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    Accurate prediction of hot spot residues through physicochemical characteristics of amino acid sequences

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    Type
    Article
    Authors
    Chen, Peng
    Li, Jinyan
    Limsoon, Wong
    Kuwahara, Hiroyuki cc
    Huang, Jianhua Z.
    Gao, Xin cc
    KAUST Department
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Computational Bioscience Research Center (CBRC)
    Computer Science Program
    Structural and Functional Bioinformatics Group
    KAUST Grant Number
    GRP-CF-2011-19-P-Gao-Huang
    KUS-CI-016-04
    Date
    2013-07-23
    Online Publication Date
    2013-07-23
    Print Publication Date
    2013-08
    Permanent link to this record
    http://hdl.handle.net/10754/562868
    
    Metadata
    Show full item record
    Abstract
    Hot spot residues of proteins are fundamental interface residues that help proteins perform their functions. Detecting hot spots by experimental methods is costly and time-consuming. Sequential and structural information has been widely used in the computational prediction of hot spots. However, structural information is not always available. In this article, we investigated the problem of identifying hot spots using only physicochemical characteristics extracted from amino acid sequences. We first extracted 132 relatively independent physicochemical features from a set of the 544 properties in AAindex1, an amino acid index database. Each feature was utilized to train a classification model with a novel encoding schema for hot spot prediction by the IBk algorithm, an extension of the K-nearest neighbor algorithm. The combinations of the individual classifiers were explored and the classifiers that appeared frequently in the top performing combinations were selected. The hot spot predictor was built based on an ensemble of these classifiers and to work in a voting manner. Experimental results demonstrated that our method effectively exploited the feature space and allowed flexible weights of features for different queries. On the commonly used hot spot benchmark sets, our method significantly outperformed other machine learning algorithms and state-of-the-art hot spot predictors. The program is available at http://sfb.kaust.edu.sa/pages/software.aspx. © 2013 Wiley Periodicals, Inc.
    Sponsors
    Grant sponsor: King Abdullah University of Science and Technology (KAUST); Grand numbers: KUS-CI-016-04; GRP-CF-2011-19-P-Gao-Huang.
    Publisher
    Wiley
    Journal
    Proteins: Structure, Function, and Bioinformatics
    DOI
    10.1002/prot.24278
    PubMed ID
    23504705
    ae974a485f413a2113503eed53cd6c53
    10.1002/prot.24278
    Scopus Count
    Collections
    Articles; Structural and Functional Bioinformatics Group; Computer Science Program; Computational Bioscience Research Center (CBRC); Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

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