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    A machine learning approach for the prediction of protein surface loop flexibility

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    Authors
    Hwang, Howook
    Vreven, Thom
    Whitfield, Troy W.
    Wiehe, Kevin
    Weng, Zhiping
    UMass Chan Affiliations
    Program in Bioinformatics and Integrative Biology
    Department of Biochemistry and Molecular Pharmacology
    Document Type
    Journal Article
    Publication Date
    2011-08-01
    Keywords
    *Artificial Intelligence
    Protein Structure, Secondary
    Proteins
    Amino Acids, Peptides, and Proteins
    Bioinformatics
    Computational Biology
    Systems Biology
    
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    Link to Full Text
    http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3341935/
    Abstract
    Proteins often undergo conformational changes when binding to each other. A major fraction of backbone conformational changes involves motion on the protein surface, particularly in loops. Accounting for the motion of protein surface loops represents a challenge for protein-protein docking algorithms. A first step in addressing this challenge is to distinguish protein surface loops that are likely to undergo backbone conformational changes upon protein-protein binding (mobile loops) from those that are not (stationary loops). In this study, we developed a machine learning strategy based on support vector machines (SVMs). Our SVM uses three features of loop residues in the unbound protein structures-Ramachandran angles, crystallographic B-factors, and relative accessible surface area-to distinguish mobile loops from stationary ones. This method yields an average prediction accuracy of 75.3% compared with a random prediction accuracy of 50%, and an average of 0.79 area under the receiver operating characteristic (ROC) curve using cross-validation. Testing the method on an independent dataset, we obtained a prediction accuracy of 70.5%. Finally, we applied the method to 11 complexes that involve members from the Ras superfamily and achieved prediction accuracy of 92.8% for the Ras superfamily proteins and 74.4% for their binding partners.
    Source

    Proteins. 2011 Aug;79(8):2467-74. doi: 10.1002/prot.23070. Epub 2011 Jun 1. Link to article on publisher's site

    DOI
    10.1002/prot.23070
    Permanent Link to this Item
    http://hdl.handle.net/20.500.14038/25883
    PubMed ID
    21633973
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    ae974a485f413a2113503eed53cd6c53
    10.1002/prot.23070
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