The post-''omic'' era can be characterized by investigations of dynamic processes within and between cells, tissues and organs. Such investigations are carried out using a combination of interdisciplinary procedures at both the theoretical and experimental level. One aspect of intracellular dynamics is the determination of complex metabolic networks and their high dynamic behavior, and their associated mechanistic pathways. Continuous technical and methodological advances and improvements have meant that biochemical pathway analysis can now be carried out in much greater depth and with increased efficiency and accuracy.

Unfortunately, such progress has led to a confusing and highly undesirable situation with respect to trying to make the maximum use of the experimental data derived from the functional characterizations of enzymes since a variety of experimental designs and analytical methods have been employed. The result is that there is a lack of systematic collections of comparable functional enzyme data. The pre-requisite for both comparability and reliability of such data is the provision of minimum information about experimental design and experimental results as well as standardization of the conditions and procedures involved in the experiments.

However, the current position is not encouraging: the quality of reported experimental data of enzymes is insufficient for the needs of systems level investigations and thus is, in point of fact, neither applicable for modeling and simulation nor for the functional characterization of the individual cellular components. Consequently, a high quality level balance between experimental in-put data and modeled out-put data needs to be created.

The STRENDA Commission (STRENDA stands for “Standards for Reporting Enzymology Data”), founded in 2003, is concerned with the improvement of the quality of reporting functional enzyme data to support, inter alia, enzyme kinetics for application in the in silico investigation of biological systems.

The STRENDA Commission has developed a set of guidelines for the reporting of data in publications. These guidelines along with the recommendations of a number of other groups that are also concerned with the standardization of reporting and experimental procedures (www.mibbi.org) are intended to pave the way to Good Publication Practice to ensure data quality and data identification.

This 4th ESCEC Symposium, organized by the Beilstein-Institut together with the STRENDA Commission, provided a platform to discuss the checklists, to consider further suggestions and to improve the existing recommendations. The STRENDA group took also the opportunity to discuss about the presented STRENDA electronic data capturing tool with members from diverse standardizations initiatives and systems biology groups such as MIBBI, YSBN, EFB and SYSMO, editorial board members from journals and all participating experimentalists and theoreticians. The focus of this presentation was to find answers on the questions such as how to organize and store these massive data sets in standard and easily accessible forms, which new experimental tools have to be developed to gather and configure such data into interactive models, which parameters should be measured, what kind of data constitute the minimum required information, and which experimental conditions should be recommended. 

We would like to thank particularly the authors who provided us with written versions of the papers that they presented. Special thanks go 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 valuable and fruitful event.

Frankfurt/Main, February 2010

Carsten Kettner
Martin G. Hicks

The Vicinal Oxygen Chelate (VOC) superfamily embodies a functionally diverse set of enzymes that catalyze both acid-base and electron transfer chemistries. A subset of these enzymes is known to confer microbial resistance to the antibiotic fosfomycin by three different mechanisms. The resistance proteins FosA (a glutathione transferase), FosB (a thiol transferase) and FosX (an epoxide hydrolase) are found in both Gram-negative and Gram-positive pathogenic microorganisms. These proteins have been proposed to be evolutionarily related to a catalytically promiscuous progenitor (FosX_Ml) encoded in a phn operon in Mesorhizobium loti. We recently reported that more robust FosA activity could be evolved by homologous recombination experiments with a FosA gene and the gene encoding the promiscuous FosX_Ml. This report is incorrect. The ‘‘evolved’’ proteins that were characterized appear not to be the result of homologous recombination but rather due to random mutations in a mutant gene that contaminated the original recombination experiments. This paper first summarizes what is known about the evolutionary relationships among these proteins and then points to new lines of investigation, particularly with respect to Gram-positive microorganisms.

The elucidation of the substrate profiles for enzymes of unknown function is a difficult and demanding problem. A general approach to this problem combines bioinformatics and operon context, computational docking to X-ray crystal structures, and the utilization of focused chemical libraries. These methods have been applied to the identification of novel substrates for enzymes of unknown function within the amidohydrolase superfamily. Operon context and X-ray crystallography was utilized in the identification of N-formimino-L-glutamate as the substrate for Pa5105 from Pseudomonas aeruginosa and D-galacturonate for Bh0493 from Bacillus halodurans. Focused substrate libraries were used to identify N-acetyl-D-glutamate as the substrate for Bb3285 from Bordetella bronchiseptica and L-Xaa-L-Arg/Lys as the substrate for Cc2672 from Caulobacter crescentus. Computational docking of potential high energy intermediates was used to determine that Tm0936 from Thermotoga maritima catalyzed the deamination of S-adenosyl homocysteine.

The U.S. National Institutes of Health Chemical Genomics Center (NCGC) has established a new screening paradigm, quantitative high-throughput screening (qHTS), wherein concentration-response curves (CRCs) are rapidly recorded on large compound collections (> 300,000). The data is automatically fit to the Hill equation and the CRCs are subjected to a classification scheme. This approach reduces false positive and negative rates compared to the traditional screening approaches where only a single concentration is tested and provides a pharmacological database that can be used to construct large-scale bioactivity profiles. We demonstrate how this approach was used to examine a coupled enzyme assay where the production of ATP by human pyruvate kinase M2 (PykM2) was coupled to the ATP-dependent bioluminescent enzyme, firefly luciferase (FLuc), to produce a luminescent signal. This identified chemical probes which specifically activate PykM2 while also providing a bioactivity profile of FLuc inhibitors. Examining the latter uncovered a counterintuitive phenomenon of great importance to compound discovery efforts wherein FLuc inhibitors specifically produce a non-specific luminescent response in cell-based assays.

Isoprenoids are the set of ~25,000 unique compounds based on the ubiquitous C₅ donor isopentenyl diphosphate (IPP), including quinones, steroid hormones, bile acids, protein membrane anchors and secondary metabolites. Streptococci and other gram-positive bacteria produce IPP via the mevalonate pathway, whose function is required for the respiratory pathogen Streptococcus pneumoniae to survive in lung and serum. With the discovery of potent selective feedback inhibition by the metabolite diphosphomevalonate (DPM), our laboratory has positioned the pneumococcal mevalonate pathway as a novel target for clinical intervention against an organism that claims the lives of over 4000 people daily. Our studies have revealed unique features of each of each of the three GHMP family kinases that comprise the pathway, including potent allosteric inhibition, a catalytic switch, and a concerted elimination mechanism-informing the design of antibiotics that can simultaneously inhibit multiple steps in a single pathway.

Estrogen sulfotransferase (EST) regulates the biological activity of estradiol – sulfation of this important signalling molecule inactivates its estrogen-receptor-binding activity. EST is a cytosolic sulfotransferase – one of family of structurally well conserved enzymes that exhibit broad, overlapping substrate specificities and that together comprise a robust catalytic network designed to handle the many substrates present in the cell. We have discovered recently that EST is a half-site enzyme – only one of the subunits of the dimer is capable of forming product during turnover. Thus, during the catalytic cycle a molecular ‘‘decision’’ is made that couples the silencing one of the subunits to the activity of the other. The discovery of this remarkable behaviour and the implications of the EST mechanism for the sulfotransferase field are the subject of this article.

The evolution of new catalytic activities and specificities within an enzyme superfamily requires the exploration of sequence space for adaptation to a new substrate with retention of those elements required to stabilize key intermediates/transition states as well as the core enzyme fold. Phylogenetic analysis, mechanistic information, and structure determination are used to reveal novel ways in which the catalytic scaffold of a mechanistically diverse superfamily, the haloalkanoic acid dehalogenase enzyme superfamily, is tailored to new biochemical functions. Newly uncovered substrate specificities and activities in members of the superfamily are highlighted to explore the interplay of function and form. We provide evidence that core residues in this large enzyme family, form a ‘‘mold’’ in which the trigonal bipyramidal transition-states formed during phosphoryl transfer are stabilized by electrostatic forces.

Protein variants, which vary in their exact chemical composition, and which are coded by one gene or by a paralogous or orthologous gene or alleles of that gene, are called protein species. The term protein species covers splicing variants, truncated proteins and post-translational modified proteins, and is defined chemically in contrast to the term isoform, which is defined genetically. The impact of the knowledge of the exact chemical composition of a protein species is determined by the relationship between its composition and its function. Since centuries it is known that post-translational modifications such as phosphorylation critically determine the activity status of enzymes. Proteolytic truncations can activate proteases, peptide hormones or receptors. However, despite of this knowledge, the relationship between the exact chemical composition of a protein and its function is not sufficiently considered in many protein investigations. In many of the current proteomics studies protein identification is based on sequence coverage significantly lower than 100%. Post-translational modifications are more or less ignored. A second drawback concerning the comprehensive description of protein species derives from the absence of an identifier system, which describes their exact chemical composition. Therefore, up to now we have to deal with a huge ambiguity concerning the identity of a protein and its function. In the past, functions were assigned to genes, implicating that the full information for the function is encoded in the DNA sequence. Now it becomes obvious that both different modifications and different combinations result in different protein species with different functions. An identifier system for protein species allows the assignment of a defined function to a defined protein species, which is determined by its exact chemical composition. The protein species identifier system was introduced in 2009 by Schlüter et al. and is presented here.

Spectroscopic methods are often used to follow the course of enzymecatalysed reactions. UV/visible spectrophotometry is the most common, but a wide range of other spectroscopic techniques, including infrared and nuclear magnetic resonance, as well as mass spectrometry, are in use. Spectroscopy and spectrometry are also used in the characterization of the enzymes themselves, and in the identification and quantification of substrates and cofactors. Hitherto, there has been no formal requirement to archive original spectroscopic data, as there is for protein structures and gene sequences. However, the funding agencies increasingly expect grantees to have policies on data sharing, and to deposit all types of experimental data. Spectroscopic data are now conveniently acquired in digital form, but apart from printed documents, there are no universally accepted formats for data storage. Spectra are produced by proprietary software written by instrument manufacturers to run their own instruments. Data formats are nonstandard and may be difficult to read directly. Standard, vendor-neutral data formats have been established for certain types of spectroscopy, such as JCAMP-DX (from the Joint Committee on Atomic and Molecular Physical Data eXchange) for several types of spectroscopy, including infrared and NMR. The details of each format necessarily depend on the type of spectroscopy. There are parallel developments of criteria for meta-data and data validation. Standard formats will facilitate the use of electronic notebooks. The extension of these formats to different types of spectroscopy and spectrometry will facilitate their linkage to other chemical information such as molecular structure. ASCII formats such as JCAMP-DX or, more recently, XML formats such as CML (Chemical Markup Language) satisfy the data-storage requirements. They are readable by generic, open-source software. The routine deposition of spectra in electronic repositories (databanks) will benefit the biochemical community by making them available for further analysis and data mining.

The stability of a protein’s native state has important implications for its folding dynamics, function, and evolution. Here we report on a study investigating general relationships between sequence- and structure-based properties of a protein and its empirically determined stability (as measured by melting temperature experiments). Surprisingly, we find that contact density – a sequence-independent measure of protein compactness – is not significantly correlated with protein melting temperature; this property has been previously implicated as a correlate of protein stability in theoretical and evolutionary analyses. After incorporating residue type in the definition of residue-residue contacts, we find that increasing the fraction of hydrophobic contacts in a protein tends to raise melting temperature, consistent with a stabilizing effect, while increasing the fraction of repulsive charge contacts results in a marginally significant decrease in melting temperature, consistent with a destabilizing effect. Our work demonstrates that subtle sequence variation may be an important factor in fine-tuning the stability of a protein fold.

Since isoenzymes were first discovered, their physiological role has generated interest and discussion, and study of the flux distribution between isoenzymes in a real pathway, studied with real parameters, should shed light on this role. The aspartate-derived amino acid pathway from plants constitutes an excellent system for understanding the role of isoenzymes, as well as the effects of regulatory mechanisms such as feedback inhibition and allosteric interactions, because there are several branch-points, numerous isoenzymes, and different allosteric control mechanisms (inhibition, activation, antagonism and synergism). It is responsible for the distribution of the carbon flux from aspartate into the branches for synthesis of lysine, threonine, methionine and isoleucine. A mathematical model of the core of the pathway in the chloroplasts of Arabidopsis thaliana was constructed, and as kinetic data from the literature are often inadequate for kinetic modelling, we combined kinetic measurements obtained in vitro with purified enzymes, in near-physiological conditions, with in vitro reconstitution and numerical simulation. The model accurately predicts the experimentally observed behaviour, and shows that the isoenzymes contribute unequally to the flux and its regulation. The effects of some isoenzymes knockouts are also studied.

This contribution illustrates a framework for reconstructing and verifying large-scale kinetic metabolic network models in a semi-automated way. The experimental basis of this approach is provided by a stimulus response experiment. Parameterizing a large-scale kinetic network model then consists of three steps. In a first step, metabolic fluxes are identified. Subsequently, the dynamic network model is set-up automatically using canonical enzyme kinetics. In a third step, the kinetic parameters of the model are estimated from time-series metabolite data through integrating an evolutionary algorithm with a highperformance dynamic simulation platform. In this study, the time-series metabolite data were collected from HepG2 cells and analysed in the range of 0 to 180 minutes after depriving glucose from the culture medium. In total, more than 6 million simulation runs were performed. The in silico metabolite dynamics were in accordance with the experimental data.

Systems biology aims to understand the complex dynamics of biochemical reaction networks by an interdisciplinary approach of mathematical modelling and quantitative cell biology. Stimulating an intracellular signalling pathway by an extracellular ligand leads to temporal changes of intracellular protein concentrations. These temporal changes are caused by nonlinear processes, including protein complex formation, enzyme catalyzed reactions and feedback regulation. Thus simple measurements of experimental time courses for protein concentrations or measurements of reaction rate constants alone are not sufficient to understand the dynamics of a biochemical network. Mathematical models, describing the temporal response of network in response to systematic perturbations of other components are needed to unravel the nonlinear dynamics of biochemical networks. [...] An important application of medical systems biology is to understand disease mechanisms and the response of cells to drugs in order to support the development of therapies. Computer simulations enable quantitative predictions of sRNAi experiments and the design of stimulus-profiles leading to a desired temporal response of a pathway target. But only recently, feedback to the wet lab in the form of quantitative predictions of pathway dynamics and supporting the design of experiments is taking place as we demonstrate in our work.

We present the framework of generalised supply-demand analysis of a kinetic model of a cellular system, which can be applied to networks of arbitrary complexity. By fixing the concentrations of each of the variable species in turn and varying them in a parameter scan, rate characteristics of supply-demand are constructed around each of these species. The shapes of the rate characteristic patterns and the magnitude of the flux-response coefficients of the supply and demand blocks, as compared to the elasticities of the enzymes that interact directly with the fixed metabolite, allow for identification of regulatory metabolites in the system. The analysis provides information not only on whether and where the system is functionally differentiated, but also on the degree to which of its species are homeostatically buffered. The novelty in our method lies in the fact it is unbiased, supplying an entry point for the further analysis and detailed characterisation of large models of cellular systems, in which the choice of metabolite around which to perform a supply-demand analysis is not always obvious. The method is exemplified with two kinetic models from the published scientific literature.

Usually enzyme kinetic parameters are measured under assay conditions that are optimized for a high activity of the enzyme of interest. The aim of this study was to test if the predictive value of a kinetic computer model of yeast glycolysis would be improved by using kinetic parameters measured in a standardized in vivo-like assay medium. The V_max and some kinetic parameters of the glycolytic and fermentative enzymes were measured in Saccharomyces cerevisiae grown in an aerobic, glucose-limited culture. The assays were done both under ‘in-vivo-like’ and optimized conditions. The new data were implemented in an adapted version of the glycolysis model of Teusink et al. The ‘in-vivo-like’ enzyme kinetic parameters improved the model substantially as compared to the parameters from optimized assays. In the latter case the model exhibited ‘turbo’ behaviour, characterized by a dramatic accumulation of hexose phosphates. The in vivo-like kinetic parameters improved the balance between the lower and upper branch of glycolysis and resulted in a better correspondence between model and experiment for both the concentrations of the glycolytic intermediates and the fluxes.

Modern biological science addresses a variety of subjects using an array of analytical techniques. Few relations between subject and technique are exclusive, making for a very large number of potential workflows, combinatorially-speaking. While this diversity is to be celebrated, it presents informatics challenges that require resolution if the data-sharing ambitions of many funders are to be realised, and the consequent benefits to science obtained. There is increasingly-organised movement towards consensus on data and reporting standards for the biosciences, but significant hurdles remain: scientists must be  convinced of the value of the exercise, and user-friendly, time-efficient and robust tools are required. The ‘Minimum Information for Biological and Biomedical Investigations’ (MIBBI) Project, which promotes and develops guidance on the content that experimental reports should contain, is dependent on progress in both these areas.

Applied biocatalysis is the general term for the transformation of natural and non-natural compounds by enzymes for preparative purposes. Because of this, the term biocatalysis is also used to refer to the application of enzymes in chemistry. There is a steadily rising number of publications reporting the use of biocatalysis. Unfortunately, the value of many of these publications is limited because essential information about the experiments is not presented. Recently, the scientific committee of the European Federation of Biotechnology Section on Applied Biocatalysis (ESAB), taking also inspiration from the STRENDA guidelines, prepared and published guidelines for the correct reporting of experiments in biocatalysis. The present manuscript would like to draw attention to some specific relevant experimental issues, which differentiate applied biocatalysis from fundamental enzymology and deserve particular methodological consideration.

SABIO-RK is a curated database containing kinetic information not only for biochemical reactions but also for individual steps of the reaction mechanism manually extracted from literature. Data in SABIO-RK comprises information about reaction participants including enzyme properties, biological locations (organism, tissue etc.), kinetic parameters and rate equations determined for the reaction, and the experimental conditions used for the determination. To understand biochemical reactions and their kinetic behaviour not only reaction details and kinetic properties of the biochemical reactions but also details of the reaction mechanism are essential. To meet these requirements additionally to the kinetic data of the reactions SABIO-RK offers a graphical representation of the mechanism of a selected reaction as a survey and also a detailed listing of all individual reaction steps as separate entries.

Both organic and inorganic chemistry have benefited greatly from the availability of tables of standard enthalpies of formation Δ_fH⁰, standard Gibbs energies of formation Δ_fG⁰, and standard entropies S⁰. These tables of standard thermodynamic properties allow the user to calculate values of enthalpy changes Δ_rH⁰, Gibbs energy changes Δ_rG⁰, equilibrium constants K, and entropy changes Δ_rS⁰for any reaction in which these standard thermodynamic properties are known for all of the reactants and products. Thus, it is not necessary that actual measurements have been performed on the reaction of interest. While several tables of standard thermodynamic properties have been prepared for biochemical substances, they are not as extensive as the corresponding tables for organic and inorganic substances or as extensive as they might be if all of the available experimental results in the literature had been utilized. Nevertheless, comprehensive tables could be produced by utilizing all of the data {apparent equilibrium constants K’ and calorimetrically determined enthalpies of reaction Δ_rH(cal)} in the Thermodynamics of Enzyme-catalyzed Reactions Database together with related property values such as standard enthalpies of combustion, entropies and heat capacities, solubilities, enthalpies of solution, pKs, and enthalpies of binding for the substances of interest. This large set of property values can be used to establish a thermodynamic network, i. e., a system of linear equations that can be solved for the desired formation properties. Such an undertaking requires extensive literature work, a substantial amount of analysis and computation on the results of the individual studies, and a careful fitting together of the property values by means of a judicious weighting of the property values. It can be viewed as a very large ‘‘jig-saw puzzle’’ of information. But the proper construction of such a network would serve to bring together a large body of related property values and would be of immense practical value to the scientific community. [...] The aim of this chapter is to briefly describe the use of these tables, how they are prepared, the current status of existing tables that pertain to biochemical reactions, and to provide a vision of what is possible.