Incorporating structural characteristics for identification of protein methylation sites

ASIAIR > College of Computer Science > Department of Biomedical informatics > Journal Article > Item 310904400/8649

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Title:	Incorporating structural characteristics for identification of protein methylation sites
Authors:	Shien, Dray-Ming;Lee, Tzong-Yi;Chang, Wen-Chi;Hsu, Justin Bo-Kai;Horng, Jorng-Tzong;Hsu, Po-Chiang;Wang, Ting-Yuan;Huang, Hsien-Da
Contributors:	Department of Bioinformatics
Keywords:	Amines;Amino acids;Image retrieval;Methylation;Organic acids;Proteins;Solvents;Support vector machines;Transcription;Cross validation;Gene transcriptions;Identification of proteins;Prediction accuracy;Prediction tools;Predictive performance;Protein data bank;Protein tertiary structures;Solvent accessible surface area (ASA);Solvent-accessible surface area;Structural characteristics;Support vector machine (SVM);Web servers
Date:	2009-07
Issue Date:	2010-04-07 13:21:17 (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://MASA.mbc.nctu.edu.tw/. © 2009 Wiley Periodicals, Inc.
Relation:	Journal of Computational Chemistry 30(9):1532-1543
Appears in Collections:	[Department of Biomedical informatics ] Journal Article

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