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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 modified residues starting from genome sequences. RiPPMiner can identify correct cross-link pattern in a core peptide from among a very large number of combinatorial possibilities. Benchmarking of prediction accuracy of RiPPMiner on a large lanthipeptide dataset indicated high sensitivity, specificity, accuracy and precision. RiPPMiner also provides interfaces for visualization of the chemical structure, downloading of simplified molecular-input line-entry system and searching for RiPPs having similar sequences or chemical structures. The backend database of RiPPMiner provides information about modification system, precursor sequence, leader and core sequence, modified residues, cross-links and gene cluster for more than 500 experimentally characterized RiPPs. RiPPMiner is available at http://www.nii.ac.in/rippminer.html.
Pubmed ID: 28499008
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Commercial organization that provides cheminformatics software platforms, applications and services to optimize the value of chemistry information in life science and other R&D. This software enables structure visualization and management, property predictions and calculations, virtual synthesis, screening, clustering and drug design.
View all literature mentionsData repository of bacteriocin natural antimicrobial peptides and includes data collected from published literature as well as high-throughput datasets. The database provides a manually curated annotation of bacteriocin sequences. New bacteriocin submissions are welcome. Various tools have been incorporated for bacteriocin analysis, such as homology search, multiple sequence alignments, Hidden Markov Models, molecular modelling and retrieval through our taxonomy Browser. BACTIBASE should be a useful tool in food preservation or food safety applications and could have implications for the development of new drugs for medical use. BACTIBASE contains calculated or predicted physicochemical properties of 218 bacteriocins produced by both Gram-positive (194) and Gram-negative bacteria (19). They also note the presence of three bacteriocins from the Archaea domain. The database now comprises 31 genera (2009).
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