant class of biomolecules and are increasingly being implicated in human health [1–5].

Glycosylation is by far the most important post-translational modification in terms of the number of proteins modified and the diversity generated. This elaborate modification enhances the functional diversity of the relatively limited number of proteins translated from the genome. Since glycoproteins, glycolipids and glycan-binding proteins (also referred to as lectins, which recognise particular glycan epitopes) are mainly located on the cell's surface, they present a primary interface for a myriad of biologically significant events that can be attributed to glycan recognition. These include interactions between mammalian eggs and sperm [6], between pathogens/parasites and hosts to establish the infection process [7, 8], and as part of the immune system [9, 10].

Glycobiology is intertwined with fields such as immunology, virology, reproductive biology and drug discovery, making significant discoveries in the fields of molecular biology and in the fight against disease. Glycobiologists are exploring the many roles of sugars, discovering that different structures have essential functions in all major organ systems and are involved in the progression of many diseases [11]. These findings have led to the development of carbohydrate-based drugs and vaccines, some of which show great promise for treating or preventing various diseases, including influenza, malaria, cancer and AIDS [12–15]. Not surprisingly, considering the central role they play in molecular encounters, carbohydrate-based drugs and therapeutics represent a greater than US$ 20 billion market, including approvals of the first (preventive and therapeutic) cancer vaccines. Other examples include the glycoprotein, erythropoietin, that is used in treating kidney disease (as well as in sports drug abuse) and which is the highest biosimilar drug revenue earner in the pharmaceutical industry; and Relenza and Tamiflu that are inhibitors of the infective viral-sugar interaction and which have been used in avian and swine flu treatment.

The rapidly expanding glycoscience field is being increasingly recognised as an important component of life science research, as highlighted in a report commissioned by the European Science Foundation, which stated in its policy briefing that “to further develop diagnostic tools, preventive medicines (vaccines) and therapeutic drugs, a better understanding of glycosylated molecules is required” (Structural Medicine – The Importance of Glycomics for Health and Disease, ESF Science Policy Briefing 27; 2006). The growing importance for glycobiology in biomedical and disease research has also recently been recognised by a major 7-year (US$ 18 million) grant awarded by the US National Heart, Lung and Blood Institute (NHLBI) under its new “Program of Excellence in Glycosciences” and efforts in Japan to bring together glyco-databases through the Government sponsored MEXT Integrated Database Project. These programmes aim to translate emerging discoveries in the glycosciences to new discoveries and therapies.

An increasing number of researchers accept this importance of sugars in basic biology, however, many are concerned that the barrier for entry into this research field remains too high, potentially hampering new high-impact discovery and innovative research efforts. New technologies and systematic glycomics (the system-wide study of the entire complement of sugars of an organism) investigations churn out lists of complex data on a routine basis and the amount of information available has expanded beyond comprehension [16, 17]. Analogous to the essential function and growth of informatics in genomics and proteomics, the need to develop appropriate databases and bioinformatics tools for glycomics is obvious. There is a demand for new methods to index and retrieve information rich datasets, and software to annotate and integrate the data. Infrastructure capabilities are now required to disseminate research collections, to allow researchers to know what information is out there, as well as analytical tools to handle and interpret the volume and complexity of experimental data being produced by a myriad of different technologies.

Glycomics and Bioinformatics: Disconnected Islands

Public availability of quality curated data collections and informatics tools with which to interpret the flood of associated data has been of paramount importance in most life sciences and has become one of the foundations of modern biology. The advances in genomics (NCBI) and proteomics (UniProt) have clearly demonstrated how the existence of well-curated extensive collections of biomolecular data, together with the development of software to facilitate the mining and correlation of this data, is crucial to research advancements. In contrast to these other areas, glycomics lacks accessible, curated and comprehensive data collections and informatics tools that summarise and integrate the experimentally determined structure, interactions, characteristics and biological origin and link them with the potential function of glycans.

This lack of glycan knowledge bases and the sparseness of resources hampers the realisation of bioinformatics tools for the interpretation of experimental data and the automated determination of glycan structure; thereby limiting the possibility of large-scale glycomics studies. The complexity of the glycan structures, and the variety of techniques that are used for their study, requires a more sophisticated approach to the development of computerised tools that will have the same impact on glycomics as, for example, the Mascot protein identification tool has had on proteomics discovery.

A small international community of researchers has emerged in the last decade with the aim of building the foundations for informatics solutions in the realm of glycobiology and glycomics. A variety of databases and tools have developed in recent years to acquire, annotate and organise data collections, managed by various consortia, institutions or local groups [18–25]. The consortia, based in US (Consortium for Functional Glycomics), Europe (EuroCarbDB) and Japan (KEGG Glycan) are currently undergoing funding difficulties. The existence of these disconnected and incompatible islands of experimental data, resources and proprietary applications have been identified by glycobiologists as the single biggest hindrance to their research [26].

There is a general consensus within the community that the availability of comprehensive and up-to-date eResearch resources comprising knowledge bases supported by efficient software is a prerequisite for successfully conducting large-scale glycomics projects and interfacing them with related genomics and proteomics activities.

The Establishment of UniCarbKB to Build a Universal Knowledgebase for Glycoscience Research

Glyco-bioinformatics is slowly expanding, and benefits from the developments made in the genomics and proteomics fields, but it is in its infancy and the available informatics resources retain focus on separate methodologies and/or functionalities. UniCarbKB [27] aims to promote the translation of research by building an effective infrastructure that enables glyco-related research (Figure 1).

Figure 1. The complexity of glycomics and its relationship to genomics and proteomics, all of which require the integration of robust databases and tools to interpret the implications of glycosylation.

Research in the life sciences can only flourish if data is preserved and made accessible to all facets of the community to promote scientific progress. This is exemplified by the biological strides and technology advancements over the last decade that has provided the ‘parts lists’ for many living organisms.

The absence of a single comprehensive resource that collects research findings reported in peer-reviewed publications, along with supporting experimental data in an accessible and searchable form, acquired from a wide variety of analytical technologies, is a limiting factor in efforts to progress the field [28]. It has been readily acknowledged that this type of resource has been essential to the progress of genomics and proteomics.

The challenge for glycobiology and glycomics is to address the problem of data distribution and the fragmentation of knowledge. This can be achieved by integrating the many sources of information into a single and systematic entity, to be called UniCarbKB, and to continue efforts to advance the interpretation of captured data through the development of novel data analysis methods and algorithms for the efficient representation and mining of large experimental data sets.

The proposed international initiative will build on our previous efforts and those of our international collaborators to construct UniCarbKB [27] (Figure 2), utilising multidisciplinary/inter-sectorial integration of data to support glycoscience research and its translation to medicine and the biotechnology industry.

Figure 2. Overview of UniCarbKB: the optimistic endpoint.

The establishment of UniCarbKB will support:

Provision of an open access infrastructure for glycobiological data from common analytical strategies,

Working with international partners to enable the standardised collection, integration, and storage to promote the sharing of user-generated data,

Providing comprehensive integrated data sets, with a web-based publically accessible portal, enabling user entered data collection,

Curating and collecting published glycoanalytical data,

Negotiating with editors of discipline journals to ensure glycobiological data is placed in the publically accessible database,

Enhancing data quality through annotation features and cross-referencing with internationally recognised databases used for genomics and proteomics,

Designing software tools to enable sharing between institutions and distributed databases for glycosciences,

Supporting the development of algorithms to enable rapid and reliable automatic interpretation and mining of mass spectrometry spectra, chromatographic and electrophoretic behaviour, molecular modelling and array data.

UniCarbKB introduces three new resources that leverage existing infrastructure. Firstly, the introduction of MIRAGE, a standard focused on the analytical technologies and data interpretation issues associated with glycomics analysis. This standard will describe the information required to reproduce and process data that was used to characterize a group of structures or an individual structure. Secondly, the development of a data-exchange or remote communication protocol to enable databases, tools and informatics developers to access data collections using REST-style protocols, this will support the integration of ‘omics’ databases similar to resources distributed by UniProt [29]. Finally, the development of new algorithms and tools to facilitate the rapid and reliable processing, interpretation and annotation of analytical MS data for glycan structure determination with a high degree of automation by i) library based matching and scoring based on experimental MS spectra data sets (UniCarb-DB [30]) and ii) improved in-silico fragmentation based on features observed from library generation.

Approach

To help achieve this ambitious goal we have focused the work into a series of milestone packages and deliverables that cover software development to address technical challenges such as data storage and representation, and the interpretation of complex and large datasets.

Implementation of a federated and unified web portal to access disparate sources

A publically accessible portal will be deployed to support the integration of existing global repositories, GlycoSuiteDB [31], EUROCarbDB [19], GlycoBase [32] and UniCarb-DB [30],(Table 1) which will provide access to a large manually curated collection of glycan structures extracted from the literature together with associated experimental data (UPLC/HPLC, Mass Spectrometry).

Table 1: Currently available glycan structural databases

Carbohydrate
Database

Web address

Number of unique structures

Content

Status in 2011

Bacterial Carbohydrate Structure Database (BCSDB)

http://www.glyco.ac.ru/bcsdb3/

9,071

bacterial glycans

Last V.3 update Sep 22, 2011

EurocarbDB

http://www.ebi.ac.uk/eurocarb/home.action

13,471

glycoconjugate glycans

Last user entry Sep 7 2010

GlycoSuiteDB

http://glycosuitedb.expasy.org/ glycosuite/glycodb

3,238

glycoprotein glycans

manually curated entry from literature V.8 up to 2005

Consortium of Functional Glycomics (CFG GlycanDB)

http://www.functionalglycomics.org/ glycomics/molecule/jsp/carbohydrate/ carbMoleculeHome.jsp

7,500

synthetic and glycoconjugate glycans

Last update 2010

Kyoto Encyclopedia of Genes and Genomes (KEGG Glycan database)

http://www.genome.jp/kegg/glycan/

10,979

glycoconjugate glycans

Last update Dec 11, 2007

Based on the specifications and the descriptions of each platform used to characterise glycan structures and their occurrence in nature and biological functions, UniCarbKB will modify the database architecture, as well as user interfaces and retrieval options, to ensure that the quality of data available and functions fit the needs of research. The modality by which the users will access the services will be through a common portal that will be developed and deployed as a component on a public web site of UniCarbKB. The portal concept hides the complexity of the underlying data architecture, and the makeup of multiple data resources. Likely expansion is the addition of 3D-structures determined by molecular modelling, leveraging the Complex Carbohydrate Research Centre expertise and high-performance computing nodes accessible through GLYCAM (http://glycam.ccrc.uga.edu) and glycan interaction array data from the US Consortium of Functional Glycomics (http://www.functionalglycomics.org/glycomics/publicdata/primaryscreen.jsp) and the Australian Glycomics Institute, Griffith University.

Building and deploying structure curation and data management workflows

One of the major goals for UniCarbKB is to develop and provide a robust and easy-to-use application which would allow researchers to feed their structural and experimental data directly into the database. The technical design of the database and the evaluation of minimum reporting requirements for storing structure and meta-data/literature descriptions will lead to enhanced workflows, interfaces and an improved tool environment to manage, assign and deposit data, thus moving towards a collaborative research resource for glycomics.

The availability of such tools will lead to the creation of one single, highly curated and centralised knowledgebase to manage published carbohydrate structure information, thus removing the key stumbling block in glycobiology where information is dispersed across multiple sites.

Semi-automated workflows to capture mass spectrometry data

Mass Spectrometry (MS) is a highly sensitive method, which is frequently applied to provide information on the carbohydrate repertoire of cells, tissue and organs. MS/MS-spectra coupled with databases of glycan structures will enable the automated assignment of the carbohydrate moieties of glycoproteins. However, no comprehensive depository of experimentally determined MS spectra is available. Moreover, no standardised procedures and agreements exist on quality measures that have to be fulfilled before entering into a database.

UniCarbKB is orientated to demonstrate how the data flow of experimental data from spectrometry to the database can be done using automated procedures. It is also our aim to extend UniCarb-DB's collection [30] of MS/MS spectra by providing workflows and connecting tools used to capture and process MS data. The existence of an accepted repository for glyco-related spectroscopic data will guarantee that the loss of primary data will be considerably reduced.

Automating assignment and interpretation of mass spectrometry data

We aim to support the improvement of existing algorithms, which will enable a rapid and reliable automatic interpretation and detection of carbohydrates from the MS/MS data instead of the highly labour intensive manual annotation currently being used (Figure 3). The existence of sufficiently large high quality collections of reference spectra in the UniCarb-DB component of UniCarbKB will allow us to extend algorithms and improve capacity by incorporating library and in silico approaches – an urgent requirement in high throughput investigations.

Figure 3. Characterising MS/MS spectra …manually…. Figure courtesy of Arun Everest-Dass

Establishing and communicating exchange standards by deploying web service solutions

A RESTful web service application will provide a novel informatics remote interface to enable communication between the databases, building on the success of the international Working Group on Glycomics Data(base) Standards and the MIRAGE standards, to implement a common data exchange standard between remote structural and experimental databases created from a myriad of different technologies. It will provide enhanced search functionality, through the provision of a unique architecture that connects several international databases, whilst providing a structure for connecting future remote Research Cloud and data storage nodes.

Establish the interlink between the UniCarbKB glycan reference database and the protein databases

There have been few intensive programmes to cross-reference sugar-containing databases with those available in the genomics and protein sciences in a systems biology context. UniCarbKB will develop cross-linking strategies to establish relationships with the home of the proteomics databases, Swiss Institute of Bioinformatics (SIB) by making use of existing remote API packages, whilst opening the web services (above) to establish a new data communication pipeline.

Outcomes

Contributing, retrieving and data-mining a curated resource

These approaches will provide efficient functionalities for i) contributing structural and meta-data ii) submitting high-quality analytical data and iii) browsing and searching within the various component databases described above. The user will be able to search for all glycans associated with: (a) a particular taxonomic branch or individual species, (b) a particular organ, tissue or cell type, (c) a particular disease or pathology, (d) a chemical perturbation or drug treatment.

Delivering new bioinformatics to promote discovery

The informatics technologies and libraries will be able to be applied to screen data collections in a high-throughput manner. The bioinformatics technologies will address limitations and inaccuracies and provide a forum for comparing in silico approaches with traditional labour intensive interpretations. Access to a screening tool will assist routine data analysis, provide mechanisms to enhance the translation of discovery research, and improve capacity.

The construction and availability of the UniCarbKB resource will thus provide freely available data and bioinformatics tools to promote glycoscience and discovery research and contribute to the advancement of biology through basic investigator-driven research in bioinformatics.

Conclusions

Fifty years after the double helix, the reference DNA sequence of Homo sapiens is now available for downloading with many tools from one site centred in the USA. The National Centre for Biotechnology Information (NCBI) thus advances science and health by providing central access to genomic information. About twenty years after the start of proteomics a curated protein database and informatics tools are available at one site centred in Europe (UniProt). The mission of UniProt is to provide the scientific community with a comprehensive, high-quality and freely accessible resource of protein sequence and functional information. It may be somewhat ambitious, but we would like the same to be said one day for glycobiology, viz. that a gold standard curated database and informatics tools for glycomics are available.

As the president of Hewlett-Packard said in 1999 “The goal is to transform data into information, and information into insight”. To do this in glycobiology requires innovative approaches, a well-organised comprehensive data repository and unique software. UniCarbKB will offer the glycoscience community new enhanced tools and capabilities to search this growing mass of information, to share user-generated content, and to provide robust solutions to assist data annotation.

UniCarbKB will be an innovative glycomics knowledge platform dedicated to bridging the gap between data-capture and data interpretation to help glycobiological researchers understand and direct investigations. The creation of the knowledgebase will set the foundations for continued development to enhance the global recognition and importance of glycobiology. The key stumbling blocks impeding progress will be removed by 1) providing a freely accessible, highly curated, amalgamated database of known glycan structures and mass spectrometric experimental evidence; 2) merging curated data collections into a centralised database, that comprise manually annotated structural and experimental verified data; 3) extending and developing new databases containing information on different properties of the carbohydrates attached to proteins and lipids; 4) providing a technical framework to deploy tools and workflows to assist the interpretation of experimental data 5) hosting of the site on the established international proteomics portal, UniProt/SwissProt, to try to ensure sustainability of access.

The successful construction of UniCarbKB will also provide a leading edge and innovative approach to connect glycomics with Systems Biology, an emerging and central discipline essential to all areas of biology and medicine. A key component will be integration of glycomics data with genomic/proteomic findings in biomedical studies where glycan structures can serve as biomarkers for specific disease or malfunctions. Links will be fostered between glyco-bioinformatics and systems informatics groups that employ similar methods for genomics, proteomics, metabolomics and lipidomics research.

The UniCarbKB data acquisition consortium, at present, comprises glycoscientists at Macquarie University and University of New South Wales, Australia; University of Gothenburg, Sweden; the Swiss Institute for Bioinformatics, Switzerland; the Max Planck Institute, Germany; and the National Institute for Bioprocessing Research and Training (NIBRT), Ireland. In the future it is hoped that the partnership will expand to include quality data on all other aspects of glycomics information. Several groups (Figure 2) have already indicated their interest in being an active part of this initiative.

UniCarbKB thus is an international initiative that aims to collect and distribute resources and practices from glycobiology practitioners to the whole biological research community. The mission is to provide a comprehensive, high quality catalogue of information on carbohydrates, and to continue efforts to advance the interpretation of captured data through the development of novel data analysis methods and algorithms for the efficient representation and mining of large experimental glycomics data sets.

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Development of User-friendly, High throughput Screening for Ligands and Inhibitors of Carbohydrate Modifying Enzymes

Hans Heindl¹, Pamela Greenwell¹*, Tamas Kiss², Chris Reynolds², Daniel Farkas², Gabor Tersztyanksy² and Stephen Winter²

¹School of Life Sciences, University of Westminster, London W1W 6UW, U.K.

²Centre for Parallel Computing (CPC), School of Electronics and Computing Science, University of Westminster, London W1W 6UW, U.K.

E-Mail: *greenwp@wmin.ac.uk

Received: 6th December 2011/Published: 11th July 2012

Abstract

This paper describes the impact of cloud computing and the use of GPUs on the performance of Autodock and Gromacs respectively. Cloud computing was applicable to reducing the “tail” seen in running Autodock on desktop grids and the GPU version of Gromacs showed significant improvement over the CPU version. A large (200,000 compounds) library of small molecules, seven sialic acid analogues of the putative substrate and 8000 sugar molecules were converted into pdbqt format and used to interrogate the Trichomonas vaginalis neuraminidase using Autodock Vina. Good binding energy was noted for some of the small molecules (˜-9 kcal/mol), but the sugars bound with affinity of less than -7.6 kcal/mol. The screening of the sugar library resulted in a “top hit” with α-2,3-sialyllacto-N-fucopentaose III, a derivative of the sialyl Lewisx structure and a known substrate of the enzyme. Indeed in the top 100 hits 8 were related to this structure. A comparison of Autodock Vina and Autodock 4.2 was made for the high affinity small molecules and in some cases the results were superimposable whereas in others, the match was less good. The validation of this work will require extensive “wet lab” work to determine the utility of the workflow in the prediction of potential enzyme inhibitors.

Introduction

At the Glyco-bioinformatics meeting in 2009, we reported the development of ProSim, a user-friendly workflow running on the National Grid Service (NGS), that would enable bioscientists to: model energy minimized proteins and ligands, carry out docking experiments to investigate protein-ligand binding, use molecular dynamics programs to interrogate the stability of the docked products [1–3]. Though successful, the methodology involved blind-docking rather than targeted docking: the latter would provide us with more useful data but requires modification of the workflow and currently, manual annotation of the grid parameters. However, such modification does allow investigation into improving the computational speed of the component programs using Graphic Processor Units (GPU), clusters, desktop grids (DG) and cloud based virtual computing (VM). Additionally, we and our collaborators could make use of the component parts of the workflow in scenarios where there is a requirement to dock a number of ligands against a single protein, reducing the need for pre-processing (energy minimisation) the receptor for each experiment. This also highlighted the need for a library of energy minimized proteins and small compounds pre-processed into the relevant pdbqt files ready for docking.

At the conference noted above, we also presented our current work which aims to 1) develop user-friendly interfaces to enable biologists to easily access complex modelling and docking programs whilst still producing high-quality results, 2) increase efficiency of the programs to enable more simulations to give robust data, 3) develop a library of small ligands for high- throughput docking experiments and 4) utilise the tool in research.

Due to progress in information technology, the use of in silico methods have gained significant momentum over the last few years. Computer simulations have advantages and shortcomings compared with traditional “wet lab” methods. Due to the increased availability of high computational power, in silico methods may be much cheaper than “wet lab” experiments which use costly reagents, may require synthesis of novel chemical compounds and need highly skilled laboratory workers. A simple in silico screen used to inform the “wet lab” experiments would significantly reduce the need to purchase, at considerable cost, a full range of compounds many of which would have little further utility. Working in silico can also reduce problems of health and safety involved in the handling of potentially dangerous chemicals, cells and organisms. Using web-based interfaces is relatively inexpensive, after the initial cost of development, and the programs are simple to use, although interpretation of the results requires knowledge and experience.

Current docking and validation technology allows the docking of about 50,000 small molecules to a receptor using a 30 standard-CPU cluster within a day. This is at least comparable in speed to “wet lab” high throughput methods. The validity of docking methods as a first screen for promising ligands has been proven in the development of the neuraminidase inhibitors Oseltamivir and Zanamivir, which relied on computer-based docking results [4].

However, the validity of simulations depends on the models used to determine chemical bonds and long range interactions between atoms (electrostatic, van der Waals). Due to limitations in computer technology, the mathematical representations of chemical interactions can only be approximations. Although the equations dominating quantum mechanics cannot be solved precisely for molecular dynamics simulations, the quality of those approximations greatly influences the results obtained. In addition, in the absence of crystal structures, models are based on homology with other known structures, which in turn may introduce bias and compromise the results.

Improving Performance

The original ProSim workflow that comprised of two programs with different computing requirements, Gromacs (energy minimization and molecular dynamics) [5] and Autodock [6] (a receptor-ligand docking), was modified to give separate workflows each with user-friendly web based interfaces. As a GPU version of Gromacs was available this was trialled on a single GPU and on clusters of GPUs [7].

The Autodock program is an ideal candidate for parallelisation as the same task is undertaken thousands or tens of thousands of times and the results co-ordinated to return the best fit molecules. Porting of Autodock to the local desktop grid infrastructure of the University of Westminster, comprising of over 1600 computers, via the P-grade portal resulted in a system that required the user to set the pdbqt files from their pdb molecule files and determine the grid space producing the relevant dpf and gpf files [8]. This was accomplished using MGL tools [9]. A web based interface was then designed to facilitate the use of the Autodock program (Fig.1).

Figure 1. The Autodock workflow

One of the major drawbacks of using desktop grids (DGs) is the problem of tails, unfinished jobs at the end of the job queue that slow down completion of the workflow. To address this problem, a scheduler was constructed that detected when the jobs were entering the tail phase and then ported the remaining jobs to the Cloud using Openstack middleware. The user simply enters jobs via the portal, as previously described. Dealing with the “tail” is automatic and completely hidden from the users. When the tail is detected, jobs are replicated and Cloud jobs are instantiated automatically. The results of both cloud and desktop grid jobs are collated for return to the user (see Fig.2) [10].

Results

A comparison of the use of a GPU versus 10 nodes of a cluster of CPUs, each with 2 processors, illustrates the increase in performance obtained with a single GPU for different stages of the Gromacs program. The single GPU decreased the workflow time by about 25% (Table 1). The greatest improvement was seen in the Mdrun6 (md1.mdp) stage, the longest of the stages tested, where the run time was reduced by 28.5%.

Figure 2. A schematic of the workflow submitted to the DG and VM

Submission of 1000 jobs to Autodock running on the DG resulted in a tail that was still unfinished after 60 minutes. On detection of the tail (last 5% of jobs) the remaining jobs were replicated to the Cloud and the mean percentage decrease in job time achieved using 30 CPU cores of the UoW Openstack Nova Cloud was 38% (Table 2). However, currently the DG is a free resource whereas the use of the Cloud will impose charges, leading to choices between speed and cost. Improvements in speed themselves enable more simulations to be run in a realistic timespan improving the statistical significance and robustness of the results.

Conclusion

Cloud computing instantiation on detection of the tail significantly improved the performance of the Autodock 4 program, but the cost versus time saved would need to be considered. GPUs were also successful in improving the performance (˜25%) of the molecular dynamics simulations using Gromacs using a single GPU versus 10 nodes of a cluster of CPUs, each with 2 processors. Work is now underway to investigate the utility of clusters of GPUs.

Table 1. Computational speed using Gromacs on CPU clusters (10 nodes; 2 processors/node) and a single GPU

Job

Cluster

GPU

MDrun3 (pr.mdp)

67s

59s

Mdrun4 (nvt.mdp)

46s

38s

Mdrun5 (npt.mdp)

48s

39s

Mdrun6 (md1.mdp)

3675s

2626s

Table 2. Decreasing the tail problem by utilizing “Cloud bursting”. Percentage decrease in makespan using 30 cpu cores of the Cloud to process the tail defined as the last 5% of 1000 jobs.

Run1

Run 2

Run 3

Mean

SD

34.3

39.6

39.7

37.9

3.1

Development of a Small Molecular Library for Use in High-throughput Screening Using Autodock Vina

For the detection of novel ligands binding to a given protein in silico, there is the need to develop a library of small molecules suitable for docking experiments. This small molecule library must be in the pdbqt format, a pdb-like format with additional information on rotable bonds and on charges assigned to single atoms. This information is required to compute the energy due to internal conformational changes in the small molecule itself and the interaction between receptor and ligand. Hence we have prepared a ligand library based on the NCI database of small molecules [11]. This file (1.3 GB) was split up into sub-files (˜100 files each containing about 2500 compounds). The ligands were translated from the sdf format into pdb using OpenBabel [12, 13] and converted into pdbqt format using the prepare_ligand4.py script from MGL-Tools package (prepare_ligand4.py) [9]. The topologies of the small molecules were generated using Antechamber (Amber 11.0) and the system was solvated and the appropriate number of ions added to achieve the proper ionic strength and electroneutrality. A short (10 nsec) MD run was performed and the best 6 conformations were then ready to be used for docking.

There are constraints and requirements for the receptor files with respect to receptor size and 3D structure. It is important that the receptor (protein) is no more than 500 amino acids as, although larger receptors may be considered, molecular dynamics experiments carried out in explicit solvent can be computationally costly and the larger the molecule the more computational power required. For the docking process itself, size is not critical. A good 3-D structure of the protein in question must be available either as an X-ray crystal structure, ideally with a substrate/analogue bound to inform on the active centre, or as a structure derived from a good homology model, well informed by available crystal structures from other related proteins. The model was tested using, for example, molprobity [14] which searches for outliers in the Ramachandran plot and for clashes. An in vacuo energy minimization was performed before hydrogen atoms could be added using, for example, the MGL-tools package or alternatively leap (Amber) [15]. The model was then solvated in a water volume and appropriate numbers of Na⁺ and Cl⁻ ions added. An initial energy minimization was performed followed by a position restrained molecular dynamics (MD) run which “immobilized” the protein and allowed the solvent to fill the pockets. The model was then heated up and pressurized (nvt and npt runs) and finally a 100ns MD simulation was performed without restraining the protein. The goal was to demonstrate the stability of the protein and study possible conformational changes of the macromolecule. By performing the MD simulation the stability of the target protein and the substrate binding pocket could be assessed. Samples of this simulation (e.g. in 10ns steps) could then be used as docking targets. The resulting pdb files were then processed by MGL-Tools into pdbqt files.

In this case Autodock Vina [16] was used to replace the conventional Autodock 4.2 program previously used due to the increased speed and suitability for use in distributed computing such as a DG. The program also incorporates an easy to use scoring function, included in the results file, enabling calculations of binding free energy. Autodock Vina uses a novel docking algorithm and performs at least 100 times faster compared to classical Autodock 4.2 (AD 4.2). The authors [17] also claim a better docking quality but this needs to be confirmed.

For the program itself, no preparational autogrid run is necessary and no gpf and dpf files are needed. This simplifies the use of the tool for novice users. The only information required for the program to commence is the coordinates of the docking space centre, the size of the docking space and the receptor and ligand pdbqt files. The necessary starting files (e.g. 10–100 ligand files together with the receptor file) can be wrapped into a package and sent to the appropriate computing infrastructure. In the present case the docking was performed on our local DG. A major advantage of Autodock Vina is that it can be compiled for many different platforms and a cygwin environment, currently required to run Autodock on Windows machines, is no longer necessary. To date we have only developed this tool to run on a command line, but a web interface is in production. The current library is 200,000 small molecules (NCI) and 8000 glycans (courtesy of Prof. Dr. Thomas Lütteke, Justus-Liebig University Giessen, Germany). After the Autodock simulation, an MD run (100ns unrestrained molecular dynamics) was performed to ensure that the final molecules were robust and hence likely to exist.

Free energy calculations such as Thermodynamical Integration (TI), Free energy Perturbation (FEP), Potential of Mean Force (PMF) and Umbrella Sampling could then be made. TI is a ‘morphing’ function (lambda) between two conformational states (e.g. two different bound ligands) and ΔG is found by integration over lambda. This is determined numerically over a number of MD calculations at defined lambda intermediates and allows a relative binding free energy to be computed [18]. The slow ‘pullout’ of a ligand from a docked position may give an idea of the binding energy by integrating the force acting on the ligand integrated over time. This method would give an easy orientational measure of the binding energy of a ligand. A calibration curve could be constructed for different systems using known (measured) free energies.

With the docking and MD computations, possible ligands or inhibitors may be found. These may then be mutated by changing the side chains on the core and the results re-scored using Autodock Vina. In concept, better ‘genes’ would be inherited preferentially to the next generations and crossing over and spontaneous mutations could be integrated leading to the production of more potently binding ligands.

Current Research

For the present study an energy minimised homology model of the Trichomonas vaginalis (TV) neuraminidase (Accession: EAY07469.1) was used as a macromolecular docking target. TV is a protozoan that infects the human urinogenital tract of 180,000 million women per year and acts as a co-factor for heterosexual transmission of HIV [19]. This neuraminidase is said to be specific for α-2,3 linked sialic acids and unable to liberate α-2,6 linked sialic acid from mucin [20]. In this paper, we present the high throughput result of docking the NCI small molecule library and the 8000 molecule sugar library against the TV neuraminidase [21]. In order to validate the results it would be necessary to run a 100ns unrestrained molecular dynamics (MD) to assess the stability and possible conformational changes of the macromolecule.

Results

The ten best hits from the screening of the NCI library (200,000 small molecules) against the TV neuraminidase are shown in Figure 3. The figure shows how a terminal COOH perfectly fits into the arginine “bay” and which part of the receptor may accommodate the rest of the natural substrate. The molecules are well clustered around the active site of the neuraminidase and the affinity values obtained were between -8.4 and -9.4 kcal/mol (Fig.3).

The individual results from the small molecule docking are seen in Table3. The affinity (Kcal/mol) for the top 10 docks was between -8.4 and -9.4. Molecules 1 and 2 are highly related, one being a carbonitrile and the other an acetonitrile derivatives of 2,4,5-triphenyl-1H-pyrrole. These derivatives were better inhibitors than molecule 9, the 2,3,4-triphenyl-1H-pyrrole (-9.4 and -9.3 versus -8.4 kcal/mol). These molecules are related to those investigated by Mohamed and colleagues in 2009 [22] in a study on antimicrobial activity. Molecule 6, N-(2-chloro-4-{[(2,4-diaminoquinazolin-6-yl)methyl]amino}benzoyl) aspartic acid, has been shown to have activity against small cell lung carcinoma cell lines [23]. The RMSD was 0.00 for all ten small molecules.

Figure 3. An overlay of the 10 best fit small molecules using TV neuraminidase as target.

The ten best results of this high throughput docking were compared to a classical Autodock 4.2 experiment (100 runs each). Some of the results showed a good agreement with the results obtained using Autodock Vina, some of them were less encouraging. The docking energy values obtained from both experiments showed a good overall correlation (Table 4). To date an equivalent high throughput experiment using Autodock 4.2 is out of reach, but more parallel implementations of Autodock are in development (using GPUs) which will eventually enable the deployment, using modern graphics cards and the Cuda C language, of such an application on, for example, a desktop grid.

Table 3. The results for the “best fit” small molecules binding to TV neuraminidase

Molecule structure

Autodock result

Affinity kcal/mol

ChemSpider name Canonical SMILES code

1.

-9.4

2,4,5-triphenyl-1H-pyrrole-3-carbonitrile²²

N#Cc1c(c2ccccc2)c([nH]c1c1ccccc1)c1ccccc1

2.

-9.3

oxo(2,4,5-triphenyl-1H-pyrrol-3-yl)acetonitrile

N#CC(=O)c1c(c2ccccc2)c([nH]c1c1ccccc1)c1ccccc1

3.

-9.2

None given

c1ccc(nc1)c1c(c2ccccc2)c2n(c1c1nccc3c1cccc3)

4.

-9.1

5-hydroxy-2-phenylanthra[1,2-d][1,3]oxazole-6,11-dione

Oc1cc2oc(nc2c2c1C(=O)c1ccccc1C2=O)c1ccccc1

5.

-9.0

biphenyl-4-yl(naphthalen-1-yl)phenylmethanol

O[C@@](c1cccc2c1cccc2)(c1ccccc1)c1ccc(cc1)c1ccccc1

6.

-8.9

N-(2-chloro-4-{[(2,4-diaminoquinazolin-6-yl) methyl]amino}benzoyl)aspartic acid²³

OC(=O)C[C@@H](C(=O)O)NC(=O)c1ccc(cc1Cl)NCc1ccc2c(c1)c(N)nc(n2)N

7.

-8.7

benzyl-bis[(8-hydroxy-6-quinolyl)methyl]ammonium

Oc1cc(C[NH+](Cc2cc(O)c3c(c2)cccn3)Cc2ccccc2)cc2c1nccc2

8.

-8.6

disodium 3-oxidanylidene -4-[(2,4,5-trimethylphenyl) hydrazinylidene]naphthalene-2,7-disulfonate
CC1=CC(=C(C=C1C)NN=C2C3=C(C=C(C=C3)S(=O)(=O)[O-])C=C(C2=O)S (=O)(=O)[O-)C.[Na+].[Na+]

9.

-8.4

2,3,4-triphenyl-1H-pyrrole

c1ccc(cc1)[C@@H]1[C@H]2CN([C@@H]1c1ccccc1)[H]1C3C[C@H]2C=CC13

10.

-8.3

2-oxo-1,2-diphenylethyl 4,7-dichloroquinoline-2-carboxylate

Clc1ccc2c(c1)nc(cc2Cl)C(=O)O[C@@H](C(=O)c1ccccc1)c1ccccc1

Table 4. Overlay of the Autodock (dark coloured) and Autodock Vina (pale coloured) results for small molecules 1–10

1

2

3

4

5

6

7

8

9

10

Table 5 shows the same experiment run with a selection of selected sialyl derivatives (S1-S7). The sugar molecules were made using Glycam Biomolecular Builder (http://glycam.ccrc.uga.edu/CCRC/biombuilder/biomb_index) translated into pdbqt using prepare_ligand4.py and then docked using Autodock Vina. It is noteworthy that, overall, the binding energy was significantly lower than the binding energies of the best small molecules. The RMSD for all but α-d-Neu5Ac-(2–3)-β-d-Galp-(1–4)-α-d-Glcp-OME (3.014 and 6.447) were 0. Interestingly the beta methylated sialyllactose bound at lower energy than the alpha conformer. It is surprising that a change in the conformation that seems to be so far from the active centre of the enzyme made such a difference (1.1 kcal/ml) to the affinity.

Table 5. The results for the sialyl derivatives binding to TV neuraminidase

Docking

Affinity kcal/mol

Structure/derivative

S1

-6.8

α-d-Neu5Ac-(2–6)-β- d-Galp-(1–4)- d-Glcp

α2,6 sialyllactose

S2

-6.5

α-d-Neu5Ac-(2–6)-α-d-Glcp-(1–4)-OME

α-methylated α2,6 sialylglucose

S3

-6.6

α-d-Neu5Ac-(2–6)-β-d-Galp-(1–4)-OME

β-methylated α2,6 sialylgalactose

S4

-6.8

α-d-Neu5Ac-(2–3)-β-d-Galp-(1–4)-d-Glcp

α2,3 sialyllactose

S5

-6.7

α-d-Neu5Ac-(2–3)-β-d-Galp-(1–4)-alpha -d-Glcp-OME

α-methylated α2,3 sialyllactose

S6

-7.6

α-d-Neu5Ac-(2–3)-β-d-Galp-(1–4)-β-d-Glcp-OME

β-methylated α2,3 sialyllactose

S7

-7.2

α-d-Neu5Ac-(2–3)-β-d-Galp-(1–4)-β-d-Galp-OME

β-methylated α2,3 sialyl β galactobiose

S8

-7.4

α-d-Neu5Ac-(2–3)-β-d-Galp-(1–4) – (α – L-Fuc [1–3])- β-d-GlcNAc- β-d-Galp(1–4)- β –d-Glcp

α2,3 sialyllacto-N-fucopentaose III

The T. vaginalis neuraminidase is reported to be specific for α-2,3 sialyl linkages yet the α-2,3 and α-2,6-sialyllactoses bound with a similar affinity although there was an obvious steric hindrance where the carboxyl group was not properly positioned in the α-2,6 linked compound. The assumption must be that the α-2,6 conformer binds but is not cleaved whereas the α-2,3 is both bound and cleaved. Of course, the protein may change conformation and adapt to the ligand. A molecular dynamics investigation would provide support for the answer. Screening the sugar library provided by Thomas Lütteke revealed that a sialyl Lewisx derivative, α-2,3 sialyllacto-N-fucopentaose III (S8) bound with the lowest energy (-7.4 kcal/mol). Indeed of the top 100 hits returned, eight were related to α-2,3-sialyl Lewisx.

Discussion

In our Autodock Vina workflow, the small molecules bound significantly better than the “natural” sugar ligands. Although focused on the interactions of sialic acid binding lectins, the paper by Kiefel and von Itzstein [25] addresses the possibilities of using other sialic acid mimetics therapeutically. In theory such mimetics are more stable than complex sugars and easier to produce. Having determined that a mimetic has good characteristics, “mutations” may be made to improve the binding. The potential of producing better molecules for the inhibition of neuraminidase has been shown in the development of Relenza [24] (Zanamivir), a viral neuraminidase inhibitor. The parent sialic acid analogue, 2-deoxy-2,3-didehydro-N-acetylneuraminic acid (DANA), is 100 times less effective than the engineered molecule.

The screening of the T.vaginalis against the 8,000 sugars in the library supplied by Thomas Lüttecke revealed, as anticipated a sialyl compound, α-2,3-sialyllacto-N-fucopentaose 1 (a sialyl Lewis x containing molecule), as the best ligand. Indeed, eight of the top 100 binding molecules contained both fucose and sialic acid. Such results increase confidence in the approach. The next task is to carry out molecular dynamics on the best compounds identified and then to test the most robust on the purified enzyme in vitro to compare to the in silico results. A preliminary molecular dynamics run with the sialyl Lewisx derivative reveals a robust conformation that appears to be stabilized by the presence of the α3-fucosylated residue.

Generally the binding prediction of the much more flexible sugar molecules, especially those with more than 3 to 4 rings is much more difficult using the classical Autodock 4.2 approach since thousands of docking runs are necessary to achieve results in agreement with X-ray crystallography conformations. The difference in docking energy between the small molecules and the sugars seen in the experiments may be attributable to the bigger conformational space probed by the more than 200,000 docked small molecules, electrostatic and van der Waals interactions not found in sugars (most of the docked compounds contain aromatic rings) or simply the lack of sufficient trials to probe all the possible conformations.

To check for possible conformation changes in the receptor molecule it will be necessary to complement the docking experiments with consecutive molecular dynamics runs using Gromacs or Amber11. GPU implementations of both already exist, with the latest version of pmemd.cuda (Amber11) clearly outperforming the current GPU implementation of Gromacs [26].

Conclusion

Results from the preliminary screening of the sugar libraries are in agreement with the known specificity of the neuraminidase. The utility of the results from the screening of the small molecule library will be determined when the relevant “wet lab” work is completed. Nevertheless, we now have a user-friendly system that enables researchers to interrogate their proteins with more than 200,000 ligands generating potential targets for in vitro work.

References

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[3] Kiss, T., Greenwell, P., Heindl, H., Terstyanszky, G., Weingarten, N.(2010) Parameter sweep workflows for modelling carbohydrate recognition. J. Grid Comput. 8:587–601.
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[10] Reynolds, C.J., Winter, S., Terstyanszky, G.Z., Kiss, T., Greenwell, P., Acs, S., Kacsuk, P. (2011) Scientific workflow makespan reduction through cloud augmented desktop grids. CloudCom. In press.
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[12] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K.I., Grier, D.L. et al. (1992) Description of several chemical structure file formats used by computer programs developed at Molecular Design Limited. J. Chem. Inf. Comp. Sci. 32:244–255.
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[16] http://vina.scripps.edu/
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[17] Trot, A., Olsen, J. (2010) Autodock Vina: improving speed and accuracy of docking with a new scoring function, efficient optimization and multithreading. J. Comp. Chem. 31:455–461.
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[18] Morris, G.M., Goodsell, D.S., Halliday, R.S., Huey, R., Hart, W.E., Belew, R.K., Olson, A.J. (1998) Automated Docking Using a Lamarckian Genetic Algorithm and Empirical Binding Free Energy Function. J. Comp. Chem. 19:1639–1662.
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[19] Thurman, A.R., Doncel, G.F. (2011) Innate immunity and inflammatory response to Trichomonas vaginalis and bacterial vaginosis: relationship to HIV acquisition. Am. J. Reprod. Immunol. 65:89–98.
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[24] von Itzstein, M., Wu, W.Y., Kok, G.B., Pegg, M.S., Dyason, J.C., Jin, B., Van Phan, T., Smythe ,M.L., White, H.F., Oliver, S.W., et al. (1993) Rational design of potent sialidase-based inhibitors of influenza virus replication. Nature 363:418–23.
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*All internet sites access 22/11/2011

Expanding Knowledge on the Biological Context of Glycan Structures

Frédérique Lisacek¹*, Matthew P. Campbell² and Nicolle H. Packer²

¹Proteome Informatics Group, Swiss Institute of Bioinformatics,
Geneva, Switzerland

²Biomolecular Frontiers Research Centre, Faculty of Science,
Macquarie University, Sydney, Australia

E-Mail: *frederique.lisacek@isb-sib.ch

Received: 20th December 2011/Published: 11th July 2012

Abstract

Advance in bioinformatics has been substantially influenced by the management of biological data flows. In the most common –omics domains, such as genomics and proteomics, data quality and integration have raised questions and have solutions that can partially address the similar current predicament of glycomics data flows. Some lessons learnt from these can benefit the study of glycans at a time when high throughput data production is well underway.

Introduction

In the last thirty years, knowledge in many fields of the Life Sciences has significantly progressed with the introduction of computer resources. In fact, bioinformatics support has moved forward each time a significant data overflow has hit a domain of biology. The boom in gene sequencing in the early 1980s prompted the creation of the first sequence databases (e.g. GenBank, [1]), shortly followed by efforts to account for the concurrent increase in gene products (e.g., Swiss-Prot, [2]). Likewise, when the list of complete genomes started to grow, specialised genome databases emerged (e.g., PEDANT, [3]). Software was improved in parallel with meeting the need for aligning, searching and comparing gene sequences, which were the central object of study and hence the major type of initial data.

The evolution of proteome bioinformatics matches the same growth pattern with a slight timeframe shift. The refinement of mass spectrometers in the late 1990s data opened the gates of another data flood complementing the genomics sequence data. The high throughput production of mass spectra led to the development of corresponding databases such as the PeptideAtlas [4], along with a range of analytical informatics tools for processing mass spectra and correlating them with protein sequence information.

The recent improvement in analytical techniques used in glycobiology has now led to a rise for the need for glyco-bioinformatics. Although sugar structures are unique entities and as such require the development of specific bioinformatics resources, the extensive use of mass spectrometry in solving glycome structures bridges with proteome bioinformatics. Previous experience in designing software and databases can be built on to benefit glycobiology applications.

To begin with, lessons in data integration of genomics and proteomics information can help in creating a single virtual space in which knowledge of the structure and function of glycosylation would be organised for the support of integrated data interpretation. The integration of proteomics knowledge into UniProtKB (http://www.uniprot.org/) that followed the trend set by Swiss-Prot [2] is a definite source of inspiration for exploring and elucidating relationships between the structure(s) and function(s) of biological entities including glycans. The steadily increasing intensive use of UniProtKB unambiguously reflects a need for such types of resources.

Secondly, experience in setting standards for data storage and exchange that will facilitate subsequent data comparisons is crucial. Biological data tends to be at first generated somewhat anarchically as a consequence of multiple, and in-the-making, technologies. The stabilisation of techniques, confirmed by their reproducibility, opens the possibility of a unified representation of the data from different experimental techniques. Again, glycomics has recently reached that stage and standards can now be implemented and enforced.

Finally, no major bioinformatics progress was ever made without grounding and testing new tools in real applications. Genomics first focused on model organisms to fine-tune browsers and analytical tools. Protein-protein interactions studies that heavily rely on bioinformatics are benchmarked with yeast data. Such examples are numerous.

This paper highlights these three lessons learnt from genomic and proteomics informatic developments and investigates their benefit to glycomics as an introduction to current efforts described in other articles in this volume; the collection of data on the glycosylation of proteins (M.P. Campbell et al. Linking Glycomics Repositories with Data Capture) and the setting of exchange formats (W. York and R. Ranzinger, MIRAGE: Minimum Information Required for A Glycomics Experiment, in the same volume). The third lesson is illustrated with the first practical steps undertaken to study and interrelate the sugars mediating host-pathogen interactions.

Bioinformatics Background

Database quality assessment

The uneven content and quality of data in public databases is a central issue of bioinformatics. Fast and massive data production is often achieved to the detriment of quality. Given the significant amount of information that is automatically inferred from experimental data (e.g., sequence annotation from complete genome data), error propagation can be far-reaching. Frequent updates are intended to address the quality problem by monitoring and correcting reported errors. The frequency of update is thus one among many criteria for assessing on-line data quality. Reliability and exhaustiveness are two other criteria that are used to assess the quality of a source.

The reliability of a source often comes through usage and steadily increases with time. Internal standards (e.g., evidence tagging, normalised scores) contribute to it. Note that the selection of data sources remains a subjective task often simply geography-dependent. Some methods (e.g., [5]) are defined to rank public biological data according to reliability metadata. This kind of approach aims at guiding and assisting users undertaking bioinformatics analyses. More initiatives are under development in the context of the Semantic Web (e.g., [6]).

Exhaustiveness is a changing criterion. Exhaustiveness long meant ever-increasing size and was applied to counting objects (sequences, species, etc., 10⁶ to 10⁸ orders of magnitude). An exhaustive set is now accepted as including a relatively small number of entities (10³ or 10⁴ order of magnitude). For instance, the number of entries (genes or proteins) of a species-specific genome or proteome database is not expected to grow but information related to each entry is supposed to expand. Properties of objects are actually expected to be, if not exhaustive, at least comprehensive. Database expansion has thereby shifted from breadth to depth and from objects to properties. Collecting and bringing properties into line involves human expertise and the resulting curated data is definitely knowledge as opposed to data. Such a trend is exemplified with the recent evolution of Amos Bairoch, the founder in the 1980s of the all-encompassing Swiss-Prot database, (exponentially growing to soon reach the million landmark) to be the initiator in 2008 of the NextProt database dedicated to the ≊20,000 human gene products.

Data integration

The tasks of integration can be summarised as (i) the selection of sources from which information is extracted and pooled in one location, (ii) the quantification and interconnection of gathered information that includes the assessment of the relative importance of each piece of information, (iii) the visualisation of interconnected information, and (iv) the gradual definition and implementation of one or more underlying principles for structuring information (i.e., ontologies and models). This last task involves questions related to the chronology and dynamics of events that are the most challenging aspects of data integration.

Standards and formats

The Protein Standard initiative (PSI, http://www.psidev.info) was launched 10 years ago in the field of proteomics. It followed a similar earlier move in transcriptomics [7] where the accumulated microarray data started to be uselessly repeated in the absence of a standardised representation of what seemed to be straightforward 2-dimensional datasets. The PSI imposed a laborious exercise in harmonising input/output formats from MS instruments and analysis software, and in normalising information extraction from databases and repositories. These efforts are contemporaneous with the rising of XML in bioinformatics, as this mark-up language facilitates sharing both (semi-structured) formats and data [8]. Not surprisingly, most current proteomics standards are XML-based [9].

Exchange formats: the example of protein-protein interaction annotation

One of the recent proteomics standards governs information extraction from the literature to support curation in databases of protein-protein interactions such as IntAct (http://www.ebi.ac.uk/intact) or MINT (http://mint.bio.uniroma2.it/mint). More specifically, the Minimum Information required for reporting a Molecular Interaction experiment (MIMIx) includes the identity of molecules that participate in an interaction (with accession number), the methods by which both the interaction and the identity of the participants were established and the role of these molecules in the context of the experiment (as distinct from their biological role).

The International Molecular Exchange (IMEx) consortium of protein-protein interaction databases committed to annotating records to an agreed, published standard (http://imex.sourceforge.net/) and to exchange these records on a regular basis. Members of the IMEx consortium act both as hosts who provide the storage facilities for submitted data and as annotators of the information existing in the literature. This guarantees not only the quality but also the visibility of inter-connected resources, and emphasises the advantage of adopting data curation standards. This connectivity and subsequent advantage is made obvious in diagrams of the Pathguide portal (http://www.pathguide.org/interactions.php) that catalogues pathway and protein-protein interaction databases.

Network biology

The shift from objects to properties described in the section Data integration is contemporary with the rapid development of the network representation trend. In the last decade, high throughput techniques of all –omics fields have produced results that are, in many cases, systematically mapped as networks of interactions using Cytoscape (www.cytoscape.org), the most popular software for network visualisation and manipulation.

Where Glycomics Fits into the Picture

Data standards and databases

Several databases describing the variety and the complexity of glycan structures on glycoproteins are available online. However, these resources are either focused on describing sugars or on describing proteins in a rather mutually exclusive manner. Published literature is the only common thread available to link two complementary types of information. In fact, the exchange and cross-referencing of data between glyco-related databases is often hampered by the lack of generally accepted exchange formats and structure description standards. Conventional encoding with the IUPAC nomenclature was adopted for linear representation but machine-readable formats for 2D structures are also needed. GlycoCT [10] was proposed by a bioinformatics group headed by Willi von der Lieth at the DKFZ (German Cancer Research Centre) in Heidelberg who re-initiated the development of informatics for glycobiology after the demise of the Complex Carbohydrate Research Centre's structure database, CarbBank, in 1997. This group developed the first glycoscience portal (http://www.glycosciences.de) and was later the leader of the EUROCarbDB project (http://www.eurocarbdb.org). Despite their efforts, the GlycoCT format is not widely spread.

Despite the delay, glycomics is now following in the footsteps of other -omics fields by introducing the initiative on Minimum Information Required for A Glycomics Experiment (MIRAGE) standard this year (see contribution of W. York and R. Ranzinger, MIRAGE: Minimum Information Required for A Glycomics Experiment, in the same volume).

Data integration

Glycomics data and knowledge now is spread around in several independent resources. Two main initiatives have attempted to link complementary information. The Consortium for Functional Glycomics (CFG, http://www.functionalglycomics.org) became, in 2001, the first large, well funded project to address the need for glycoinformatics and endorsed the management and automatic annotation of experimental data generated by glycomics research. In parallel, the integration of glyco-related biological pathways by the Kyoto Encyclopaedia of Genes and Genomes (KEGG, http://www.genome.jp/kegg) and inclusion of an associated database of glycan structures, led to the establishment of connections between glycan structures and enzymatic pathways (KEGG-Glycan, http://www.genome.jp/kegg/glycan) [20]. Several other database initiatives have been started in recent years due to this increasing interest in glycomics research (see [11]).

The use of mass spectrometry in analytical glycobiology has spread significantly as illustrated in a recent review [12] and UniCarb-DB (http://unicarb-db.biomedicine.gu.se/) has recently been initiated to collect and annotate MS/MS data on released glycoprotein oligosaccharides [13]. Previous efforts to integrate this type of data with sequence data as in the PeptideAtlas [4] are not directly transposable to glycomics.

Towards network glycobiology

The popularity of network biology has reached glycobiology and a topological study of the network of N-linked glycosylation was recently published [14]. The authors carried out a preliminary investigation into the large-scale organisation of glycosylation pathways in mammalian cells in order to propose rules for glycoprotein engineering.

A logical step would rely on the CAZy database (http://www.cazy.org) to build networks from the knowledge of the range of glycoenzymes featured in CAZy. Visualisation tools will be essential in highlighting the cooperative effects of glyco-enzymatic reactions in response to environmental changes.

Practical First Steps

Existing environment

In the past eighteen months, concerted action has been initiated with the prospect of collecting and linking glyco-related data and its annotation in a single virtual space. Knowledge of the structure and function of the glycosylation of proteins will be improved in much the same way as the structure and function of proteins was facilitated by the integration of proteomics knowledge into UniProtKB (http://www.uniprot.org). A nascent consortium [15] is collecting as much data on the glycosylation of proteins that is available to each participant in an attempt to create a UniCarbKB equivalent for the integration of glycomics knowledge (see contribution of M. P. Campbell et al., Linking Glycomics Repositories with Data Capture, in the same volume). The consortium has reinforced its commitment to gathering and linking glycan information in a structure network of bioinformatics resources. A special effort is being directed toward facilitating the study of the role of sugars in infection.

Although it cannot compete with dedicated sites like GLYCOSCEINCES.de or the CFG portal, the ExPASy server (http://www.expasy.org) hosts several bioinformatics resources for glycobiologists. ExPASy was created in 1994 by members of PIG and of the Swiss-Prot group of SIB [16]. Both groups co-developed and maintained the website until 2011 when the service originally known as a proteomics portal was extended to other bioinformatics applications. ExPASy provides a fair level of integration. Many hosted resources are cross-linked allowing the navigation across several databases and/or the combined use of software with databases. This is the case for instance for the first, and now ten years old, example of a glyco-bioinformatics tool, GlycoMod (http://web.expasy.org/glycomod/), that calculates the monosaccharide composition from mass spectrometry data [17] and predicts the structures possible with this composition by linking to GlycoSuiteDB, a curated database of glycan structures reported in the literature [21].

PIG has imported onto ExPASy in 2009 this key database, GlycosuiteDB (http://glycosuitedb.expasy.org/glycosuite/glycodb) originally developed in a commercial environment (Proteome Systems Ltd), and in 2010 brought in SugarBindDB (http://sugarbind.expasy.org) in preparation to stepping up their involvement in glyco-bioinformatics. The further development of SugarBind has become, in 2011, a collaborative project between the MITRE Corporation (http://www.mitre.org) who initiated the database, the Alimentary Glycoscience Research Cluster (AGRC) in Ireland and Prof. N. Packer's Glycomics Group in Australia.

The space shared by glyco-bioinformatics resources and many other –omics resources on the ExPASy server is a unique opportunity to integrate glycomics knowledge. Note that GlycosuiteDB is already cross-referenced in UniProtKB/Swiss-Prot and vice-versa.

Support for the study of host-pathogen interactions

As hinted in the introduction, the development of resources is best achieved in the context of a selection of biological questions. A point of focus is the mediation of host-pathogen interactions by sugars.

The scenario of bacterial infection involves molecular entities of both the host and the bacterium. Each has its own set of surface sugars and surface receptors that are susceptible to interaction. Roughly speaking, bacterial lectins recognise host sugars and host lectins recognise bacterial sugars [18]. The focus of research has remained so far mainly on one side of the story that is, elucidating recognition from the point of view of the host [19] since the adhesion of a microorganism to a surface sugar was identified as often the first step in human microbial pathogenesis. In some instances the host has evolved an innate protective mechanism, which actually mimics this initial adhesion interaction to trap invading organisms in their secreted fluids in order to subsequently clear them from the body. While various genetic tools such as microarrays have been applied for studies addressing gene regulation and immunological studies in pathogenesis (e.g., [22, 23]) the molecular basis for the initial cellular contact between the target cell and the invading pathogen, known to involve one or more protein-sugar interactions, is still poorly understood. Nevertheless, the expression pattern of glycans combined with a better understanding of their functional roles is much needed. As the conserved glycan epitopes play an important role in infection, their prediction from mass spectrometry data appears like a reachable goal.

A breakdown of the specific glycan structures required for adhesion is necessary for mapping and comparing the precise sugar epitopes involved in the binding of glycoproteins to bacterial species. This data can advantageously be compared to previously published studies. Indeed, the investigation of the mediation of sugars in bacterial infection produced a wealth of knowledge that tends to be trapped in the literature. Information extraction is a long and tedious process undertaken by experienced readers. Only one database SugarBind (unpublished) collects information on host sugar epitopes and the bacterial proteins that recognise them as a result of manual curation. Note that a similar effort is undertaken in GlycoEpitope (http://www.glyco.is.ritsumei.ac.jp, unpublished) for eukaryotic proteins and that bacterial sugar structures are found in the Bacterial Carbohydrate Structure DataBase (BCSDB, http://www.glyco.ac.ru).

Table 1 summarises the range of resources available for each type of entity. It also highlights the current gaps in the knowledge that is stored in current resources and the imperative need for cross-linking them. It appears that the combination of GlycoSuiteDB (centred on the sugar structure (bacterial or eukaryotic) and the protein to which it is linked), and SugarBind, (presenting the recognition or presentation mechanism), spans the entities. The enhancement of these two resources will assist the determination of the structural specificity of glycoprotein oligosaccharides involved in adhesion.

Table 1. Summary of the range of resources available

Glycosuite

SugarBind

GlycoEpitope

BCSDB

GRAL

Lectines

Sugars on host cell surface

yes

yes

yes

Sugars on bacterial cell surface

yes

yes

Sugar epitopes

yes

yes

Host lectins

yes

yes

yes

yes

Bacterial lectins

yes

yes

Other glycoproteins

yes

yes

Evolution of SugarBind

The SugarBind database collects protein-carbohydrate binding pairs associated with initial stages of infection by human pathogens. Further development is planned and on-going data curation is provided by a trained glycobiologist. SugarBind originally listed bacterial and viral pathogens, biotoxins, and proteins associated with adhesive structures including hemagglutinins, fimbriae, pili and adhesins with associated gene names, as well as their reported glycan binding ligands. Recent enhancements include the addition of recently published material, pathogen strain designations, and new ligands. Furthermore, the cross-linking of SugarBind to GlycoSuiteDB (glycan structural data linked with protein source and tissue information) is in progress. This connects the pathogen adhesion information to the oligosaccharide structures attached to proteins and/or lipids through the IUPAC linear description of sugar structures, which is a common feature of both databases. The cross-linking of these two databases then will allow the integration of this protein-sugar interaction data into the overall curated knowledgebase of glycan information (UniCarbKB, http://unicarbkb.com, M.P. Campbell et al. Linking Glycomics Repositories with Data Capture, in the same volume) that is being assembled to facilitate the analysis of the structure and function of glycoconjugates.

The figure below describes with straight lines the cross-links already or soon to be implemented between resources. Planned links feature in dotted lines. Navigation between the different resources will support the functional interpretation of sugars.

Figure 1. Cross-links between bioinformatics resources. Full lines show existing or upcoming cross-links. Dotted lines show planned work.

Conclusion

Modern day biology has proven unfeasible without the support of bioinformatics resources given the success of high throughput analytical methods. In the specific case of glycomics, this support is lagging behind mainly due to the complexity of sugar structures and their carriers and the subsequent unknown extent of the functional role of these entities in cellular communication in general and in infection in particular. Current initiatives as described in this volume are intended to be a major step towards interconnecting isolated efforts and as such, are meant to boost faster progress.

As a first step, the molecular characterisation of microbial carbohydrate-binding proteins and oligosaccharide epitopes in infectious diseases will benefit from using bioinformatics tools that support the curation of mammalian glycan structures involved in infectious diseases and sets the basis for the prediction of antigenic sugar (sub-)structures.

Acknowledgements

We particularly thank Elaine Mullen, Leela Pavan Tadoori, Luc Mottin, Khaled Khatib and Julien Mariethoz for their contribution to the release of SugarBind in August 2011.

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Biographies

Kiyoko Aoki-Kinoshita

Apr., 2008

Associate Professor

Department of Bioinformatics, Faculty of Engineering, Soka University, Japan

Apr., 2006

Lecturer

Department of Bioinformatics, Faculty of Engineering, Soka University, Japan

Apr., 2004

Research Associate/Assistant Professor

Bioinformatics Center, Institute for Chemical Research, Kyoto University, Japan

Apr., 2003

Postdoctoral fellow

Bioinformatics Center, Institute for Chemical Research, Kyoto University, Japan

May, 2000

Senior software engineer

BioDiscovery, Inc., Los Angeles, California, USA

Jan., 2000

Postdoctoral fellow

Institute for Information Science, Academia Sinica, Taiwan

Dec., 1999

PhD in Computer Engineering

Northwestern University, USA

Sabine Flitsch

is currently Professor for Chemical Biology at the University of Manchester, after spells at the Universities of Oxford and Edinburgh. Sabine is interested in the application of peptide, glycopeptides and carbohydrate arrays for the study of enzyme-catalysed reaction, in particular glycosylations. She has established a platform in her laboratory based on SAMs on gold technology, which allows for the investigation of potential acceptor and donor substrates for glycosyltransferases and can also be used for inhibitor screening. Using MALDI-TOF mass spectrometry, such reactions can be followed in situ in a label-free manner. At the same time, enzymatic glycosylation can be used for the synthesis of glycoarrays directly on the surface with high selectivity and efficiency. Very recently, such glycoarrays were used to study clinical mutants of POMGnT, a glycosyltransferase implicated in some forms of congenital muscular dystrophies.

Hans-Joachim Gabius

born in Bad Bevensen (Germany), studied biochemistry in Hannover as a fellow of the Studienstiftung des Deutschen Volkes, obtained his MSc in 1980 and then his PhD in 1982 for chemical and biochemical studies on the proofreading mechanism of aminoacyl-tRNA synthetases under the direction of Prof. F. Cramer, Max-Planck-Institute for Experimental Medicine, Göttingen. He spent most of 1981 investigating tRNA splicing in the laboratory of J. Abelson, Dept. of Chemistry, at the UC San Diego. After starting work on glycosciences with emphasis on tumour lectinology in 1983at the Max-Planck-Institute in Göttingen, he was awarded the Otto-Hahn-Medal by the Max-Planck-Society and worked as post-doc in the group of S. H. Barondes at UC San Diego from 1984–85. Appointments as assistant professor for biochemistry in Göttingen (1987), associate professor for pharmaceutical chemistry in Marburg (1991) and as chair for physiological chemistry in Munich (1993) followed. His research interests comprise chemical, biophysical, and biochemical analysis of protein-carbohydrate recognition relevant to biological and medical fields, such as development of and glycoscientific strategies for tumour diagnosis and therapy, and elucidation of function of mammalian lectins and glycan epitopes (sugar code). He edited several books and special issues of peer-reviewed journals, e.g. Lectins and Glycobiology (1993), Glycosciences: Status and Perspectives (1997) and The Sugar Code (2008/9).

His work was prominently placed in the ranking of researchers by number of hot papers published by the Institute of Scientific Information in 1998 (http://www.the-scientist.library.upwn.edu/yr1999/june/hotresearch_p1_990621.html; please see also www.lectins.de), so far having received over 10,000 citations with an h-factor above 50.

Pamela Greenwell

is currently a principal lecturer in medical molecular biology at the University of Westminster (UoW), an MSc course leader, the co-ordinator of our Diagnostic Assessment and Intervention research cluster and a research group leader. She has 35 years of research experience in the field of molecular glycobiology and has published in excess of 80 papers. Her original research was on glycosyltransferases but she moved on to looking at organisms (eukaryotic and prokaryotic) that infect mucinous niches or in which glycans may be involved in biofilm formation.

In the last 3 years she has been involved in projects with the researchers in Grid Computing at UoW to develop easy to use interfaces for complex molecular modelling software and to speed the analysis process by porting these applications to the GRID or desktop clusters. Funding through JISC enabled the setup of the ProSim workflow, that requires minimal computer experience and this has been incorporated into both research and teaching. The workflow has been adapted to accommodate a collaborative research project from Imperial College. More recently she and her group have simplified access to Autodock, one of the components of the work flow and are now investigating cloud bursting and the use of GPUs in modelling.

Martin G. Hicks

is a member of the board of management of the Beilstein-Institut. He received an honours degree in chemistry from Keele University in 1979. There, he also obtained his PhD in 1983 studying synthetic and theoretical approaches to the photochemistry of pyridotropones under the supervision of Gurnos Jones. He then went to the University of Wuppertal as a postdoctoral fellow, where he carried out research with Walter Thiel on semi-empirical quantum chemical methods. In 1985, Martin joined the computer department of the Beilstein-Institut where he worked on the Beilstein Database project. His subsequent activities involved the development of cheminformatics tools and products in the areas of substructure searching and reaction databases.

Thereafter, he took on various roles for the Beilstein-Institut, including managing directorships of subsidiary companies and was head of the funding department 2000–2007. He joined the board of management in 2002 and his current interests and responsibilities range from organization of Beilstein Symposia with the aim of furthering interdisciplinary communication between chemistry and neighbouring scientific areas, to the publishing of Beilstein Open Access journals such as the Beilstein Journal of Organic Chemistry and the Beilstein Journal of Nanotechnology.

Carsten Kettner

studied biology at the University of Bonn and obtained his diploma at the University of Göttingen. In 1996 he joined the group of Dr. Adam Bertl at the University of Karlsruhe and successfully narrowed the gap between the biochemical and genetic properties, and the biophysical comprehension of the vacuolar proton-translocating ATP-hydrolase using the patch clamp techniques. He was awarded his Ph.D for this work in 1999. As a post-doctoral student he continued both the studies on the biophysical properties of the pump and investigated the kinetics and regulation of the dominant plasma membrane potassium channel (TOK1).

In 2000 he moved to the Beilstein-Institut to represent the biological section of the funding department. Here, he is responsible for the organization of the Beilstein symposia, research (proposals) and publication of the proceedings of the symposia. Since 2004 he coordinates the work of the STRENDA commission and promotes along with the commissioners the proposed standards of reporting enzyme data (www.strenda.org). These reporting standards have been adopted by 28 biochemical journals for their instructions for authors, and further journals will follow soon. In 2007 Carsten became involved in the development of a program for the establishment of Beilstein Endowed Chairs for Chemical Sciences and related sciences. At the same time he started a correspondence course at the Studiengemeinschaft Darmstadt (a certified service provider) where he was awarded his certificate of competence as project manager for his studies and thesis. In 2009, he took part in the establishment of a large-scale funding project in the nanoscience area called NanoBiC.

Thisbe K. Lindhorst

is full professor at the Faculty of Mathematics and Natural Science of Christiana Albertina University of Kiel since 2000. She studied chemistry at the Universities of München and Münster, received her diploma in chemistry/biochemistry in 1988 and her Ph.D. in Organic Chemistry in 1991at the University of Hamburg. After a postdoctoral stay at the University of British Columbia she worked on her habilitation and became Private Docent in 1998at the University of Hamburg. In 1997 she was a Visiting Professor at the University of Ottawa in Canada. Since 2000 she holds a chair in Organic and Biological Chemistry in Kiel. Her scientific interests are in the field of synthetic organic chemistry and in biological chemistry, especially in glycochemistry and glycobiology. Current research is focussed on the study of glycosylated surfaces. She is the author of over 100 publications and of the text book Essentials in Carbohydrate Chemistry and Biochemistry amongst others. Her most important awards are ‘Förderpreis der Karl-Ziegler-Stiftung award’, ‘Chemiepreis der Akademie der Wissenschaften zu Göttingen award’, and ‘Carl-Duisberg-Gedächtnispreis award’. Her activities and professional responsibilities comprise of positions as institute director, head of chemistry department, representative of the Gesellschaft Deutscher Chemiker (GDCh) in Kiel, board member and vice-chairwoman of Liebig-Vereinigung within GDCh and she is an elected member of the DFG Forschungsforum as well as a referee for various other programmes and scientific journals. She is an Editorial Board member of Carbohydrate Research and Scientific Editor of Carbohydrate Periodicals within the Royal Society of Chemistry.

Frédérique Lisacek

received a PhD in Computer Science (Artificial Intelligence) from the University Pierre & Marie Curie, Paris, France in 1984. From then on she has held research positions in bioinformatics in France, Japan and Australia working on knowledge representation and sequence analysis in various fields of biology. She has been involved in proteomics projects since 1999 through work with companies Proteome Systems Ltd in Sydney, Australia as a visiting researcher and Geneva Bioinformatics (GeneBio) S.A where she acted as CSO until 2005. She then joined the Swiss Institute of Bioinformatics (SIB) in the Proteome Informatics Group that she manages since 2008 and where she initiated several projects on the study of posttranslational modifications, especially glycosylation.

Nicolle H. Packer

Nicki has had an extensive and varied career in biochemical research in both Chemistry and Biological Sciences. She was part of the team that established the Australian Proteome Analysis Facility (APAF) at Macquarie University in 1996 which was at the forefront of the emergence of proteomics research. She left the University in 1999 to co-found Proteome Systems Limited, an Australian biotechnology company specialising in the development and application of proteomic and glycomic technology for diagnostics discovery, in which her group developed a platform of glycoanalytical technology and informatics tools. After 8 years she now is back at Macquarie University as a Professor and Director of the Centre of Biomolecular Frontiers, Department of Chemistry and Biomolecular Sciences. Her research interests now are in applying glycomics technology and glycoinformatic tools to understand the structure and function of glycans, particularly their role in cancer and microbial pathogenesis.

Nicki has authored 117 publications including 12 book chapters, is an editor of two books and a journal issue, holds 6 patents, is a senior editor of the Proteomics journal and is on the scientific advisory boards of two biotechnology companies. She has also succeeded in producing 3 reasonably well-balanced children...

René Ranzinger

Work

04.2009–12.2009

Software developer, Bruker Daltonics GmbH, Bremen.

Integration of GlycomeDB into Bruker MS Software

04.2005–12.2008

Software developer and server administrator, German Cancer Research Center, Heidelberg.

Java Software development in the field of carbohydrate structure databases and mass spectrometry tool. Administration of web server and databases of the department.

Academic Career

01.2010– today

Research associate, Complex Carbohydrate Research Center, Athens, Georgia.

Software and database development to assist glycomics research: SimGlycanTool-Suite, GlycomicsPortal, GlycomeDB, GlycoVault, MIRAGE

04.2005–11.2010

Dr. sc. hum., Ruprecht-Karls-Universität, Heidelberg.

Development of mass spectroscopic annotation program Glyco-Peakfinder and an integrated database (GlycomeDB) out of the mayor carbohydrate structure databases.

Doctoral thesis with the title “GlycomeDB – Integration of carbohydrate structure databases.”

09.2002–11.2004

Master of Science in Informatik, Fachhochschule Darmstadt, Darmstadt.

Study of computer science with the main focus computer graphics. Master thesis with the title “Development of a web based framework for the calculation of high resolution molecule images with a ray tracer.”

09.1996–06.2002

Diplom-Informatiker (FH), Hochschule Zittau/Görlitz (FH), Görlitz.

Study of computer science with the main focus technical computer science. German diploma thesis with the title “Development of a distributed control system for mobile robots”.

Gisbert Schneider

studied biochemistry and computer science at the Free University of Berlin, Germany, where he received his doctoral degree in 1994. After several post-doctoral research activities he joined F. Hoffmann-La Roche Pharmaceuticals in Basel, Switzerland, where he headed the cheminformatics group. In 2000, he received his habilitation and venia legendi in biochemistry and bioinformatics from the University of Freiburg, Germany. From 2002–2009 he was Full Professor of Chem- and Bioinformatics (Beilstein Endowed Chair) at Goethe-University Frankfurt, Germany; now Distinguished Adjunct Professor at Goethe-University. In 2010 he joined ETH Zürich as a Full Professor of Computer-Assisted Drug Design.

Gisbert's research activities concentrate on method development for virtual screening, molecular de novo design, and adaptive autonomous systems in drug discovery. In a trans-disciplinary approach his group amalgamates computer-based pattern recognition and machine learning with compound synthesis and biochemical activity determination.

Peter H. Seeberger

received his Vordiplom in 1989 from the Universität Erlangen-Nürnberg, where he studied chemistry as a Bavarian government fellow. In 1990 he moved as a Fulbright scholar to the University of Colorado where he earned his Ph.D. in biochemistry under the guidance of Marvin H. Caruthers in 1995. After a postdoctoral fellowship with Samuel J. Danishefsky at the Sloan-Kettering Institute for Cancer Research in New York City he became Assistant Professor at the Massachusetts Institute of Technology in January 1998 and was promoted to Firmenich Associate Professor of Chemistry with tenure in 2002. From June 2003 until January 2009 held the position of Professor for Organic Chemistry at the Swiss Federal Institute of Technology (ETH) in Zurich, Switzerland where he served as chair of the laboratory in 2008. In 2009 he assumed positions as Director at the Max-Planck Institute for Colloids and Surfaces in Potsdam and Professor at the Free University of Berlin. Since 2003 he serves as an Affiliate Professor at the Sanford-Burnham Institute in La Jolla, CA.

Professor Seeberger's research has been documented in over 250 articles in peer-reviewed journals, two books, fifteen issued patents and patent applications, more than 100 published abstracts and more than 500 invited lectures. Among other awards he received the Arthur C. Cope Young Scholar and Horace B. Isbell Awards from the American Chemical Society (2003), the Otto-Klung Weberbank Prize for Chemistry (2004), the Havinga Medal (2007), the Yoshimasa Hirata Gold Medal (2007), the Körber Prize for European Sciences (2007), the UCB-Ehrlich Award for Excellence in Medicinal Chemistry (2008), the Claude S. Hudson Award for Carbohydrate Chemistry from the American Chemical Society (2009) and the Tetrahedron Young Investigator Award for Medicinal and Bioorganic Chemistry (2010). In 2007 and 2008 he was selected among “The 100 Most Important Swiss” by the magazine “Schweizer Illustrierte”. In 2011 he received the Hans Herloff Inhoffen-Medal.

Peter H. Seeberger is the Editor-in-Chief of the Beilstein Journal of Organic Chemistry (2011 –), was the Editor of the Journal of Carbohydrate Chemistry (2003–2010) and serves on the editorial advisory boards of many other journals. He is a founding member of the board of the Tesfa-Ilg “Hope for Africa” Foundation that aims at improving health care in Ethiopia in particular by providing access to malaria vaccines and HIV treatments. He is a consultant and serves on the scientific advisory board of several companies. In 2006 he served as president of the Swiss Academy of Natural Sciences.

The research in professor Seeberger's laboratory has resulted in two spin-off companies: Ancora Pharmaceuticals (founded in 2002, Medford, USA) that is currently developing a promising malaria vaccine candidate in late preclinical trials as well as several other therapeutics based on carbohydrates and i2chem (2007, Cambridge, USA) that is commercializing microreactors for chemical applications.

Jürgen Seibel

born in 1971, studied chemistry at the Georg August University of Göttingen and received his diploma in 1998. During his PhD thesis under the guidance of Professor Dr. Dr. h.c. Lutz F. Tietze, he was involved in the synthesis of Vitamin E. In 2000, he moved to the University of Oxford to work with Professor Dr. Chris Schofield in the Dyson Perrins Laboratory (Department of Organic Chemistry) on serine proteases and Hypoxia-inducible factors (HIF) on the mechanisms and as therapeutic targets. In 2002, he came back to Germany starting his independent research at the University of Braunschweig. In 2006 he finished his habilitation. In 2007 he moved to Helmholtz Centre for Infection Research (HZI). In 2009 he became professor at the University of Würzburg. His present research interests are focused on Bioorganic Chemistry and Chemical Biology including the development of chemical and enzymatic syntheses, biocatalysis, protein engineering, drug delivery and glycosciences. In 2008 he was awarded with the Jochen-Block prize by the Dechema (Society for Chemical Engineering and Biotechnology).

Roland Stenutz

received his Ph.D. from Stockholm University in 1997. For his thesis he worked with conformational analyses using NMR and molecular mechanics and synthesis. Roland also improved a program for the interpretation of polysaccharide NMR, CASPER. As a post doc at Notre Dame, IN, he worked with Prof. A. Serianni on isotope labelling and developing Karplus curves for couplings across the glycosidic linkage. In 1999, he returned to Sweden and spent the next two years at the Swedish University of Agricultural Sciences as a researcher. At that time he started to develop the web interface for CASPER and came in contact with what was to become the EuroCarbDB network. In 2001 he got a position as associate professor at the department of organic chemistry, Stockholm University and was coordinator of the NMR workgroup of EUROCarbDB until his appointment ended in 2006.

Since 2007 Roland works at IsoSep AB, a small fine chemicals firm specializing in oligosaccharides and glycoconjugates.

W. Bruce Turnbull

received his BSc in chemistry from the University of St Andrews, Scotland, in 1995 where he subsequently undertook PhD studies with Professor Rob Field. In 1998 he moved to the University of California, Los Angeles as a Wellcome Trust International Prize Travelling Research Fellow, to work with Professor Fraser Stoddart on the design and synthesis of polysaccharide scaffolds for displaying carbohydrate ligands in multivalent arrays. In 2001 he returned to the UK to join Professor Steve Homans at the University of Leeds, where he gained an interest in using biophysical techniques to study protein-ligand interactions. Since 2005 he has held a Royal Society University Research Fellowship in the School of Chemistry at the University of Leeds. His current research interests include synthetic carbohydrate chemistry, bacterial toxins and their application in synthetic biology.

William York

received his B.A. in Molecular, Cellular, and Developmental Biology in 1978 from the University of Colorado and his PhD in 1996 from the Department of Biochemistry and Molecular Biology at the University of Georgia in Athens (UGA). He is currently an Associate Professor in that department with an adjunct appointment at the UGA Department of Computer Science. He has been involved in the development of software for representing glycan structure and extracting structural information from mass spectral and NMR data for over 20 years. He has had a strong interest in glycoinformatics since 1988, when he developed the initial core algorithms for representing and manipulating carbohydrate structures for CARBBANK, which was the first widely used database for carbohydrate structures, providing most of the primary structural information currently held in many international carbohydrate structural databases.

In collaboration with the group of Willi von der Lieth at the German Cancer Research Center, Dr. York's group developed GLYDE, the currently accepted standard for the exchange of glycan structural data. His current position as leader of the bioinformatics subgroup of the Consortium for Functional Glycomics has allowed him to organize several international efforts to develop computational infrastructure for glycan analysis, including the initiative to begin developing the Minimum Information Required for a Glycomics Experiment (MIRAGE) standard.

Dr. York has published over 95 articles for peer-reviewed journals and has made numerous presentations to academic and learned societies.

Dr. York's research is supported by the National Institutes of Health, the National Science Foundation, and the U.S. Department of Energy.

Conference Photo