As a widespread and reversible post-translational modification of proteins, S-glutathionylation specifically generates the mixed disulfides between cysteine residues and glutathione, which regulates various biological processes including oxidative stress, nitrosative stress and signal transduction. The identification of proteins and specific sites that undergo S-glutathionylation is crucial for understanding the underlying mechanisms and regulatory effects of S-glutathionylation. Experimental identification of S-glutathionylation sites is laborious and time-consuming, whereas computational predictions are more attractive due to their high speed and convenience. Here, we developed a novel computational framework DeepGSH (http://deepgsh.cancerbio.info/) for species-specific S-glutathionylation sites prediction, based on deep learning and particle swarm optimization algorithms. 5-fold cross validation indicated that DeepGSH was able to achieve an AUC of 0.8393 and 0.8458 for Homo sapiens and Mus musculus. According to critical evaluation and comparison, DeepGSH showed excellent robustness and better performance than existing tools in both species, demonstrating DeepGSH was suitable for S-glutathionylation prediction. The prediction results of DeepGSH might provide guidance for experimental validation of S-glutathionylation sites and helpful information to understand the intrinsic mechanisms.
Keywords: Deep learning; Prediction; S-glutathionylation; Species-specific.
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