標題: Incorporating Support Vector Machine for Identifying Protein Tyrosine Sulfation Sites
作者: Chang, Wen-Chi
Lee, Tzong-Yi
Shien, Dray-Ming
Hsu, Justin Bo-Kai
Horng, Jorng-Tzong
Hsu, Po-Chiang
Wang, Ting-Yuan
Huang, Hsien-Da
Pan, Rong-Long
生物科技學系
生物資訊及系統生物研究所
Department of Biological Science and Technology
Institude of Bioinformatics and Systems Biology
關鍵字: protein;sulfation;prediction
公開日期: 30-十一月-2009
摘要: 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
URI: http://dx.doi.org/10.1002/jcc.21258
http://hdl.handle.net/11536/6406
ISSN: 0192-8651
DOI: 10.1002/jcc.21258
期刊: JOURNAL OF COMPUTATIONAL CHEMISTRY
Volume: 30
Issue: 15
起始頁: 2526
結束頁: 2537
顯示於類別:期刊論文


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  1. 000270869600014.pdf