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    Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/29125


    Title: Incorporating Support Vector Machine for Identifying Protein Tyrosine Sulfation Sites
    Authors: Chang,WC;Lee,TY;Shien,DM;Hsu,JBK;Horng,JT;Hsu,PC;Wang,TY;Huang,HD;Pan,RL
    Contributors: 資訊工程研究所
    Keywords: O-SULFATION;ELECTROSTATIC POTENTIALS;MASS-SPECTROMETRY;PREDICTION;PHOSPHORYLATION;SEQUENCES;BINDING;RECOGNITION;RECEPTORS;PEPTIDES
    Date: 2009
    Issue Date: 2010-06-29 20:14:19 (UTC+8)
    Publisher: 中央大學
    Abstract: Tyrosine sulfation is a post-translational modification of many secreted and membrane-bound proteins. It governs protein-protein interactions that are involved in leukocyte adhesion, hemostasis, and chemokine signaling. However, the intrinsic feature of sulfated protein remains elusive and remains to be delineated. This investigation presents SulfoSite, which is a computational method based on a support vector machine (SVM) for predicting protein sulfotyrosine sites. The approach was developed to consider structural information such as concerning the secondary structure and solvent accessibility of amino acids that surround the sulfotyrosine sites. One hundred sixty-two experimentally verified tyrosine sulfation sites were identified using UniProtKB/SwissProt release 53.0. The results of a five-fold cross-validation evaluation suggest that the accessibility of the solvent around the sulfotyrosine sites contributes substantially to predictive accuracy. The SVM classifier can achieve an accuracy of 94.2% in fivefold cross validation when sequence positional weighted matrix (PWM) is coupled with values of the accessible surface area (ASA). The proposed method significantly outperforms previous methods for accurately predicting the location of tyrosine sulfation sites. (C) 2009 Wiley Periodicals, Inc. J Comput Chem 30: 2526-2537, 2009
    Relation: JOURNAL OF COMPUTATIONAL CHEMISTRY
    Appears in Collections:[資訊工程研究所] 期刊論文

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