Allows users to extract significant motifs from large data sets, like groups of proteins that share a common biological function and tandem mass spectrometry (MS/MS) post-translational modification data. Motif-x can extract over-represented patterns from any sequence data set. It permits users to import their data and also to download results that use Web browsers on essentially any Web-compatible computer.
Simplifies the recognition of large sets of spectra and improves the flexibility of post-translational modification (PTM) searching. DBDigger is implemented for single-pass, rapid scoring of potential matches. The software discovers peptides from multiple isotopic distributions in a single pass. It automates the determination of the mass for each amino acid residue based on the specified isotopic distributions.
Allows users to develop systematic searches for proteins of interest and explore network visualisations that address complex post-translational modifications of proteins (PTM)-associated relationships. PTMOracle is a Cytoscape app for co-analysing PTMs within protein-protein interaction (PPI) networks. It also allows extensive data to be integrated and co-analysed with PPI networks, allowing the role of domains, motifs and disordered regions to be considered. For proteins of interest, or a whole proteome, PTMOracle can generate network visualisations to reveal complex PTM-associated relationships.
Supports localization of Post-Translational Modifications (PTMs) results from multiple search engines. ModLS is a method that operates on meta-search results combining output from these search engines. ModLS can also perform localization scoring for any PTM selected as a variable modification in a search, from those defined in the comprehensive UniMod database. ModLS is part of the Central Proteomics Facilities Pipeline.
Predicts whether sites of protein modifications are inside or outside of protein-protein interacting regions (PPIRs), based on the existing structural and post-translational modifications (PTM) databases. PtmPPIR showed high performance measures, and important features contributing to predictive power were identified. The models were evaluated using 10 independent iterations of 5-fold cross-validation, and the resulting calculated performance measures for the models were high.
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