GSHSite: exploiting an iteratively statistical method to identify s-glutathionylation sites with substrate specificity - PubMed (original) (raw)

GSHSite: exploiting an iteratively statistical method to identify s-glutathionylation sites with substrate specificity

Yi-Ju Chen et al. PLoS One. 2015.

Abstract

S-glutathionylation, the covalent attachment of a glutathione (GSH) to the sulfur atom of cysteine, is a selective and reversible protein post-translational modification (PTM) that regulates protein activity, localization, and stability. Despite its implication in the regulation of protein functions and cell signaling, the substrate specificity of cysteine S-glutathionylation remains unknown. Based on a total of 1783 experimentally identified S-glutathionylation sites from mouse macrophages, this work presents an informatics investigation on S-glutathionylation sites including structural factors such as the flanking amino acids composition and the accessible surface area (ASA). TwoSampleLogo presents that positively charged amino acids flanking the S-glutathionylated cysteine may influence the formation of S-glutathionylation in closed three-dimensional environment. A statistical method is further applied to iteratively detect the conserved substrate motifs with statistical significance. Support vector machine (SVM) is then applied to generate predictive model considering the substrate motifs. According to five-fold cross-validation, the SVMs trained with substrate motifs could achieve an enhanced sensitivity, specificity, and accuracy, and provides a promising performance in an independent test set. The effectiveness of the proposed method is demonstrated by the correct identification of previously reported S-glutathionylation sites of mouse thioredoxin (TXN) and human protein tyrosine phosphatase 1b (PTP1B). Finally, the constructed models are adopted to implement an effective web-based tool, named GSHSite (http://csb.cse.yzu.edu.tw/GSHSite/), for identifying uncharacterized GSH substrate sites on the protein sequences.

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Conflict of interest statement

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

Figures

Fig 1

Fig 1. The analytical flowchart of MDDLogo application.

(A) Using chi-square test to detect the maximal dependence of position, and (B) Tree-like visualization of MDDLogo-clustering result.

Fig 2

Fig 2. The conceptual diagram of two-layered SVMs trained with MDDLogo- identified substrate motifs.

Fig 3

Fig 3. TwoSampleLogo presents the compositional biases of amino acids around _S_-glutathionylation sites compared to the non-_S_-glutathionylation sites in mouse macrophages.

The significant amino acids around _S_-glutathionylated cysteine residue is enriched from the positive dataset and presented in upper panel (p < 0.01). Relatively, the high frequency of amino acids around non-_S_-glutathionylated cysteines is depleted from the negative dataset and presented in lower panel.

Fig 4

Fig 4. The MDDLogo-clustered subgroups from 1783 _S_-glutathionylation sites in mouse data set.

Fig 5

Fig 5. Comparison of independent testing performance between single SVM and MDDLogo-clustered SVM models.

Sn, sensitivity; Sp, specificity; Acc, accuracy; MCC, Matthews Correlation Coefficient.

Fig 6

Fig 6. A case study of S-glutathionylation site prediction for mouse thioredoxin (THIO_MOUSE).

The website presents (A) protein information and annotation, (B) S-glutathionylation sites from published experiment, and (C) prediction result of potential consensus motifs.

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