期刊
MOLECULAR BIOSYSTEMS
卷 11, 期 3, 页码 923-929出版社
ROYAL SOC CHEMISTRY
DOI: 10.1039/c4mb00680a
关键词
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资金
- National Natural Science Foundation of China [61403077]
- Fundamental Research Funds for the Central Universities [14QNJJ029]
- Post-doctoral Science Foundation of China [2014M550166]
- Jilin Province Science Foundation for Youths [20150520061JH]
- Research Fund for the Doctoral Program of Higher Education of China [20130043110016]
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/.
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