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首页> 外文期刊>International Journal of Molecular Sciences >Prediction of Protein S-Nitrosylation Sites Based on Adapted Normal Distribution Bi-Profile Bayes and Chou’s Pseudo Amino Acid Composition
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Prediction of Protein S-Nitrosylation Sites Based on Adapted Normal Distribution Bi-Profile Bayes and Chou’s Pseudo Amino Acid Composition

机译：基于自适应正态分布双谱贝叶斯算法和周氏伪氨基酸组成的蛋白质S-亚硝基化位点预测

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Protein S-nitrosylation is a reversible post-translational modification by covalent modification on the thiol group of cysteine residues by nitric oxide. Growing evidence shows that protein S-nitrosylation plays an important role in normal cellular function as well as in various pathophysiologic conditions. Because of the inherent chemical instability of the S-NO bond and the low abundance of endogenous S-nitrosylated proteins, the unambiguous identification of S-nitrosylation sites by commonly used proteomic approaches remains challenging. Therefore, computational prediction of S-nitrosylation sites has been considered as a powerful auxiliary tool. In this work, we mainly adopted an adapted normal distribution bi-profile Bayes (ANBPB) feature extraction model to characterize the distinction of position-specific amino acids in 784 S-nitrosylated and 1568 non-S-nitrosylated peptide sequences. We developed a support vector machine prediction model, iSNO-ANBPB, by incorporating ANBPB with the Chou’s pseudo amino acid composition. In jackknife cross-validation experiments, iSNO-ANBPB yielded an accuracy of 65.39% and a Matthew’s correlation coefficient (MCC) of 0.3014. When tested on an independent dataset, iSNO-ANBPB achieved an accuracy of 63.41% and a MCC of 0.2984, which are much higher than the values achieved by the existing predictors SNOSite, iSNO-PseAAC, the Li et al. algorithm, and iSNO-AAPair. On another training dataset, iSNO-ANBPB also outperformed GPS-SNO and iSNO-PseAAC in the 10-fold crossvalidation test.
机译：蛋白S-亚硝基化是可逆的翻译后修饰，通过一氧化氮对半胱氨酸残基的巯基进行共价修饰。越来越多的证据表明，蛋白质S-亚硝基化在正常细胞功能以及各种病理生理状况中起着重要作用。由于S-NO键固有的化学不稳定性和内源性S-亚硝基化蛋白的丰度低，通过常用的蛋白质组学方法对S-亚硝基化位点的明确鉴定仍然具有挑战性。因此，S-亚硝基化位点的计算预测已被认为是强大的辅助工具。在这项工作中，我们主要采用适应性正态分布双谱贝叶斯（ANBPB）特征提取模型来表征784个S-亚硝化和1568个非S-亚硝化的肽序列中特定位置氨基酸的区别。通过将ANBPB与Chou的伪氨基酸组成结合起来，我们开发了一种支持向量机预测模型iSNO-ANBPB。在折刀交叉验证实验中，iSNO-ANBPB的准确度为65.39％，马修相关系数（MCC）为0.3014。当在独立的数据集上进行测试时，iSNO-ANBPB的准确度为63.41％，MCC为0.2984，远远高于现有的预报器SNOSite，iSNO-PseAAC和Li等人所获得的值。算法和iSNO-AAPair。在另一个训练数据集上，iSNO-ANBPB在10倍交叉验证测试中也胜过GPS-SNO和iSNO-PseAAC。

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