Reactive oxygen species are produced by a number of stimuli and can lead both to irreversible intracellular damage and signaling through reversible post-translational modification. It is unclear which factors contribute to the sensitivity of cysteines to redox modification. Here, we used statistical and machine learning methods to investigate the influence of different structural and sequence features on the modifiability of cysteines. We found several strong structural predictors for redox modification. Sensitive cysteines tend to be characterized by higher exposure, a lack of secondary structure elements, and a high number of positively charged amino acids in their close environment. Our results indicate that modified cysteines tend to occur close to other post-translational modifications, such as phosphorylated serines. We used these features to create models and predict the presence of redox-modifiable cysteines in human mitochondrial complex I as well as make novel predictions regarding redox-sensitive cysteines in proteins.

中文翻译：

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使用机器学习和统计方法预测和分析氧化还原敏感半胱氨酸

活性氧由多种刺激产生，可通过可逆的翻译后修饰导致不可逆的细胞内损伤和信号传导。目前尚不清楚哪些因素有助于半胱氨酸对氧化还原修饰的敏感性。在这里，我们使用统计和机器学习方法来研究不同结构和序列特征对半胱氨酸可修饰性的影响。我们发现了几个强有力的氧化还原修饰结构预测因子。敏感的半胱氨酸往往以较高的暴露量、缺乏二级结构元素以及在其封闭环境中大量带正电荷的氨基酸为特征。我们的结果表明修饰的半胱氨酸倾向于发生在其他翻译后修饰附近，例如磷酸化丝氨酸。