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  • Sequence-Based Prediction of Protein-Carbohydrate Binding Sites Using Support Vector Machines

    Author(s)
    Taherzadeh, Ghazaleh
    Zhou, Yaoqi
    Liew, Alan Wee-Chung
    Yang, Yuedong
    Griffith University Author(s)
    Liew, Alan Wee-Chung
    Year published
    2016
    Metadata
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    Abstract
    Carbohydrate-binding proteins play significant roles in many diseases including cancer. Here, we established a machine-learning-based method (called sequence-based prediction of residue-level interaction sites of carbohydrates, SPRINT-CBH) to predict carbohydrate-binding sites in proteins using support vector machines (SVMs). We found that integrating evolution-derived sequence profiles with additional information on sequence and predicted solvent accessible surface area leads to a reasonably accurate, robust, and predictive method, with area under receiver operating characteristic curve (AUC) of 0.78 and 0.77 and Matthew’s ...
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    Carbohydrate-binding proteins play significant roles in many diseases including cancer. Here, we established a machine-learning-based method (called sequence-based prediction of residue-level interaction sites of carbohydrates, SPRINT-CBH) to predict carbohydrate-binding sites in proteins using support vector machines (SVMs). We found that integrating evolution-derived sequence profiles with additional information on sequence and predicted solvent accessible surface area leads to a reasonably accurate, robust, and predictive method, with area under receiver operating characteristic curve (AUC) of 0.78 and 0.77 and Matthew’s correlation coefficient of 0.34 and 0.29, respectively for 10-fold cross validation and independent test without balancing binding and nonbinding residues. The quality of the method is further demonstrated by having statistically significantly more binding residues predicted for carbohydrate-binding proteins than presumptive nonbinding proteins in the human proteome, and by the bias of rare alleles toward predicted carbohydrate-binding sites for nonsynonymous mutations from the 1000 genome project. SPRINT-CBH is available as an online server at http://sparks-lab.org/server/SPRINT-CBH.
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    Journal Title
    Journal of Chemical Information and Modeling
    Volume
    56
    Issue
    10
    DOI
    https://doi.org/10.1021/acs.jcim.6b00320
    Subject
    Medicinal and biomolecular chemistry
    Theoretical and computational chemistry
    Theoretical and computational chemistry not elsewhere classified
    Publication URI
    http://hdl.handle.net/10072/143250
    Collection
    • Journal articles

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