Microbial bioprospecting for benzoxazolinate-like molecules: unleashing the potential of genome mining
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The benzoxazolinate moiety is a key functional group found in a few natural products (NPs), exhibiting diverse bioactivities, including antitumor, antibacterial, and cytotoxic activities. Despite their clinical importance, only a few bacterial strains and NPs have been reported harboring this rare bis-heterocyclic moiety, underscoring a largely unexplored chemical space. Here, we performed large-scale genome mining and identified 277 putative biosynthetic gene clusters (BGCs) across diverse bacterial hosts, including previously unreported bacterial genera and strains. The BGCs were grouped into three compound classes: benzoxazolinate, benzobactin, and ashimides based on sequence similarity network clustering. Bioactivity predictions of the identified BGCs revealed the predominance of antibacterial and cytotoxic potential, highlighting promising candidates for future experimental validation and functional studies. This study also presents a neural network-based bioprospecting model that efficiently detects rare BGCs encoding benzoxazolinate-containing molecules from genomic sequences. Overall, our findings expand the known repertoire of bacterial hosts with the potential to produce benzoxazolinate-containing NPs and provide a comprehensive framework for the discovery and identification of candidate BGCs.
Importance
This study helped uncover previously unknown bacterial hosts with the potential to encode benzoxazolinate-containing NPs through extensive genome mining. The findings suggest that benzoxazolinate-associated biosynthetic potential is more widespread than previously recognized and often overlooked by conventional annotation tools. We developed a neural network-based bioprospecting model to rapidly identify rare clusters in genomic and metagenomic datasets with high accuracy. Our work demonstrates a systematic strategy for uncovering cryptic gene clusters associated with benzoxazolinate-like metabolites across microbial genomes, thereby advancing a rational and scalable approach for future natural product discovery efforts.
