Prediction of S-glutathionylation sites based on protein sequences - PubMed (original) (raw)

Prediction of S-glutathionylation sites based on protein sequences

Chenglei Sun et al. PLoS One. 2013.

Abstract

S-glutathionylation, the reversible formation of mixed disulfides between glutathione(GSH) and cysteine residues in proteins, is a specific form of post-translational modification that plays important roles in various biological processes, including signal transduction, redox homeostasis, and metabolism inside cells. Experimentally identifying S-glutathionylation sites is labor-intensive and time consuming, whereas bioinformatics methods provide an alternative way to this problem by predicting S-glutathionylation sites in silico. The bioinformatics approaches give not only candidate sites for further experimental verification but also bio-chemical insights into the mechanism of S-glutathionylation. In this paper, we firstly collect experimentally determined S-glutathionylated proteins and their corresponding modification sites from the literature, and then propose a new method for predicting S-glutathionylation sites by employing machine learning methods based on protein sequence data. Promising results are obtained by our method with an AUC (area under ROC curve) score of 0.879 in 5-fold cross-validation, which demonstrates the predictive power of our proposed method. The datasets used in this work are available at http://csb.shu.edu.cn/SGDB.

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Conflict of interest statement

Competing Interests: The authors have declared that no competing interests exist.

Figures

Figure 1

Figure 1. The ROC curves for different classifiers based on different feature extraction approaches.

ThrAA: triplet amino acid composition; Phyche:10 physicochemical properties; RedAA: reduced amino acid composition; Bbayes: Bi-profile Bayes; BinAA: binary encoding amino acid; SinAA: amino acid composition; PaiAA: pair amino acid composition.

Figure 2

Figure 2. The effects of different window sizes on SVM performance.

Figure 3

Figure 3. Distributions of thiol ASA values of _S_-glutathionylation cysteine and non-_S_-glutathionylation cysteine.

(a) The points on the curve mean the percentage of the samples that have an ASA formula image the corresponding ASA value. (b) Box plots of thiol ASA values.

Figure 4

Figure 4. Distributions of thiol pKa values of _S_-glutathionylation cysteine and non-_S_-glutathionylation cysteine.

(a) Thiol pKa value distributions. (b) Box plots of thiol pKa values.

Figure 5

Figure 5. Graphical overview of the method for prediction of protein _S_-glutathionylation sites.

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