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Koji Tsuda - One of the best experts on this subject based on the ideXlab platform.
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Reaction graph kernels predict EC numbers of unknown Enzymatic Reactions in plant secondary metabolism.
BMC bioinformatics, 2010Co-Authors: Hiroto Saigo, Masahiro Hattori, Hisashi Kashima, Koji TsudaAbstract:Understanding of secondary metabolic pathway in plant is essential for finding druggable candidate enzymes. However, there are many enzymes whose functions are not yet discovered in organism-specific metabolic pathways. Towards identifying the functions of those enzymes, assignment of EC numbers to the Enzymatic Reactions they catalyze plays a key role, since EC numbers represent the categorization of enzymes on one hand, and the categorization of Enzymatic Reactions on the other hand. We propose reaction graph kernels for automatically assigning EC numbers to unknown Enzymatic Reactions in a metabolic network. Reaction graph kernels compute similarity between two chemical Reactions considering the similarity of chemical compounds in reaction and their relationships. In computational experiments based on the KEGG/REACTION database, our method successfully predicted the first three digits of the EC number with 83% accuracy. We also exhaustively predicted missing EC numbers in plant's secondary metabolism pathway. The prediction results of reaction graph kernels on 36 unknown Enzymatic Reactions are compared with an expert's knowledge. Using the same data for evaluation, we compared our method with E-zyme, and showed its ability to assign more number of accurate EC numbers. Reaction graph kernels are a new metric for comparing Enzymatic Reactions.
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Reaction graph kernels predict EC numbers of unknown Enzymatic Reactions in plant secondary metabolism
BMC Bioinformatics, 2010Co-Authors: Hiroto Saigo, Masahiro Hattori, Hisashi Kashima, Koji TsudaAbstract:Abstract Background Understanding of secondary metabolic pathway in plant is essential for finding druggable candidate enzymes. However, there are many enzymes whose functions are not yet discovered in organism-specific metabolic pathways. Towards identifying the functions of those enzymes, assignment of EC numbers to the Enzymatic Reactions they catalyze plays a key role, since EC numbers represent the categorization of enzymes on one hand, and the categorization of Enzymatic Reactions on the other hand. Results We propose reaction graph kernels for automatically assigning EC numbers to unknown Enzymatic Reactions in a metabolic network. Reaction graph kernels compute similarity between two chemical Reactions considering the similarity of chemical compounds in reaction and their relationships. In computational experiments based on the KEGG/REACTION database, our method successfully predicted the first three digits of the EC number with 83% accuracy. We also exhaustively predicted missing EC numbers in plant's secondary metabolism pathway. The prediction results of reaction graph kernels on 36 unknown Enzymatic Reactions are compared with an expert's knowledge. Using the same data for evaluation, we compared our method with E-zyme, and showed its ability to assign more number of accurate EC numbers. Conclusion Reaction graph kernels are a new metric for comparing Enzymatic Reactions.
Arieh Warshel - One of the best experts on this subject based on the ideXlab platform.
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origin of the non arrhenius behavior of the rates of Enzymatic Reactions
Journal of Physical Chemistry B, 2017Co-Authors: Subhendu Roy, Patrick Schopf, Arieh WarshelAbstract:The origin of the non-Arrhenius behavior of the rate constant for hydride transfer Enzymatic Reactions has been a puzzling problem since its initial observation. This effect has been used originally to support the idea that enzymes work by dynamical effects and more recently to suggest an entropy funnel model. Our analysis, however, has advanced the idea that the reason for the non-Arrhenius trend reflects the temperature dependence of the rearrangements of the protein polar groups in response to the change in the charge distribution of the reacting system during the transition from the ground state (GS) to the transition state (TS). Here we examine the validity of our early proposal by simulating the catalytic reaction of alcohol dehydrogenase (ADH) and determine the microscopic origin of the entropic and enthalpic contributions to the activation barrier. The corresponding analysis establishes the origin of the non-Arrhenius behaviors and quantifies our original suggestion that the classical effect is du...
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enhancing paradynamics for qm mm sampling of Enzymatic Reactions
Journal of Physical Chemistry B, 2016Co-Authors: Jeronimo Lameira, Ilya Kupchencko, Arieh WarshelAbstract:Despite the enormous increase in computer power, it is still extremely challenging to obtain computationally converging sampling of ab initio QM/MM (QM(ai)/MM) free energy surfaces in condensed phases. The sampling problem can be significantly reduced by the use of the reference potential paradynamics (PD) approach, but even this approach still requires major computer time in studies of Enzymatic Reactions. To further reduce the sampling problem we developed here a new PD version where we use an empirical valence bond reference potential that has a minimum rather than a maximum at the transition state region of the target potential (this is accomplished conveniently by shifting the EVB of the product state). Hence, we can map the TS region in a more efficient way. Here, we introduce and validate the inverted EVB PD approach. The validation involves the study of the S(N)2 step of the reaction catalyzed by haloakene dehalogenase (DhlA) and the GTP hydrolysis in the RasGAP system. In addition, we have also studied the corresponding reaction in water for each of the systems described here and the reaction involving trimethylsulfonium and dimethylamine in solution. The results are encouraging and the new strategy appears to provide a powerful way of evaluating QM(ai)/MM activation free energies.
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towards accurate ab initio qm mm calculations of free energy profiles of Enzymatic Reactions
Journal of Physical Chemistry B, 2006Co-Authors: Edina Rosta, And Marco Klahn, Arieh WarshelAbstract:Reliable studies of Enzymatic Reactions by combined quantum mechanical/molecular mechanics (QM/MM) approaches, with an ab initio description of the quantum region, presents a major challenge to computational chemists. The main problem is the need for a large amount of computer time to evaluate the QM energy, which in turn makes it extremely challenging to perform proper configurational sampling. This work presents major progress toward the evaluation of ab initio QM/MM free-energy surfaces and activation free energies of Reactions in enzymes and in solutions. This is done by exploiting our previous idea of using the empirical valence bond (EVB) method as a reference potential and then using the linear response approximation (LRA) approach to evaluate the free energies of transfer from the EVB to the QM/MM surfaces in the reactant and product state. However, the new crucial step involves the use of a constraint at the transition state that fixes the system at a given value of the reaction coordinate and al...
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on possible pitfalls in ab initio quantum mechanics molecular mechanics minimization approaches for studies of Enzymatic Reactions
Journal of Physical Chemistry B, 2005Co-Authors: Marco Klahn, Edina Rosta, Sonja Braunsand, Arieh WarshelAbstract:Reliable studies of Enzymatic Reactions by combined quantum mechanics/molecular mechanics (QM/MM) approaches, with an ab initio description of the quantum region, presents a major challenge to computational chemists. The main problem is the need for a very large computer time for the evaluation of the QM energy, which in turn makes it extremely challenging to perform proper configurational sampling. A seemingly reasonable alternative is to perform energy minimization studies of the type used in gas-phase ab initio studies. However, it is hard to see why such an approach should give reliable results in protein active sites. To examine the problems with energy minimization QM/MM approaches, we chose the hypothetical reaction of a metaphosphate ion with water in the Ras.GAP complex. This hypothetical reaction served as a simple benchmark reaction. The possible problems with the QM/MM minimization were explored by generating several protein configurations from long MD simulations and using energy minimization and scanning of the reaction coordinates to evaluate the corresponding potential energy surfaces of the reaction for each of these different protein configurations. Comparing these potential energy surfaces, we found major variations of the corresponding minima. Furthermore, the reaction energies and activation energies also varied significantly even for similar protein configurations. The specific coordination of a magnesium ion, present in the active center of the protein complex, turned out to influence the energetics of the reaction in a major way, where a direct coordination to the reactant leads to an increase of the activation energy by 17 kcal/mol. Apparently, using energy minimization to generate potential surfaces for an Enzymatic reaction, while starting from a single protein structure, could lead to major errors in calculations of activation free energies and binding free energies. Thus we believe that extensive samplings of the configurational space of the protein are essential for meaningful determination of the energetics of Enzymatic Reactions. The possible relevance of our conclusion with regard to a recent study of the RasGAP reaction is discussed.
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Energetics and Dynamics of Enzymatic Reactions
Journal of Physical Chemistry B, 2001Co-Authors: Jordi Villà And, Arieh WarshelAbstract:This review considers the advances made in using computer simulations to elucidate the catalytic power of enzymes. It is shown that some current approaches, and in particular the empirical valence bond approach, allow us to describe Enzymatic Reactions by rigorous concepts of current chemical physics and to estimate any proposed catalytic contribution. This includes evaluation of activation free energies, nonequilibrium solvation, quantum mechanical tunneling, entropic effects, and other factors. The ability to evaluate activation free energies for Reactions in water and proteins allows us to simulate the rate acceleration in Enzymatic Reactions. It is found that the most important contribution to catalysis comes from the reduction of the activation free energy by electrostatic effects. These effects are found to be associated with the preorganized polar environment of the enzyme active site. The use of computer simulations as effective tools for examining different catalytic proposals is illustrated by t...
Hiroto Saigo - One of the best experts on this subject based on the ideXlab platform.
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Reaction graph kernels predict EC numbers of unknown Enzymatic Reactions in plant secondary metabolism.
BMC bioinformatics, 2010Co-Authors: Hiroto Saigo, Masahiro Hattori, Hisashi Kashima, Koji TsudaAbstract:Understanding of secondary metabolic pathway in plant is essential for finding druggable candidate enzymes. However, there are many enzymes whose functions are not yet discovered in organism-specific metabolic pathways. Towards identifying the functions of those enzymes, assignment of EC numbers to the Enzymatic Reactions they catalyze plays a key role, since EC numbers represent the categorization of enzymes on one hand, and the categorization of Enzymatic Reactions on the other hand. We propose reaction graph kernels for automatically assigning EC numbers to unknown Enzymatic Reactions in a metabolic network. Reaction graph kernels compute similarity between two chemical Reactions considering the similarity of chemical compounds in reaction and their relationships. In computational experiments based on the KEGG/REACTION database, our method successfully predicted the first three digits of the EC number with 83% accuracy. We also exhaustively predicted missing EC numbers in plant's secondary metabolism pathway. The prediction results of reaction graph kernels on 36 unknown Enzymatic Reactions are compared with an expert's knowledge. Using the same data for evaluation, we compared our method with E-zyme, and showed its ability to assign more number of accurate EC numbers. Reaction graph kernels are a new metric for comparing Enzymatic Reactions.
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Reaction graph kernels predict EC numbers of unknown Enzymatic Reactions in plant secondary metabolism
BMC Bioinformatics, 2010Co-Authors: Hiroto Saigo, Masahiro Hattori, Hisashi Kashima, Koji TsudaAbstract:Abstract Background Understanding of secondary metabolic pathway in plant is essential for finding druggable candidate enzymes. However, there are many enzymes whose functions are not yet discovered in organism-specific metabolic pathways. Towards identifying the functions of those enzymes, assignment of EC numbers to the Enzymatic Reactions they catalyze plays a key role, since EC numbers represent the categorization of enzymes on one hand, and the categorization of Enzymatic Reactions on the other hand. Results We propose reaction graph kernels for automatically assigning EC numbers to unknown Enzymatic Reactions in a metabolic network. Reaction graph kernels compute similarity between two chemical Reactions considering the similarity of chemical compounds in reaction and their relationships. In computational experiments based on the KEGG/REACTION database, our method successfully predicted the first three digits of the EC number with 83% accuracy. We also exhaustively predicted missing EC numbers in plant's secondary metabolism pathway. The prediction results of reaction graph kernels on 36 unknown Enzymatic Reactions are compared with an expert's knowledge. Using the same data for evaluation, we compared our method with E-zyme, and showed its ability to assign more number of accurate EC numbers. Conclusion Reaction graph kernels are a new metric for comparing Enzymatic Reactions.
Masahiro Hattori - One of the best experts on this subject based on the ideXlab platform.
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Reaction graph kernels predict EC numbers of unknown Enzymatic Reactions in plant secondary metabolism.
BMC bioinformatics, 2010Co-Authors: Hiroto Saigo, Masahiro Hattori, Hisashi Kashima, Koji TsudaAbstract:Understanding of secondary metabolic pathway in plant is essential for finding druggable candidate enzymes. However, there are many enzymes whose functions are not yet discovered in organism-specific metabolic pathways. Towards identifying the functions of those enzymes, assignment of EC numbers to the Enzymatic Reactions they catalyze plays a key role, since EC numbers represent the categorization of enzymes on one hand, and the categorization of Enzymatic Reactions on the other hand. We propose reaction graph kernels for automatically assigning EC numbers to unknown Enzymatic Reactions in a metabolic network. Reaction graph kernels compute similarity between two chemical Reactions considering the similarity of chemical compounds in reaction and their relationships. In computational experiments based on the KEGG/REACTION database, our method successfully predicted the first three digits of the EC number with 83% accuracy. We also exhaustively predicted missing EC numbers in plant's secondary metabolism pathway. The prediction results of reaction graph kernels on 36 unknown Enzymatic Reactions are compared with an expert's knowledge. Using the same data for evaluation, we compared our method with E-zyme, and showed its ability to assign more number of accurate EC numbers. Reaction graph kernels are a new metric for comparing Enzymatic Reactions.
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Reaction graph kernels predict EC numbers of unknown Enzymatic Reactions in plant secondary metabolism
BMC Bioinformatics, 2010Co-Authors: Hiroto Saigo, Masahiro Hattori, Hisashi Kashima, Koji TsudaAbstract:Abstract Background Understanding of secondary metabolic pathway in plant is essential for finding druggable candidate enzymes. However, there are many enzymes whose functions are not yet discovered in organism-specific metabolic pathways. Towards identifying the functions of those enzymes, assignment of EC numbers to the Enzymatic Reactions they catalyze plays a key role, since EC numbers represent the categorization of enzymes on one hand, and the categorization of Enzymatic Reactions on the other hand. Results We propose reaction graph kernels for automatically assigning EC numbers to unknown Enzymatic Reactions in a metabolic network. Reaction graph kernels compute similarity between two chemical Reactions considering the similarity of chemical compounds in reaction and their relationships. In computational experiments based on the KEGG/REACTION database, our method successfully predicted the first three digits of the EC number with 83% accuracy. We also exhaustively predicted missing EC numbers in plant's secondary metabolism pathway. The prediction results of reaction graph kernels on 36 unknown Enzymatic Reactions are compared with an expert's knowledge. Using the same data for evaluation, we compared our method with E-zyme, and showed its ability to assign more number of accurate EC numbers. Conclusion Reaction graph kernels are a new metric for comparing Enzymatic Reactions.
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Generalized reaction patterns for prediction of unknown Enzymatic Reactions.
Genome informatics. International Conference on Genome Informatics, 2008Co-Authors: Yugo Shimizu, Masahiro Hattori, Susumu Goto, Minoru KanehisaAbstract:Prediction of unknown Enzymatic Reactions is useful for understanding biological processes such as Reactions to external substances like endocrine disrupters. To create an accurate prediction, we need to define a similarity measure in the reaction. We have developed the KEGG RPAIR database which is a collection of chemical structure transformation patterns, called RDM patterns, for substrate-product pairs of Enzymatic Reactions. In this study, we compared RDM patterns with EC numbers which are the well-known hierarchical classification scheme for enzymes. Additionally, we performed hierarchical clustering of RDM patterns using the information stating whether each sub-subclass of EC has a particular RDM patterns or not. To represent the variation of RDM patterns in a cluster, we generalized RDM patterns in the same cluster using the hierarchy of KEGG Atomtypes, which are the components of RDM patterns. Using this generalized pattern, we can predict which cluster includes a given RDM pattern even if the reaction of the pattern has not been assigned any EC numbers. Thus we will be able to define the similarity between Enzymatic Reactions by using this cluster information.
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computational assignment of the ec numbers for genomic scale analysis of Enzymatic Reactions
Journal of the American Chemical Society, 2004Co-Authors: Masaaki Kotera, Masahiro Hattori, Susumu Goto, Yasushi Okuno, Minoru KanehisaAbstract:The EC (Enzyme Commission) numbers represent a hierarchical classification of Enzymatic Reactions, but they are also commonly utilized as identifiers of enzymes or enzyme genes in the analysis of complete genomes. This duality of the EC numbers makes it possible to link the genomic repertoire of enzyme genes to the chemical repertoire of metabolic pathways, the process called metabolic reconstruction. Unfortunately, there are numerous Reactions known to be present in various pathways, but they will never get EC numbers because the EC number assignment requires published articles on full characterization of enzymes. Here we report a computerized method to automatically assign the EC numbers up to the sub-subclasses, i.e., without the fourth serial number for substrate specificity, given pairs of substrates and products. The method is based on a new classification scheme of Enzymatic Reactions, named the RC (reaction classification) number. Each reaction in the current dataset of the EC numbers is first dec...
Walter Thiel - One of the best experts on this subject based on the ideXlab platform.
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importance of mm polarization in qm mm studies of Enzymatic Reactions assessment of the qm mm drude oscillator model
Journal of Chemical Theory and Computation, 2017Co-Authors: Abir Ganguly, Eliot Boulanger, Walter ThielAbstract:For accurate quantum mechanics/molecular mechanics (QM/MM) studies of Enzymatic Reactions, it is desirable to include MM polarization, for example by using the Drude oscillator (DO) model. For a long time, such studies were hampered by the lack of well-tested polarizable force fields for proteins. Following up on a recent preliminary QM/MM-DO assessment (J. Chem. Theory. Comput. 2014, 10, 1795–1809), we now report a comprehensive investigation of the effects of MM polarization on two Enzymatic Reactions, namely the Claisen rearrangement in chorismate mutase and the hydroxylation reaction in p-hydroxybenzoate hydroxylase, using the QM/CHARMM-DO model and two QM methods (B3LYP, OM2). We compare the results from extensive geometry optimizations and free energy simulations at the QM/MM-DO level to those obtained from analogous calculations at the conventional QM/MM level.
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toward qm mm simulation of Enzymatic Reactions with the drude oscillator polarizable force field
Journal of Chemical Theory and Computation, 2014Co-Authors: Eliot Boulanger, Walter ThielAbstract:The polarization of the environment can influence the results from hybrid quantum mechanical/molecular mechanical (QM/MM) simulations of Enzymatic Reactions. In this article, we address several technical aspects in the development of polarizable QM/MM embedding using the Drude Oscillator (DO) force field. We propose a stable and converging update of the DO polarization state for geometry optimizations and a suitable treatment of the QM/MM-DO boundary when the QM and MM regions are separated by cutting through a covalent bond. We assess the performance of our approach by computing binding energies and geometries of three selected complexes relevant to biomolecular modeling, namely the water trimer, the N-methylacetamide dimer, and the cationic bis(benzene)sodium sandwich complex. Using a recently published MM-DO force field for proteins, we evaluate the effect of MM polarization on the QM/MM energy profiles of the Enzymatic Reactions catalyzed by chorismate mutase and by p-hydroxybenzoate hydroxylase. We f...
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long range electrostatic effects in qm mm studies of Enzymatic Reactions application of the solvated macromolecule boundary potential
Journal of Chemical Theory and Computation, 2011Co-Authors: Tobias Benighaus, Walter ThielAbstract:Long-range electrostatic interactions are important in simulations of Enzymatic Reactions. They can be divided into the effects due to bulk solvent and those due to the electrostatic potential of the outer macromolecule. We study and quantify the importance of these two effects for two test systems by application of the solvated macromolecule boundary potential (SMBP) [J. Chem. Theory Comput. 2009, 5, 3114-3128]. We validate the accuracy of the SMBP for these test systems and present a transferable protocol for determination of optimal SMBP parameters as well as recommended default values for these parameters. Two Enzymatic Reactions with different characteristics are studied: the intramolecular Claisen rearrangement in chorismate mutase that is associated with little charge transfer and the hydroxylation reaction in p-hydroxybenzoate hydroxylase that corresponds to a formal "OH(+)" transfer and thus involves significant charge transfer. It is found that the effects of the electrostatic potential of the outer macromolecule and of bulk solvent are only important in the latter case, where their neglect causes deviations in the computed barriers on the order of 1-2 kcal/mol, respectively. Even larger deviations on the order of several kilocalories per mole are observed for the reaction energies in p-hydroxybenzoate hydroxylase if the electrostatic potential of the outer macromolecule is neglected.
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finite temperature effects in Enzymatic Reactions insights from qm mm free energy simulations
Canadian Journal of Chemistry, 2009Co-Authors: Hans Martin Senn, Johannes Kastner, Jurgen Breidung, Walter ThielAbstract:We report potential-energy and free-energy data for three Enzymatic Reactions: carbon–halogen bond formation in fluorinase, hydrogen abstraction from camphor in cytochrome P450cam, and chorismate-t...