Literature DB >> 19445497

Investigations of enzyme-catalyzed reactions based on physicochemical descriptors applied to hydrolases.

Oliver Sacher1, Martin Reitz, Johann Gasteiger.   

Abstract

The EC number system for the classification of enzymes uses different criteria such as reaction pattern, the nature of the substrate, the type of transferred groups or the type of acceptor group. These criteria are used with different emphasis for the various enzyme classes and thus do not contribute much to an understanding of the mechanisms of enzyme catalyzed reactions. To explore the reasons for bonds being broken in enzyme catalyzed metabolic reactions, we calculated physicochemical effects for the bonds reacting in the substrate of these enzymatic reactions. These descriptors allow the definition of similarities within these reactions and thus can serve as a method for the classification of enzyme reactions. To foster an understanding of the investigations performed here, we compare the similarities found on the basis of the physicochemical effects with the EC number classification. To allow a reasonable comparison we selected enzymatic reactions where the EC number system is largely built on criteria based on the reaction mechanism. This is true for hydrolysis reactions, falling into the domain of the EC class 3 (EC 3.b.c.d). The comparison is made by a Kohonen neural network based on an unsupervised learning algorithm. For these hydrolysis reactions, the similarity analysis on physicochemical effects produces results that are, by and large, similar to the EC number. However, this similarity analysis reveals finer details of the enzymatic reactions and thus can provide a better basis for the mechanistic comparison of enzymes.

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Year:  2009        PMID: 19445497     DOI: 10.1021/ci800277f

Source DB:  PubMed          Journal:  J Chem Inf Model        ISSN: 1549-9596            Impact factor:   4.956


  8 in total

1.  IGERS: inferring Gibbs energy changes of biochemical reactions from reaction similarities.

Authors:  Kristian Rother; Sabrina Hoffmann; Sascha Bulik; Andreas Hoppe; Johann Gasteiger; Herrmann-Georg Holzhütter
Journal:  Biophys J       Date:  2010-06-02       Impact factor: 4.033

Review 2.  Toward mechanistic classification of enzyme functions.

Authors:  Daniel E Almonacid; Patricia C Babbitt
Journal:  Curr Opin Chem Biol       Date:  2011-04-12       Impact factor: 8.822

3.  Computational Approaches for Automated Classification of Enzyme Sequences.

Authors:  Akram Mohammed; Chittibabu Guda
Journal:  J Proteomics Bioinform       Date:  2011-08-23

4.  Quantitative comparison of catalytic mechanisms and overall reactions in convergently evolved enzymes: implications for classification of enzyme function.

Authors:  Daniel E Almonacid; Emmanuel R Yera; John B O Mitchell; Patricia C Babbitt
Journal:  PLoS Comput Biol       Date:  2010-03-12       Impact factor: 4.475

5.  Is EC class predictable from reaction mechanism?

Authors:  Neetika Nath; John B O Mitchell
Journal:  BMC Bioinformatics       Date:  2012-04-24       Impact factor: 3.169

6.  Combining chemoinformatics with bioinformatics: in silico prediction of bacterial flavor-forming pathways by a chemical systems biology approach "reverse pathway engineering".

Authors:  Mengjin Liu; Bruno Bienfait; Oliver Sacher; Johann Gasteiger; Roland J Siezen; Arjen Nauta; Jan M W Geurts
Journal:  PLoS One       Date:  2014-01-08       Impact factor: 3.240

7.  Assignment of EC numbers to enzymatic reactions with reaction difference fingerprints.

Authors:  Qian-Nan Hu; Hui Zhu; Xiaobing Li; Manman Zhang; Zhe Deng; Xiaoyan Yang; Zixin Deng
Journal:  PLoS One       Date:  2012-12-28       Impact factor: 3.240

8.  Characterising Complex Enzyme Reaction Data.

Authors:  Handan Melike Dönertaş; Sergio Martínez Cuesta; Syed Asad Rahman; Janet M Thornton
Journal:  PLoS One       Date:  2016-02-03       Impact factor: 3.240

  8 in total

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