Computational Methods for Identification of Novel Secondary Metabolite Biosynthetic Pathways by Genome Analysis

Abstract

Secondary metabolites belonging to polyketide and nonribosomal peptide families constitute a major class of natural products with diverse biological functions and a variety of pharmaceutically important properties. Experimental studies have shown that the biosynthetic machinery for polyketide and nonribosomal peptides involves multi-functional megasynthases like Polyketide Synthases (PKSs) and nonribosomal peptide synthetases (NRPSs) which utilize a thiotemplate mechanism similar to that for fatty acid biosynthesis. Availability of complete genome sequences for an increasing number of microbial organisms has provided opportunities for using in silico genome mining to decipher the secondary metabolite natural product repertoire encoded by these organisms. Therefore, in recent years there have been major advances in development of computational methods which can analyze genome sequences to identify genes involved in secondary metabolite biosynthesis and help in deciphering the putative chemical structures of their biosynthetic products based on analysis of the sequence and structural features of the proteins encoded by these genes. These computational methods for deciphering the secondary metabolite biosynthetic code essentially involve identification of various catalytic domains present in this PKS/NRPS family of enzymes; a prediction of various reactions in these enzymatic domains and their substrate specificities and also precise identification of the order in which these domains would catalyze various biosynthetic steps. Structural bioinformatics analysis of known secondary metabolite biosynthetic clusters has helped in formulation of predictive rules for deciphering domain organization, substrate specificity, and order of substrate channeling. In this chapter, the progress in development of various computational methods is discussed by different research groups, and specifically, the utility in identification of novel metabolites by genome mining and rational design of natural product analogs by biosynthetic engineering studies.