Incorporating structural characteristics for identification of protein methylation sites

Dray Ming Shien, Tzong Yi Lee, Wen-Chi Chang, Justin Bo Kai Hsu, Jorng Tzong Horng, Po Chiang Hsu, Ting Yuan Wang, Hsien Da Huang

Research output: Contribution to journalArticlepeer-review

83 Citations (Scopus)

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/.

Original languageEnglish
Pages (from-to)1532-1543
Number of pages12
JournalJournal of Computational Chemistry
Volume30
Issue number9
DOIs
Publication statusPublished - 2009 Jul 15

All Science Journal Classification (ASJC) codes

  • Chemistry(all)
  • Computational Mathematics

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