A Machine Learning-Based Genome Mining Approach Reveals Unprecedented Biarylitide Diversity
TL;DR
This study repurposed a machine learning algorithm to comprehensively chart the biosynthetic space of the biarylitides, including variation of precursor motifs, P450, and additional modifying enzymes, which yielded 277 biarylitide biosynthetic gene clusters (BGCs).
Abstract
Biarylitides are a group of bacterial ribosomally synthesized and post-translationally modified peptides (RiPPs) that contain a biaryl bridge formed by dedicated cytochrome P450 enzymes that can introduce different cross-links. The biarylitides are produced via a five-amino-acid precursor peptide, encoded by a minimal 18 bp gene that evades automatic detection. Previous genome mining approaches for biarylitides do not capture their full biosynthetic space. We therefore repurposed a machine learning algorithm to comprehensively chart the biosynthetic space of the biarylitides, including variation of precursor motifs, P450, and additional modifying enzymes, which yielded 277 biarylitide biosynthetic gene clusters (BGCs). We experimentally investigated biaryl formation with previously uninvestigated core peptide motifs, including YWH, YVH, and YWY, and elucidated the nature of these cross-links. This study significantly expands the biarylitide precursor and BGC diversity and provides directions for the systematic exploration of other RiPP families.