Since the end of the 20th century, many genomes from various species have been profiled, enabling their comparison, and the comparison of inter-species enzymes to find Selleckchem Dabrafenib conserved and non-conserved regions. This gives a hint as to which amino acid residues undergo evolutionary change. Thanks to improvements in computational ability and data storage, it is becoming possible to design protein structures from amino acid sequences and to predict their function through computer simulation and bioinformatics tools. There have been some successful attempts to manipulate
enzyme functions; in 2008 Ghirlanda et al. published on the computational enzyme design of Kemp elimination catalysts (Ghirlanda, 2008). Nevertheless, manipulation of enzyme function still remains a trial and error process. Although some examples of the structure–function relationships of enzymes are known, it is only limited to a small set of enzymes. We have been tackling the genome-scale metabolic pathway reconstruction problem, constructing a systematic assembly and organization of all the metabolism of a given organism,
while focusing on the relationships between genomic and chemical spaces. Through this process, the KEGG Orthology (KO) database was created, mapping orthologue gene groups that at the same place in regard to biological pathway and functional hierarchy, offering strategies to predict the possible set of chemical structures from the genomic information of a given organism. At first, we accumulated knowledge about glycosyltransferase Protease Inhibitor Library groups for the post-translational modification of proteins. These enzyme genes were grouped based on the orthology and substrate specificity, and were successfully applied to predict the possible glycan structures for a given genome or transcriptome (Hashimoto et al., 2009 and Kawano
et al., 2005). The same strategy was applied to polyketides, non-ribosomal peptides and polyunsaturated fatty acids (Minowa et al., 2007 and Hashimoto et al., 2008). This strategy Carbachol was successful because the orthology and the substrate specificity correlated well. In order for this strategy to be applied to other enzyme groups, it is necessary to organize the enzyme classification so that enzyme function can be deduced from protein sequences, or vice versa, for a wide range of enzymes. We recently established the KEGG Reaction Class (RCLASS) based on the similarity of reactions in terms of the reaction motifs or the RDM chemical transformation patterns. RCLASS enables users to develop a method to predict what kind of enzymes can catalyze putative reactions, and also a method to predict the metabolic fate of given compounds. We plan to use these two methodologies to connect chemical and genomic spaces, whilst simultaneously continuing work to refine enzyme classifications for predictive genomic and metabolomic analyses.