Phogly–PseAAC: Prediction of lysine phosphoglycerylation in proteins incorporating with position-specific propensity. (21st August 2015)

• Record Type: Journal Article
• Authors: Xu, Yan; Ding, Ya-Xin; Ding, Jun; Wu, Ling-Yun; Deng, Nai-Yang
• Is Part Of: Journal of theoretical biology. Volume 379(2015)
• Page Start: 10
• Page End: 15
• Publication Date: 2015-08-21
• Subjects: Amino acid propensity -- F-score -- Phosphoglycerylation; Biology -- Periodicals; Biological Science Disciplines -- Periodicals; Biology -- Periodicals; Biologie -- Périodiques; Theoretische biologie; Biology; Periodicals; 571.05
• DOI: 10.1016/j.jtbi.2015.04.016
• Languages: English
• ISSNs: 0022-5193

Abstract

Abstract: Large-scale characterization of post-translational modifications (PTMs), such as posphorylation, acetylation and ubiquitination, has highlighted their importance in the regulation of a myriad of signaling events. However, as another type of PTMs–lysine phosphoglycerylation, the data of phosphoglycerylated sites has just been manually experimented in recent years. Given an uncharacterized protein sequence that contains many lysine residues, which one of them can be phosphoglycerylated and which one not? This is a challenging problem. In view of this, establishing a useful computational method and developing an efficient predictor are highly desired. Here a new predictor named Phogly–PseAAC was developed which incorporated with the position specific amino acid propensity. The feature importance through F -score value has also been ranked. The predictor with the best feature set obtained the accuracy 75.10%, sensitivity 68.87%, specificity 75.57% and MCC 0.2538 in LOO test cross validation with center nearest neighbor algorithm. Meanwhile, a web-server for Phogly–PseAAC is accessible athttp://app.aporc.org/Phogly-PseAAC/ . For the convenience of most experimental scientists, we have further provided a brief instruction for the web-server, by which users can easily get their desired results without the need to follow the complicated mathematics presented in this paper. It is anticipated that Phogly–PseAAC may become a useful high throughput tool for identifying the lysine phosphoglycerylation sites. Highlights: A new predictor for lysine phosphoglycerylation in proteins was developed. Index Feature Score (IFS) curve was obtained with the optimal 13 features. A web-server for the predictor is accessible athttp://app.aporc.org/Phogly-PseAAC/ .