title: Investigations of enzyme-catalyzed reactions based on physicochemical descriptors applied to hydrolases.
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.
authors.affiliation: Molecular Networks GmbH, D-91052 Erlangen, Germany.
authors.forename: Oliver
authors.initials: O
authors.lastname: Sacher
authors.forename: Martin
authors.initials: M
authors.lastname: Reitz
authors.forename: Johann
authors.initials: J
authors.lastname: Gasteiger
mesh_terms: D055162:Biocatalysis; D055598:Chemical Phenomena; D016208:Databases, Factual; D006867:Hydrolases / Q000737:chemistry* / Q000145:classification / Q000378:metabolism*; D016571:Neural Networks, Computer
chemical_list: D006867:Hydrolases