Incorporating Structural Characteristics for Identification of Protein Methylation Sites

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Please use this identifier to cite or link to this item: http://asiair.asia.edu.tw/ir/handle/310904400/8533

Title:	Incorporating Structural Characteristics for Identification of Protein Methylation Sites
Authors:	Shien, DM (Shien, Dray-Ming);Lee, TY (Lee, Tzong-Yi);Chang, WC (Chang, Wen-Chi);Hsu, JBK (Hsu, Justin Bo-Kai);Horng, JT (Horng, Jorng-Tzong);Hsu, PC (Hsu, Po-Chiang);Wang, TY (Wang, Ting-Yuan);Huang, HD (Huang, Hsien-Da)
Contributors:	Department of Bioinformatics
Keywords:	protein methylation;solvent accessible surface area (ASA);support vector machine (SVM);HIDDEN MARKOV-MODELS;POSTTRANSLATIONAL MODIFICATIONS;PHOSPHORYLATION SITES;ENZYMATIC METHYLATION;HISTONE METHYLATION;PREDICTION;SEQUENCE;LYSINE;ACCESSIBILITY;DATABASE
Date:	2009-07
Issue Date:	2010-03-26 02:56:41 (UTC+0)
Publisher:	Asia University
Abstract:	Studies over the last few years have identified protein methylation on histones and other proteins that are involved in the regulation of gene transcription. Several works have developed approaches to identify computationally the potential methylation sites on lysine and arginine. Studies of protein tertiary structure have demonstrated that the sites of protein methylation are preferentially in regions that are easily accessible. However, previous studies have not taken into account the solvent-accessible surface area (ASA) that surrounds the methylation sites. This work presents a method named MASA that combines the support vector machine with the sequence and structural characteristics of proteins to identify methylation sites on lysine, arginine, glutamate, and asparagine. Since most experimental methylation sites are not associated with corresponding protein tertiary structures in the Protein Data Bank, the effective solvent-accessible prediction tools have been adopted to determine the potential ASA values of amino acids in proteins. Evaluation of predictive performance by cross-validation indicates that the ASA values around the methylation sites can improve the accuracy of prediction. Additionally, an independent test reveals that the prediction accuracies for methylated lysine and arginine are 80.8 and 85.0%, respectively. Finally, the proposed method is implemented as an effective system for identifying protein methylation sites. The developed web server is freely available at http:/IMASA.mbc.nctu.edu.tw/. (C) 2009 Wiley Periodicals, Inc. J Comput Chem 30: 1532-1543, 2009
Relation:	JOURNAL OF COMPUTATIONAL CHEMISTRY, 30 (9): 1532-1543
Appears in Collections:	[生物資訊與醫學工程學系 ] 期刊論文

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