Background: Enzymes are responsible for the catalysis of the biochemical reactions in metabolic pathways. Analogous enzymes are able to catalyze the same reactions, but they present no significant sequence similarity at the primary level, and possibly different tertiary structures as well. They are thought to have arisen as the result of independent evolutionary events. A detailed study of analogous enzymes may reveal new catalytic mechanisms, add information about the origin and evolution of biochemical pathways and disclose potential targets for drug development.
Results: In this work, we have constructed and implemented a new approach, AnEnPi (the Analogous Enzyme Pipeline), using a combination of bioinformatics tools like BLAST, HMMer, and in-house scripts, to assist in the identification, annotation, comparison and study of analogous and homologous enzymes. The algorithm for the detection of analogy is based i) on the construction of groups of homologous enzymes and ii) on the identification of cases where a given enzymatic activity is performed by two or more proteins without significant similarity between their primary structures. We applied this approach to a dataset obtained from KEGG Comprising all annotated enzymes, which resulted in the identification of 986 EC classes where putative analogy was detected (40.5% of all EC classes). AnEnPi is of considerable value in the construction of initial datasets that can be further curated, particularly in gene and genome annotation, in studies involving molecular evolution and metabolism and in the identification of new potential drug targets.
Conclusion: AnEnPi is an efficient tool for detection and annotation of analogous enzymes and other enzymes in whole genomes. It is available for academic use at: http://bioinfo.pdtis.fiocruz.br/AnEnPi/