BRENDA, the enzyme database: updates and major new developments

Schomburg, Ida; Chang, Antje; Ebeling, Christian; Gremse, Marion; Heldt, C; Huhn, Gregor; Schomburg, Dietmar

doi:10.1093/nar/gkh081

Cited by 800 publications

(512 citation statements)

References 11 publications

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“…The ATP binding sites were clustered into 19 functional classes based on the enzyme classifi cation in the BRENDA database. 46,47 Similarly, the PLP binding sites were clustered into 20 functional classes. The ATP binding sites were compared with each other for a total of 30,976 comparisons.…”

Section: Validation Of Cpassmentioning

confidence: 99%

Comparison of protein active site structures for functional annotation of proteins and drug design

et al. 2006

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Rapid and accurate functional assignment of novel proteins is increasing in importance, given the completion of numerous genome sequencing projects and the vastly expanding list of unannotated proteins. Traditionally, global primary‐sequence and structure comparisons have been used to determine putative function. These approaches, however, do not emphasize similarities in active site configurations that are fundamental to a protein's activity and highly conserved relative to the global and more variable structural features. The Comparison of Protein Active Site Structures (CPASS) database and software enable the comparison of experimentally identified ligand‐binding sites to infer biological function and aid in drug discovery. The CPASS database comprises the ligand‐defined active sites identified in the protein data bank, where the CPASS program compares these ligand‐defined active sites to determine sequence and structural similarity without maintaining sequence connectivity. CPASS will compare any set of ligand‐defined protein active sites, irrespective of the identity of the bound ligand. Proteins 2006. © 2006 Wiley‐Liss, Inc.

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Section: Validation Of Cpassmentioning

confidence: 99%

Comparison of protein active site structures for functional annotation of proteins and drug design

et al. 2006

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“…We obtained the protein structures from the Protein Data Bank [2] and their corresponding enzyme class labels from the BRENDA enzyme database [20]. We randomly choose 90 proteins from each of the 6 enzyme EC hierarchy top level classes.…”

Section: Experimental Settingmentioning

confidence: 99%

Shortest-Path Kernels on Graphs

Borgwardt

Kriegel

Fifth IEEE International Conference on Data Mining (ICDM'05)

635

543

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Data mining algorithms are facing the challenge to deal with an increasing number of complex objects. For graph data, a whole toolbox of data mining algorithms becomes available by defining a kernel function on instances of graphs. Graph kernels based on walks, subtrees and cycles in graphs have been proposed so far. As a general problem, these kernels are either computationally expensive or limited in their expressiveness. We try to overcome this problem by defining expressive graph kernels which are based on paths. As the computation of all paths and longest paths in a graph is NP-hard, we propose graph kernels based on shortest paths. These kernels are computable in polynomial time, retain expressivity and are still positive definite. In experiments on classification of graph models of proteins, our shortest-path kernels show significantly higher classification accuracy than walk-based kernels.

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“…Their description comprises two parts: the stoichiometric structure and the individual reaction kinetics. Extensive data on network structures have been collected in databases [19 -22], whereas quantitative knowledge about reaction kinetics [23] is less detailed, and kinetic parameters can also vary strongly between experiments.…”

Section: Parameter Distributionsmentioning

confidence: 99%

“…Uncertainty can arise for different reasons (i) a parameter value is known, but with certain measurement errors (ii) a value is known from a different experiment, and so biological variability will lead to uncertainty: for instance, enzyme activities are actively adapted to the demands of the cell by differential gene expression; in particular, parameters will vary within a population of cells (iii) a parameter value is unknown, but a rough guess can be made from the range of known parameter values of the same type. For instance, inhibition constants in the Brenda database [23] typically range between 10 26 and 100 mM, K M values lie between 10 24 and 100 mM, and turnover numbers vary between 0.01 and 10 6 min 21 .…”

Section: Parameter Distributionsmentioning

confidence: 99%

“…3 shows histograms of parameters, fluxes and concentrations from 1000 simulation runs: solid lines denote the corresponding probability densities, which are exact for the parameters and computed by second-order expansion (see Section 3.1) for fluxes and concentrations. The distributions of fluxes were computed numerically from the log-normal distribution of forward and backward fluxes using (23). The probabilities of flux directions are shown in Fig.…”

Section: Steady States Of a Small Networkmentioning

confidence: 99%

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Biochemical networks with uncertain parameters

Liebermeister

Klipp

2005

IEE Proc. Syst. Biol.

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The modelling of biochemical networks becomes delicate if kinetic parameters are varying, uncertain or unknown. Facing this situation, we quantify uncertain knowledge or beliefs about parameters by probability distributions. We show how parameter distributions can be used to infer probabilistic statements about dynamic network properties, such as steady-state fluxes and concentrations, signal characteristics or control coefficients. The parameter distributions can also serve as priors in Bayesian statistical analysis. We propose a graphical scheme, the 'dependence graph', to bring out known dependencies between parameters, for instance, due to the equilibrium constants. If a parameter distribution is narrow, the resulting distribution of the variables can be computed by expanding them around a set of mean parameter values. We compute the distributions of concentrations, fluxes and probabilities for qualitative variables such as flux directions. The probabilistic framework allows the study of metabolic correlations, and it provides simple measures of variability and stochastic sensitivity. It also shows clearly how the variability of biological systems is related to the metabolic response coefficients. IntroductionCell simulations aspired to in systems biology [1] require knowledge of enzyme kinetic parameters. However, owing to a lack of measurements, measurement errors and biological variability, most of these parameters are still unknown or uncertain, which turns out to be a major obstacle in large-scale cell modelling. In this situation, a probabilistic description of the parameters can be helpful: to assess the effects of measurement errors, to find out which model results persist for a wide range of parameters, and to derive probabilities for different model outcomes. Besides this, parameter distributions can also be employed to study the natural variability and robustness of biological systems. The effects of temporal random fluctuations in gene expression [2,3] and metabolism [4,5] have been studied. Here, we focus on models with static yet uncertain parameters: the parameters are described by a probability distribution, and the standard deviation of the resulting variable distributions (their variability) reflects how strongly the variables respond to parameter variations. (In this paper, we use the term 'variable' quite generally for quantitative model results, such as concentrations or fluxes in steady state or as functions of time, or functions of them, such as signal amplitudes or durations [6].) Of course, this influence depends on the system, on the variable of interest and on the parameter: at bifurcation points, a small parameter change can even change the qualitative dynamic behaviour. In other cases, parameters can have a weak influence on the system behaviour. In fact, various biological systems seem to have evolved robustness, that is, low sensitivity and thus low variability, against a typical amount of parameter variation (see [7] and references therein) [8 -10].If the parameter v...

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BRENDA, the enzyme database: updates and major new developments

Cited by 800 publications

References 11 publications

Comparison of protein active site structures for functional annotation of proteins and drug design

Comparison of protein active site structures for functional annotation of proteins and drug design

Shortest-Path Kernels on Graphs

Biochemical networks with uncertain parameters

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