Prediction of short linear protein binding regions

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dc.contributor.author Mooney, Catherine
dc.contributor.author Pollastri, Gianluca
dc.contributor.author Shields, Denis C.
dc.contributor.author Haslam, Niall J.
dc.date.accessioned 2011-12-12T11:44:59Z
dc.date.available 2011-12-12T11:44:59Z
dc.date.copyright 2011 Elsevier Ltd en
dc.date.issued 2012-01-06
dc.identifier.citation Journal of Molecular Biology en
dc.identifier.issn 0022-2836
dc.identifier.uri http://hdl.handle.net/10197/3395
dc.description.abstract Short 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. en
dc.description.sponsorship Science Foundation Ireland en
dc.format.extent 430805 bytes
dc.format.mimetype application/pdf
dc.language.iso en en
dc.publisher Elsevier en
dc.relation.requires Conway Institute Research Collection en
dc.rights This 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.2011.10.025 en
dc.subject Intrinsically unstructured proteins en
dc.subject Molecular recognition en
dc.subject Protein–protein interface en
dc.subject Linear motif en
dc.subject BRNN en
dc.subject Neural network en
dc.subject Functional prediction en
dc.subject Peptide binding en
dc.subject Mini-motif en
dc.subject.lcsh Proteins--Structure en
dc.subject.lcsh Molecular recognition en
dc.subject.lcsh Protein-protein interactions en
dc.subject.lcsh Proteins--Research--Data processing en
dc.subject.lcsh Neural networks (Computer science) en
dc.title Prediction of short linear protein binding regions en
dc.type Journal Article en
dc.internal.availability Full text available en
dc.internal.webversions http://dx.doi.org/10.1016/j.jmb.2011.10.025
dc.status Peer reviewed en
dc.identifier.volume 415 en
dc.identifier.issue 1 en
dc.identifier.startpage 193 en
dc.identifier.endpage 204 en
dc.identifier.doi 10.1016/j.jmb.2011.10.025
dc.neeo.contributor Mooney|Catherine|aut|
dc.neeo.contributor Pollastri|Gianluca|aut|
dc.neeo.contributor Shields|Denis C.|aut|
dc.neeo.contributor Haslam|Niall J.|aut|
dc.description.admin au, ti, ke, - kpw30/11/11 en
dc.internal.rmsid 242472655


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