RiPPMiner: a bioinformatics resource for deciphering chemical structures of RiPPs based on prediction of cleavage and cross-links.

ABSTRACT: Ribosomally synthesized and post-translationally modified peptides (RiPPs) constitute a rapidly growing class of natural products with diverse structures and bioactivities. We have developed RiPPMiner, a novel bioinformatics resource for deciphering chemical structures of RiPPs by genome mining. RiPPMiner derives its predictive power from machine learning based classifiers, trained using a well curated database of more than 500 experimentally characterized RiPPs. RiPPMiner uses Support Vector Machine to distinguish RiPP precursors from other small proteins and classify the precursors into 12 sub-classes of RiPPs. For classes like lanthipeptide, cyanobactin, lasso peptide and thiopeptide, RiPPMiner can predict leader cleavage site and complex cross-links between post-translationally modifie

SUBMITTER: Agrawal P 

PROVIDER: S-EPMC5570163 | biostudies-literature | 2017 Jul

REPOSITORIES: biostudies-literature