Identification of biosynthetic gene clusters using bioinformatic tools

Abstract

Biosynthetic gene clusters (BGCs) are genomic regions responsible for producing natural products with diverse biological activities. Identifying and characterizing these gene clusters is crucial for understanding the biosynthesis of secondary metabolites and for drug discovery efforts. In recent years, bioinformatics tools have played a pivotal role in the identification, annotation, and analysis of BGCs in microbial genomes. Tools such as antiSMASH, PRISM, and MultiGeneBlast leverage computational algorithms, comparative genomics, and machine learning techniques have been developed to predict BGCs based on the presence of biosynthetic enzymes and other conserved features. These tools enable the inference of chemical structures of natural products encoded by BGCs, further enhancing our understanding of secondary metabolite biosynthesis. They have become indispensable in the field of natural product discovery, empowering researchers to uncover novel secondary metabolites with potential therapeutic applications.