Prediction of Short Linear Protein Binding Regions

JOURNAL OF MOLECULAR BIOLOGY

卷:415

Article

Mooney, Catherine^1,2,3  Pollastri, Gianluca^1,4  Shields, Denis C.^1,2,3  Haslam, Niall J.^1,2,3 

[1] Univ Coll Dublin, Complex & Adapt Syst Lab, Dublin 4, Ireland

[2] Univ Coll Dublin, Conway Inst Biomol & Biomed Sci, Dublin 4, Ireland

[3] Univ Coll Dublin, Sch Med & Med Sci, Dublin 4, Ireland

[4] Univ Coll Dublin, Sch Comp Sci & Informat, Dublin 4, Ireland

关键词: intrinsically unstructured proteins;    molecular recognition;    protein-protein interface;    linear motif;    peptide binding;   

DOI  :  10.1016/j.jmb.2011.10.025

来源: Elsevier

【 摘 要 】

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. (C) 2011 Elsevier Ltd. All rights reserved.

【 授权许可】

Free