A new genome-mining tool redefines the lasso peptide biosynthetic landscape.

ABSTRACT: Ribosomally synthesized and post-translationally modified peptide (RiPP) natural products are attractive for genome-driven discovery and re-engineering, but limitations in bioinformatic methods and exponentially increasing genomic data make large-scale mining of RiPP data difficult. We report RODEO (Rapid ORF Description and Evaluation Online), which combines hidden-Markov-model-based analysis, heuristic scoring, and machine learning to identify biosynthetic gene clusters and predict RiPP precursor peptides. We initially focused on lasso peptides, which display intriguing physicochemical properties and bioactivities, but their hypervariability renders them challenging prospects for automated mining. Our approach yielded the most comprehensive mapping to date of lasso peptide space, reveali

SUBMITTER: Tietz JI 

PROVIDER: S-EPMC5391289 | biostudies-literature | 2017 May

REPOSITORIES: biostudies-literature