Predictive genomic and metabolomic analysis for the standardization of enzyme data

The IUBMB׳s Enzyme List gives a valuable library of the individual experimental facts on enzyme activities, providing the standard classification and nomenclature of enzymes. Empirical knowledge about the relationships between the enzyme protein sequences (or structures) and their functions (the capability of catalyzing chemical reactions) has been accumulating in public literatures and databases. This provides a complementary approach to standardize and organize enzyme data, i.e., predicting the possible enzymes, reactions and metabolites that remain to be identified experimentally. Thus, we suggest the necessity of classifying enzymes based on the evidence and different perspectives obtained from various experimental works. The KEGG (Kyoto Encyclopedia of Genes and Genomes) database describes enzymes from many different viewpoints including; the IUBMB׳s enzyme nomenclature/classification (EC numbers), the similarity group of enzyme reactions (KEGG Reaction Class; RCLASS) based solely on the chemical structure transformation patterns, and the similarity groups of enzyme genes (KEGG Orthology; KO) based on the orthologous groups that can be mapped to the KEGG PATHWAY and BRITE functional hierarchy. Some unique identifiers were additionally introduced to the KEGG database other than the EC numbers established by IUBMB. R, RP and RC numbers are given to distinguish reactions, reactant pairs and RCLASS, respectively. Genes, including enzyme genes, have their own ID numbers in specific organisms, and they are classified into ortholog groups that are identified by K numbers. In this review, we explain the concept and methodology of this formulation with some concrete example cases. We propose it beneficial to create a standard classification scheme that deals with both experimentally identified and theoretically predicted enzymes.