Targeted genome mining for natural product discovery

Abstract

Natural products are a rich source of bioactive compounds, which are encoded by the biosynthetic gene clusters (BGCs). Genome mining is an essential strategy for identifying and characterizing BGCs. Targeted genome mining excels in the identification of similar BGCs based on key biosynthetic enzymes across multiple genomes. This chapter details the use of both manual and automated targeted genome mining to identify members of the FK-family BGCs (rapamycin, FK520/506). We describe the process of selecting query proteins, evaluating genomic context, and determining the presence of putative BGCs. Additionally, to streamline the manual process, we used GATOR-GC, a computational tool that identifies similar BGCs using required and optional proteins, performs comparative genomic analysis, deduplicates redundant BGCs, and generates visualizations of gene conservation and BGC diversity. Applying this approach, we showed how to identify FK-family members, both by looking into the cluster conservation diagrams, and the clustered heatmap summarizing all-vs-all BGC comparisons. The methods outlined here can be adapted for mining other natural product families, offering a scalable framework for uncovering novel biosynthetic pathways. Beyond natural product discovery, GATOR-GC provides broader applications for analyzing gene cluster conservation, organization, and evolutionary patterns.

Keywords: Biosynthetic gene clusters,; GATOR-GC,; Genome mining; Natural products.