Prediction of short linear protein binding regions

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Title: Prediction of short linear protein binding regions
Authors: Mooney, CatherinePollastri, GianlucaShields, Denis C.Haslam, Niall J.
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Date: 6-Jan-2012
Online since: 2011-12-12T11:44:59Z
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.
Funding Details: Science Foundation Ireland
Type of material: Journal Article
Publisher: Elsevier
Journal: Journal of Molecular Biology
Volume: 415
Issue: 1
Start page: 193
End page: 204
Copyright (published version): 2011 Elsevier Ltd
Keywords: Intrinsically unstructured proteinsMolecular recognitionProtein–protein interfaceLinear motifBRNNNeural networkFunctional predictionPeptide bindingMini-motif
Subject LCSH: Proteins--Structure
Molecular recognition
Protein-protein interactions
Proteins--Research--Data processing
Neural networks (Computer science)
DOI: 10.1016/j.jmb.2011.10.025
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Language: en
Status of Item: Peer reviewed
Appears in Collections:Conway Institute Research Collection
Computer Science Research Collection
CASL Research Collection
Medicine Research Collection

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