RiPPMiner-Genome: A Web Resource for Automated Prediction of Crosslinked Chemical Structures of RiPPs by Genome Mining. Issue 11 (28th May 2021)

• Record Type: Journal Article
• Authors: Agrawal, Priyesh; Amir, Sana; Deepak, ; Barua, Drishtee; Mohanty, Debasisa
• Is Part Of: Journal of molecular biology. Volume 433:Issue 11(2021)
• Publication Date: 2021-05-28
• Subjects: RiPP -- cross-link -- genome mining -- machine learning -- chemical structure prediction; Molecular biology -- Periodicals; Biology -- Periodicals; Biochemistry -- Periodicals; Bacteriology -- Periodicals; Molecular Biology -- Periodicals; Biochemistry -- Periodicals; Biologie moléculaire -- Périodiques; Biologie -- Périodiques; Biochimie -- Périodiques; Moleculaire biologie; Biochemistry; Biology; Molecular biology; Periodicals; 572.805
• DOI: 10.1016/j.jmb.2021.166887
• Languages: English
• ISSNs: 0022-2836

Abstract

Graphical abstract: Highlights: Completely automated prediction of crosslinked chemical structure of RiPPs using genomic sequences of RiPP BGCs. ML approach for identifying precursor from multiple small ORFs in a RiPP BGC & predicting cleavage and cross-links. Identification of uncrosslinked modified residues in lanthipeptides and enhancement of prediction accuracy. Abstract: RiPPMiner-Genome is a unique bioinformatics resource for identifying Biosynthetic Gene Clusters (BGC) for RiPPs (Ribosomally Synthesized and Post-translationally Modified Peptides) and automated prediction of crosslinked chemical structures of RiPPs starting from genomic sequences. It is a major update of the RiPPMiner webserver, which used only peptide sequence of RiPP precursors as input for predicting RiPP class and crosslinked chemical structures. Other major improvements are, machine learning (ML) based identification of correct RiPP precursor peptide from among multiple small ORFs (Open Reading Frames) in a BGC, prediction of the cleavage site and cross-links in thiopeptides and identification of non-crosslinked modified residues in lanthipeptides. It has been benchmarked on a dataset of 204 experimentally characterized RiPP BGCs. RiPPMiner-Genome also facilitates visualization of the RiPP BGCs and depiction of the chemical structure of crosslinked RiPP. It also has an interface for searching characterized RiPPs, similar to the predicted core peptide sequence or crosslinked chemical structure.