title: Identification and characterization of lysine-methylated sites on histones and non-histone proteins.
abstract: Protein methylation is a kind of post-translational modification (PTM), and typically takes place on lysine and arginine amino acid residues. Protein methylation is involved in many important biological processes, and most recent studies focused on lysine methylation of histones due to its critical roles in regulating transcriptional repression and activation. Histones possess highly conserved sequences and are homologous in most species. However, there is much less sequence conservation among non-histone proteins. Therefore, mechanisms for identifying lysine-methylated sites may greatly differ between histones and non-histone proteins. Nevertheless, this point of view was not considered in previous studies. Here we constructed two support vector machine (SVM) models by using lysine-methylated data from histones and non-histone proteins for predictions of lysine-methylated sites. Numerous features, such as the amino acid composition (AAC) and accessible surface area (ASA), were used in the SVM models, and the predictive performance was evaluated using five-fold cross-validations. For histones, the predictive sensitivity was 85.62% and specificity was 80.32%. For non-histone proteins, the predictive sensitivity was 69.1% and specificity was 88.72%. Results showed that our model significantly improved the predictive accuracy of histones compared to previous approaches. In addition, features of the flanking region of lysine-methylated sites on histones and non-histone proteins were also characterized and are discussed. A gene ontology functional analysis of lysine-methylated proteins and correlations of lysine-methylated sites with other PTMs in histones were also analyzed in detail. Finally, a web server, MethyK, was constructed to identify lysine-methylated sites. MethK now is available at http://csb.cse.yzu.edu.tw/MethK/.
authors.affiliation: Department of Computer Science and Engineering, Yuan Ze University, Chung-Li, Taiwan.
authors.forename: Tzong-Yi
authors.initials: TY
authors.lastname: Lee
authors.affiliation: Department of Information Management, Hsing Wu University, New Taipei City, Taiwan.
authors.forename: Cheng-Wei
authors.initials: CW
authors.lastname: Chang
authors.affiliation: Department of Computer Science and Engineering, Yuan Ze University, Chung-Li, Taiwan.
authors.forename: Cheng-Tzung
authors.initials: CT
authors.lastname: Lu
authors.affiliation: Department of Computer Science and Engineering, Yuan Ze University, Chung-Li, Taiwan.
authors.forename: Tzu-Hsiu
authors.initials: TH
authors.lastname: Cheng
authors.affiliation: Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan. Electronic address: kevinchang@tmu.edu.tw.
authors.forename: Tzu-Hao
authors.initials: TH
authors.lastname: Chang
mesh_terms: D000818:Animals; D030562:Databases, Protein; D006801:Humans; D008239:Lysine / Q000378:metabolism*; D008745:Methylation; D051379:Mice; D011499:Protein Processing, Post-Translational; D011506:Proteins / Q000145:classification / Q000378:metabolism*; D060388:Support Vector Machine
chemical_list: D011506:Proteins; D008239:Lysine
keywords: Histone; Lysine; Methylation; Non-histone; PTM; Post-translational modification; SVM; Support vector machine