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dc.contributor.authorLee, Tzong-Yien_US
dc.contributor.authorHsu, Justin Bo-Kaien_US
dc.contributor.authorLin, Feng-Maoen_US
dc.contributor.authorChang, Wen-Chien_US
dc.contributor.authorHsu, Po-Chiangen_US
dc.contributor.authorHuang, Hsien-Daen_US
dc.date.accessioned2019-04-02T05:58:36Z-
dc.date.available2019-04-02T05:58:36Z-
dc.date.issued2010-11-30en_US
dc.identifier.issn0192-8651en_US
dc.identifier.urihttp://dx.doi.org/10.1002/jcc.21569en_US
dc.identifier.urihttp://hdl.handle.net/11536/150052-
dc.description.abstractProtein acetylation, which is catalyzed by acetyltransferases, is a type of post-translational modification and crucial to numerous essential biological processes, including transcriptional regulation, apoptosis, and cytokine signaling. As the experimental identification of protein acetylation sites is time consuming and laboratory intensive, several computational approaches have been developed for identifying the candidates of experimental validation. In this work, solvent accessibility and the physicochemical properties of proteins are utilized to identify acetylated alanine, glycine, lysine, methionine, serine, and threonine. A two-stage support vector machine was applied to learn the computational models with combinations of amino acid sequences, and the accessible surface area and physicochemical properties of proteins. The predictive accuracy thus achieved is 5% to 14% higher than that of models trained using only amino acid sequences. Additionally, the substrate specificity of the acetylated site was investigated in detail with reference to the subcellular colocalization of acetyltransferases and acetylated proteins. The proposed method, N-Ace, is evaluated using independent test sets in various acetylated residues and predictive accuracies of 90% were achieved, indicating that the performance of N-Ace is comparable with that of other acetylation prediction methods. N-Ace not only provides a user-friendly input/output interface but also is a creative method for predicting protein acetylation sites. This novel analytical resource is now freely available at http://N-Ace.mbc.NCTU.edu.tw/. (C) 2010 Wiley Periodicals, Inc. J Comput Chem 31: 2759-2771, 2010en_US
dc.language.isoen_USen_US
dc.subjectprotein acetylationen_US
dc.subjectacetyltransferaseen_US
dc.subjectaccessible surface areaen_US
dc.subjectphysicochemical propertiesen_US
dc.subjectsupport vector machineen_US
dc.titleN-Ace: Using Solvent Accessibility and Physicochemical Properties to Identify Protein N-Acetylation Sitesen_US
dc.typeArticleen_US
dc.identifier.doi10.1002/jcc.21569en_US
dc.identifier.journalJOURNAL OF COMPUTATIONAL CHEMISTRYen_US
dc.citation.volume31en_US
dc.citation.spage2759en_US
dc.citation.epage2771en_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.department分子醫學與生物工程研究所zh_TW
dc.contributor.departmentDepartment of Biological Science and Technologyen_US
dc.contributor.departmentInstitude of Bioinformatics and Systems Biologyen_US
dc.contributor.departmentInstitute of Molecular Medicine and Bioengineeringen_US
dc.identifier.wosnumberWOS:000282309900008en_US
dc.citation.woscount39en_US
Appears in Collections:Articles