SNOSite: exploiting maximal dependence decomposition to identify cysteine S-nitrosylation with substrate site specificity.
SNOSite: exploiting maximal dependence decomposition to identify cysteine S-nitrosylation with substrate site specificity.
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DOI：
10.1371/journal.pone.0021849
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发表时间：
2011
期刊：
影响因子：
3.7
通讯作者：
Chen YJ
中科院分区：
文献类型：
--
作者：
Lee TY;Chen YJ;Lu TC;Huang HD;Chen YJ
S-亚硝基化是一氧化氮与半胱氨酸硫原子 (NO) 的共价连接，是一种选择性、可逆的蛋白质翻译后修饰 (PTM)，可调节蛋白质活性、定位和稳定性。尽管半胱氨酸 S-亚硝基化对蛋白质功能和细胞信号传导的调节具有重要意义，但其底物特异性仍然未知。基于 SNAP/L-半胱氨酸刺激的小鼠内皮细胞中总共 586 个实验鉴定的 S-亚硝基化位点，这项工作对 S-亚硝基化位点进行了信息学研究，包括结构因素，如侧翼氨基酸组成、可及表面积 (ASA) 和物理化学性质，即正电荷和侧链相互作用参数。由于传统的基序分析难以获得保守基序，因此应用最大依赖性分解（MDD）来获得统计上显着的保守基序。应用支持向量机 (SVM) 为每个 MDD 聚类主题生成预测模型。根据五折交叉验证，MDD聚类SVM可以达到0.902的准确率，并且在独立测试集中提供了良好的性能。该模型的有效性在先前报道的牛二甲基精氨酸二甲氨基水解酶 1 (DDAH1) 和人血红蛋白亚基β (HBB) 的 S-亚硝基化位点的正确识别上得到了证明。最后，采用MDD聚类模型构建了一个有效的基于网络的工具，名为SNOSite（http://csb.cse.yzu.edu.tw/SNOSite/），用于识别未表征的蛋白质序列上的S-亚硝基化位点。
S-nitrosylation, the covalent attachment of a nitric oxide to (NO) the sulfur atom of cysteine, is a selective and reversible protein post-translational modification (PTM) that regulates protein activity, localization, and stability. Despite its implication in the regulation of protein functions and cell signaling, the substrate specificity of cysteine S-nitrosylation remains unknown. Based on a total of 586 experimentally identified S-nitrosylation sites from SNAP/L-cysteine-stimulated mouse endothelial cells, this work presents an informatics investigation on S-nitrosylation sites including structural factors such as the flanking amino acids composition, the accessible surface area (ASA) and physicochemical properties, i.e. positive charge and side chain interaction parameter. Due to the difficulty to obtain the conserved motifs by conventional motif analysis, maximal dependence decomposition (MDD) has been applied to obtain statistically significant conserved motifs. Support vector machine (SVM) is applied to generate predictive model for each MDD-clustered motif. According to five-fold cross-validation, the MDD-clustered SVMs could achieve an accuracy of 0.902, and provides a promising performance in an independent test set. The effectiveness of the model was demonstrated on the correct identification of previously reported S-nitrosylation sites of Bos taurus dimethylarginine dimethylaminohydrolase 1 (DDAH1) and human hemoglobin subunit beta (HBB). Finally, the MDD-clustered model was adopted to construct an effective web-based tool, named SNOSite (http://csb.cse.yzu.edu.tw/SNOSite/), for identifying S-nitrosylation sites on the uncharacterized protein sequences.
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