Rahman Afrida, Ahmed Sabit, Al Mehedi Hasan Md, Ahmad Shamim, Dehzangi Iman
Department of Computer Science and Engineering, Rajshahi University of Engineering and Technology, Rajshahi, Bangladesh.
Department of Computer Science and Engineering, University of Rajshahi, Rajshahi, Bangladesh.
Gene. 2022 Jun 5;826:146445. doi: 10.1016/j.gene.2022.146445. Epub 2022 Mar 28.
Post-translational modification (PTM) is defined as the enzymatic changes of proteins after the translation process in protein biosynthesis. Nitrotyrosine, which is one of the most important modifications of proteins, is interceded by the active nitrogen molecule. It is known to be associated with different diseases including autoimmune diseases characterized by chronic inflammation and cell damage. Currently, nitrotyrosine sites are identified using experimental approaches which are laborious and costly. In this study, we propose a new machine learning method called PredNitro to accurately predict nitrotyrosine sites. To build PredNitro, we use sequence coupling information from the neighboring amino acids of tyrosine residues along with a support vector machine as our classification technique.Our results demonstrates that PredNitro achieves 98.0% accuracy with more than 0.96 MCC and 0.99 AUC in both 5-fold cross-validation and jackknife cross-validation tests which are significantly better than those reported in previous studies. PredNitro is publicly available as an online predictor at: http://103.99.176.239/PredNitro.
翻译后修饰（PTM）被定义为蛋白质生物合成中翻译过程后蛋白质的酶促变化。硝基酪氨酸是蛋白质最重要的修饰之一，由活性氮分子介导。已知它与包括以慢性炎症和细胞损伤为特征的自身免疫性疾病在内的不同疾病有关。目前，硝基酪氨酸位点是使用费力且昂贵的实验方法来鉴定的。在本研究中，我们提出了一种名为PredNitro的新机器学习方法来准确预测硝基酪氨酸位点。为了构建PredNitro，我们使用来自酪氨酸残基相邻氨基酸的序列耦合信息，并将支持向量机作为我们的分类技术。我们的结果表明，在5折交叉验证和留一法交叉验证测试中，PredNitro的准确率达到98.0%，马修斯相关系数（MCC）超过0.96，曲线下面积（AUC）为0.99，明显优于先前研究报告的结果。PredNitro作为在线预测工具可公开获取，网址为：http://103.99.176.239/PredNitro。
