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dc.contributor.authorChang, Wen-Chien_US
dc.contributor.authorLee, Tzong-Yien_US
dc.contributor.authorShien, Dray-Mingen_US
dc.contributor.authorHsu, Justin Bo-Kaien_US
dc.contributor.authorHorng, Jorng-Tzongen_US
dc.contributor.authorHsu, Po-Chiangen_US
dc.contributor.authorWang, Ting-Yuanen_US
dc.contributor.authorHuang, Hsien-Daen_US
dc.contributor.authorPan, Rong-Longen_US
dc.date.accessioned2014-12-08T15:08:13Z-
dc.date.available2014-12-08T15:08:13Z-
dc.date.issued2009-11-30en_US
dc.identifier.issn0192-8651en_US
dc.identifier.urihttp://dx.doi.org/10.1002/jcc.21258en_US
dc.identifier.urihttp://hdl.handle.net/11536/6406-
dc.description.abstractTyrosine 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, 2009en_US
dc.language.isoen_USen_US
dc.subjectproteinen_US
dc.subjectsulfationen_US
dc.subjectpredictionen_US
dc.titleIncorporating Support Vector Machine for Identifying Protein Tyrosine Sulfation Sitesen_US
dc.typeArticleen_US
dc.identifier.doi10.1002/jcc.21258en_US
dc.identifier.journalJOURNAL OF COMPUTATIONAL CHEMISTRYen_US
dc.citation.volume30en_US
dc.citation.issue15en_US
dc.citation.spage2526en_US
dc.citation.epage2537en_US
dc.contributor.department生物科技學系zh_TW
dc.contributor.department生物資訊及系統生物研究所zh_TW
dc.contributor.departmentDepartment of Biological Science and Technologyen_US
dc.contributor.departmentInstitude of Bioinformatics and Systems Biologyen_US
dc.identifier.wosnumberWOS:000270869600014-
dc.citation.woscount26-
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