Protein nitration and nitrosylation are essential post-translational modifications (PTMs) involved in many fundamental cellular processes. Recent studies have revealed that excessive levels of nitration and nitrosylation in some critical proteins are linked to numerous chronic diseases. Therefore, the identification of substrates that undergo such modifications in a site-specific manner is an important research topic in the community and will provide candidates for targeted therapy. In this study, we aimed to develop a computational tool for predicting nitration and nitrosylation sites in proteins. We first constructed four types of encoding features, including positional amino acid distributions, sequence contextual dependencies, physicochemical properties, and position-specific scoring features, to represent the modified residues. Based on these encoding features, we established a predictor called DeepNitro using deep learning methods for predicting protein nitration and nitrosylation. Using n-fold cross-validation, our evaluation shows great AUC values for DeepNitro, 0.65 for tyrosine nitration, 0.80 for tryptophan nitration, and 0.70 for cysteine nitrosylation, respectively, demonstrating the robustness and reliability of our tool. Also, when tested in the independent dataset, DeepNitro is substantially superior to other similar tools with a 7%?42% improvement in the prediction performance. Taken together, the application of deep learning method and novel encoding schemes, especially the position-specific scoring feature, greatly improves the accuracy of nitration and nitrosylation site prediction and may facilitate the prediction of other PTM sites. DeepNitro is implemented in JAVA and PHP and is freely available for academic research at http://deepnitro.renlab.org.
蛋白质硝基化和亚硝基化是一种关键的蛋白质翻译后修饰类型，它在多种常见的细胞调控过程中都发挥着重要的作用。最近的研究表明，在某些关键蛋白上的异常硝基化及亚硝基化水平与多种慢性疾病相关。因此，在修饰底物上鉴定精确的修饰位点是当前研究的重要关注点，并且能为慢性疾病的靶向治疗提供潜在靶点。本研究中，我们针对蛋白质硝基化及亚硝基化开发了一套精确的位点预测工具——DeepNitro。首先，我们在计算模型中引入了氨基酸分布、序列上下游特征、理化性质以及位点特异性打分这四种编码算法来对修饰位点进行训练特征提取。基于这些特征编码，我们利用深度学习算法构建了一个专门针对蛋白质硝基化及亚硝基化的位点预测模型。同时，N折交叉验证显示，本研究所建立的模型可以给出稳定及可信的预测结果，其中对酪氨酸、色氨酸硝基化以及半胱氨酸亚硝基化的预测AUC分别达到0.65、0.80以及0.70。另外，在独立测试集的评估中我们也发现DeepNitro在预测精度上要显著高于当前已有的工具。相较于其他工具，DeepNitro具有7% - 42%的预测性能提升。综合上述，应用深度学习算法及新型的特征编码方法，我们提高了针对蛋白质硝基化及亚硝基化的预测精度，进一步辅助了对这些修饰位点的高通量鉴定。目前，DeepNitro使用JAVA和PHP开发，可以通过http://deepnitro.renlab.org免费获取。





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