RiPPMiner-Genome is the updated version of RiPPMiner which can predict the complete chemical structure of RiPPs using the genomic sequence of the producing bacteria as input.
RiPPMiner-Peptide It takes RiPP precursor peptide sequence as input and uses SVM to classify the precursor into nine sub-classes of RiPPs. For classes like Lanthipeptide, Cyanobactin and Lassopeptide, RiPPMiner predicts the leader cleavage site and finally complex crosslinking and post-translationally modified residues are predicted in the core peptide. For Thiopeptide it predicts crosslinking and modified residues. RiPPMiner can identify correct PTM and crosslink pattern in a query RiPP core peptide from among a very large number of combinatorial possibilities using machine learning. RiPPMiner also provides GUI for visualization of the predicted chemical structure and also search for characterized RiPPs similar to a given peptide sequence or a given chemical structure.
Database: RiPPMiner derives its predictive power from training using a manually curated database of more than 500+ experimentally characterized RiPPs belonging to 13 Subclasses. These Classes include Lanthipeptide, Bottromycin, Cyanobactin, Glycocin, Lassopeptide, Linaridin, Linearazol, Microcin, Sactipeptide, Thiopeptide, Auto Inducing Peptide, ComX, Bacterial Head to Tail Cyclized. Each RiPP has information like Modification System, Complete Sequence, Leader and Core Sequence, modified residues, Cross-links, Pubmed, PDB ID, Gene Cluster among others.
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RiPPMiner-Peptide It takes RiPP precursor peptide sequence as input and uses SVM to classify the precursor into nine sub-classes of RiPPs. For classes like Lanthipeptide, Cyanobactin and Lassopeptide, RiPPMiner predicts the leader cleavage site and finally complex crosslinking and post-translationally modified residues are predicted in the core peptide. For Thiopeptide it predicts crosslinking and modified residues. RiPPMiner can identify correct PTM and crosslink pattern in a query RiPP core peptide from among a very large number of combinatorial possibilities using machine learning. RiPPMiner also provides GUI for visualization of the predicted chemical structure and also search for characterized RiPPs similar to a given peptide sequence or a given chemical structure.
Database: RiPPMiner derives its predictive power from training using a manually curated database of more than 500+ experimentally characterized RiPPs belonging to 13 Subclasses. These Classes include Lanthipeptide, Bottromycin, Cyanobactin, Glycocin, Lassopeptide, Linaridin, Linearazol, Microcin, Sactipeptide, Thiopeptide, Auto Inducing Peptide, ComX, Bacterial Head to Tail Cyclized. Each RiPP has information like Modification System, Complete Sequence, Leader and Core Sequence, modified residues, Cross-links, Pubmed, PDB ID, Gene Cluster among others.