title: Position-specific prediction of methylation sites from sequence conservation based on information theory.
abstract: Protein methylation plays vital roles in many biological processes and has been implicated in various human diseases. To fully understand the mechanisms underlying methylation for use in drug design and work in methylation-related diseases, an initial but crucial step is to identify methylation sites. The use of high-throughput bioinformatics methods has become imperative to predict methylation sites. In this study, we developed a novel method that is based only on sequence conservation to predict protein methylation sites. Conservation difference profiles between methylated and non-methylated peptides were constructed by the information entropy (IE) in a wider neighbor interval around the methylation sites that fully incorporated all of the environmental information. Then, the distinctive neighbor residues were identified by the importance scores of information gain (IG). The most representative model was constructed by support vector machine (SVM) for Arginine and Lysine methylation, respectively. This model yielded a promising result on both the benchmark dataset and independent test set. The model was used to screen the entire human proteome, and many unknown substrates were identified. These results indicate that our method can serve as a useful supplement to elucidate the mechanism of protein methylation and facilitate hypothesis-driven experimental design and validation.
authors.affiliation: College of Chemistry, Sichuan University, Chengdu, Sichuan 610064, PR China.
authors.forename: Yinan
authors.initials: Y
authors.lastname: Shi
authors.affiliation: College of Chemistry, Sichuan University, Chengdu, Sichuan 610064, PR China.
authors.forename: Yanzhi
authors.initials: Y
authors.lastname: Guo
authors.affiliation: College of Chemistry, Sichuan University, Chengdu, Sichuan 610064, PR China.
authors.forename: Yayun
authors.initials: Y
authors.lastname: Hu
authors.affiliation: College of Chemistry, Sichuan University, Chengdu, Sichuan 610064, PR China.
authors.forename: Menglong
authors.initials: M
authors.lastname: Li
mesh_terms: D000595:Amino Acid Sequence; D000596:Amino Acids / Q000737:chemistry*; D001665:Binding Sites; D002874:Chromosome Mapping / Q000379:methods*; D017124:Conserved Sequence / Q000235:genetics*; D030562:Databases, Protein; D007257:Information Theory; D008745:Methylation; D008969:Molecular Sequence Data; D011485:Protein Binding; D020543:Proteome / Q000737:chemistry* / Q000235:genetics*; D020539:Sequence Analysis, Protein / Q000379:methods*
chemical_list: D000596:Amino Acids; D020543:Proteome