Mooney, CatherineCatherineMooneyPollastri, GianlucaGianlucaPollastriShields, Denis C.Denis C.ShieldsHaslam, Niall J.Niall J.Haslam2011-12-122011-12-122011 Elsev2012-01-06Journal of Molecular Biology0022-2836http://hdl.handle.net/10197/3395Short linear motifs in proteins (typically 3–12 residues in length) play key roles in protein–protein interactions by frequently binding specifically to peptide binding domains within interacting proteins. Their tendency to be found in disordered segments of proteins has meant that they have often been overlooked. Here we present SLiMPred (short linear motif predictor), the first general de novo method designed to computationally predict such regions in protein primary sequences independent of experimentally defined homologs and interactors. The method applies machine learning techniques to predict new motifs based on annotated instances from the Eukaryotic Linear Motif database, as well as structural, biophysical, and biochemical features derived from the protein primary sequence. We have integrated these data sources and benchmarked the predictive accuracy of the method, and found that it performs equivalently to a predictor of protein binding regions in disordered regions, in addition to having predictive power for other classes of motif sites such as polyproline II helix motifs and short linear motifs lying in ordered regions. It will be useful in predicting peptides involved in potential protein associations and will aid in the functional characterization of proteins, especially of proteins lacking experimental information on structures and interactions. We conclude that, despite the diversity of motif sequences and structures, SLiMPred is a valuable tool for prioritizing potential interaction motifs in proteins.430805 bytesapplication/pdfenThis is the author’s version of a work that was accepted for publication in Journal of Molecular Biology. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Journal of Molecular Biology, IN PRESS DOI: 10.1016/j.jmb.2011.10.025Intrinsically unstructured proteinsMolecular recognitionProtein–protein interfaceLinear motifBRNNNeural networkFunctional predictionPeptide bindingMini-motifProteins--StructureMolecular recognitionProtein-protein interactionsProteins--Research--Data processingNeural networks (Computer science)Journal Article415119320410.1016/j.jmb.2011.10.025https://creativecommons.org/licenses/by-nc-sa/1.0/