Computational prediction of methylation types of covalently modified lysine and arginine residues in proteins. (28th May 2016)

• Record Type: Journal Article
• Authors: Deng, Wankun; Wang, Yongbo; Ma, Lili; Zhang, Ying; Ullah, Shahid; Xue, Yu
• Is Part Of: Briefings in bioinformatics. Volume 18:Number 4(2017:Jul.)
• Page Start: 647
• Page End: 658
• Publication Date: 2016-05-28
• Subjects: protein methylation -- post-translational modification -- methyllysine -- methylarginine -- methylation type; Genetics -- Data processing -- Periodicals; Molecular biology -- Data processing -- Periodicals; Genomes -- Data processing -- Periodicals; 572.80285
• DOI: 10.1093/bib/bbw041
• Languages: English
• ISSNs: 1467-5463

Abstract

Abstract: Protein methylation is an essential posttranslational modification (PTM) mostly occurs at lysine and arginine residues, and regulates a variety of cellular processes. Owing to the rapid progresses in the large-scale identification of methylation sites, the available data set was dramatically expanded, and more attention has been paid on the identification of specific methylation types of modification residues. Here, we briefly summarized the current progresses in computational prediction of methylation sites, which provided an accurate, rapid and efficient approach in contrast with labor-intensive experiments. We collected 5421 methyllysines and methylarginines in 2592 proteins from the literature, and classified most of the sites into different types. Data analyses demonstrated that different types of methylated proteins were preferentially involved in different biological processes and pathways, whereas a unique sequence preference was observed for each type of methylation sites. Thus, we developed a predictor of GPS-MSP, which can predict mono-, di- and tri-methylation types for specific lysines, and mono-, symmetric di- and asymmetrical di-methylation types for specific arginines. We critically evaluated the performance of GPS-MSP, and compared it with other existing tools. The satisfying results exhibited that the classification of methylation sites into different types for training can considerably improve the prediction accuracy. Taken together, we anticipate that our study provides a new lead for future computational analysis of protein methylation, and the prediction of methylation types of covalently modified lysine and arginine residues can generate more useful information for further experimental manipulation.