Issue 3, 2015

PGluS: prediction of protein S-glutathionylation sites with multiple features and analysis

Abstract

S-Glutathionylation is a reversible protein post-translational modification, which generates mixed disulfides between glutathione (GSH) and cysteine residues, playing an important role in regulating protein stability, activity, and redox regulation. To fully understand S-glutathionylation mechanisms, identification of substrates and specific S-glutathionylated sites is crucial. Compared with the labor-intensive and time-consuming experimental approaches, computational predictions of S-glutathionylated sites are very desirable due to their convenience and high speed. Therefore, in this study, a new bioinformatics tool named PGluS was developed to predict S-glutathionylated sites based on multiple features and support vector machines. The performance of PGluS was measured with an accuracy of 71.41% and a MCC of 0.431 using the 5-fold cross-validation on the training dataset. Additionally, PGluS was evaluated using an independent testing dataset resulting in an accuracy of 71.25%, which demonstrated that PGluS was very promising for predicting S-glutathionylated sites. Furthermore, feature analysis was performed and it was shown that all features adopted in this method contributed to the S-glutathionylation process. A site-specific analysis showed that S-glutathionylation was intimately correlated with the features derived from its surrounding sites. The conclusions derived from this study might help to understand more of the S-glutathionylation mechanism and guide the related experimental validation. For public access, PGluS is freely accessible at http://59.73.198.144:8088/PGluS/.

Graphical abstract: PGluS: prediction of protein S-glutathionylation sites with multiple features and analysis

Supplementary files

Article information

Article type
Paper
Submitted
23 Nov 2014
Accepted
08 Jan 2015
First published
09 Jan 2015

Mol. BioSyst., 2015,11, 923-929

PGluS: prediction of protein S-glutathionylation sites with multiple features and analysis

X. Zhao, Q. Ning, M. Ai, H. Chai and M. Yin, Mol. BioSyst., 2015, 11, 923 DOI: 10.1039/C4MB00680A

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