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.

中文翻译：

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基于深度学习的物种特异性蛋白质S-谷胱甘肽化位点的预测。

作为蛋白质的广泛且可逆的翻译后修饰，S-谷胱甘肽酰化作用专门在半胱氨酸残基和谷胱甘肽之间产生混合的二硫化物，从而调节各种生物过程，包括氧化应激，亚硝化应激和信号转导。鉴定经历S-谷胱甘肽化的蛋白质和特定位点对于理解S-谷胱甘肽化的基本机制和调节作用至关重要。S-谷胱甘肽化位点的实验鉴定是费力且费时的，而计算预测由于其高速和方便而更具吸引力。在这里，我们开发了一种新颖的计算框架DeepGSH（http://deepgsh.cancerbio.info/），用于特定物种的S-谷胱甘肽酰化位点预测，基于深度学习和粒子群优化算法。5倍交叉验证表明，对于智人和小家鼠，DeepGSH能够实现0.8393和0.8458的AUC。根据严格的评估和比较，DeepGSH在这两个物种中均显示出比现有工具优异的鲁棒性和更好的性能，证明DeepGSH适合进行S-谷胱甘肽化预测。DeepGSH的预测结果可能为S-谷胱甘肽化位点的实验验证提供指导，并有助于了解内在机制。在这两个物种中，DeepGSH均表现出出色的鲁棒性和比现有工具更好的性能，证明DeepGSH适合进行S-谷胱甘肽化预测。DeepGSH的预测结果可能为S-谷胱甘肽化位点的实验验证提供指导，并有助于了解内在机制。在这两个物种中，DeepGSH均表现出出色的鲁棒性和比现有工具更好的性能，证明DeepGSH适合进行S-谷胱甘肽化预测。DeepGSH的预测结果可能为S-谷胱甘肽化位点的实验验证提供指导，并有助于了解内在机制。