SLiMScape 3.x: a Cytoscape 3 app for discovery of Short Linear Motifs in protein interaction networks

Emily Olorin, Kevin T. O'Brien, Nicolas Palopoli, Åsa Pérez-Bercoff, Denis C. Shields, Richard J. Edwards
<span title="2015-08-05">2015</span> <i title="F1000 Research, Ltd."> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/lz7xw3k77zcg5f66jhfurufu64" style="color: black;">F1000Research</a> </i> &nbsp;
Short linear motifs (SLiMs) are small protein sequence patterns that mediate a large number of critical protein-protein interactions, involved in processes such as complex formation, signal transduction, localisation and stabilisation. SLiMs show rapid evolutionary dynamics and are frequently the targets of molecular mimicry by pathogens. Identifying enriched sequence patterns due to convergent evolution in non-homologous proteins has proven to be a successful strategy for computational SLiM
more &raquo; ... diction. Tools of the SLiMSuite package use this strategy, using a statistical model to identify SLiM enrichment based on the evolutionary relationships, amino acid composition and predicted disorder of the input proteins. The quality of input data is critical for successful SLiM prediction. Cytoscape provides a user-friendly, interactive environment to explore interaction networks and select proteins based on common features, such as shared interaction partners. SLiMScape embeds tools of the SLiMSuite package for SLiM discovery (SLiMFinder and QSLiMFinder) and de novo identifying occurrences/enrichment of known SLiMs (SLiMProb) within this interactive framework. SLiMScape makes it easier to (1) generate high quality hypothesis-driven datasets for these tools, and (2) visualise predicted SLiM occurrences within the context of the network. To generate new predictions, users can select nodes from a protein network or provide a set of Uniprot identifiers. SLiMProb also requires additional query motif input. Jobs are then run remotely on the SLiMSuite server ( ) for http://rest.slimsuite.unsw.edu.au subsequent retrieval and visualisation. SLiMScape can also be used to retrieve and visualise results from jobs run directly on the server. SLiMScape and SLiMSuite are open source and freely available via GitHub under GNU licenses. PubMed Abstract | Publisher Full Text 2. Edwards RJ, Palopoli N: Computational prediction of short linear motifs from protein sequences. Methods Mol Biol. 2015; 1268: 89-141. PubMed Abstract | Publisher Full Text 3. Kelil A, Dubreuil B, Levy ED, et al.: Fast and accurate discovery of degenerate linear motifs in protein sequences. PLoS One. 2014; 9(9): e106081. PubMed Abstract | Publisher Full Text | Free Full Text 4. Davey NE, Van Roey K, Weatheritt RJ, et al.: Attributes of short linear motifs. Mol Biosyst. 2012; 8(1): 268-81. PubMed Abstract | Publisher Full Text 5. 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