A new genome-mining tool redefines the lasso peptide biosynthetic landscape
By Jonathan I. Tietz, Christopher J. Schwalen, Parth Patel, Tucker Maxson, P. Blair, Hua-Chia Tai, U. I. Zakai, D. Mitchell. Nature Chemical Biology, 2016
424 citations recorded by the source corpus.
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 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 physiochemical properties and bioactivities, but their hypervariability renders them challenging prospects for automated mining. Our approach yielded the most comprehensive mapping of lasso peptide space, revealing >1,300 compounds. We characterized the structures and bioactivities of six lasso peptides, prioritized based on predicted structural novelty, including an unprecedented handcuff-like topology and another with a citrulline modification exceptionally rare among bacteria. These combined insights significantly expand the knowledge of lasso peptides, and more broadly, provide a framework for future genome-mining efforts.
Subfields: Microbiology, Molecular Biology, Pharmacology
View Paper: https://www.semanticscholar.org/paper/f270341d8609f555a9062ac6ab0f2070f7d89383
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Citations in the corpus
- Artificial Intelligence For Natural Product Drug Discovery (2023, methodology): “For example, the hidden Markov model-based method ClusterFinder 15 , the deep learning approaches DeepBGC 16 , GECCO 17 and SanntiS 18 , and several genome mining algorithms for ribosomally synthesized and post-translationally modified peptides (RiPPs) 19–22 each use deep learning”
- antiSMASH 4.0—improvements in chemistry prediction and gene cluster boundary identification (2017, methodology): “To assist in identifying the precursor peptide-encoding gene, antiSMASH now utilizes the algorithm from the genome-mining platform Rapid ORF Description and Evaluation Online (RODEO) (24), which uses a combination of heuristic scoring, SVM and motif analysis to evaluate all candidate precursor pepti”