Key Laboratory of Systems Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, PR China.
J Proteome Res. 2010 Dec 3;9(12):6490-7. doi: 10.1021/pr1007152. Epub 2010 Nov 11.
Protein tyrosine sulfation is a ubiquitous post-translational modification (PTM) of secreted and transmembrane proteins that pass through the Golgi apparatus. In this study, we developed a new method for protein tyrosine sulfation prediction based on a nearest neighbor algorithm with the maximum relevance minimum redundancy (mRMR) method followed by incremental feature selection (IFS). We incorporated features of sequence conservation, residual disorder, and amino acid factor, 229 features in total, to predict tyrosine sulfation sites. From these 229 features, 145 features were selected and deemed as the optimized features for the prediction. The prediction model achieved a prediction accuracy of 90.01% using the optimal 145-feature set. Feature analysis showed that conservation, disorder, and physicochemical/biochemical properties of amino acids all contributed to the sulfation process. Site-specific feature analysis showed that the features derived from its surrounding sites contributed profoundly to sulfation site determination in addition to features derived from the sulfation site itself. The detailed feature analysis in this paper might help understand more of the sulfation mechanism and guide the related experimental validation.
蛋白质酪氨酸硫酸化是一种普遍存在的翻译后修饰（PTM），发生于穿过高尔基体的分泌型和跨膜蛋白。在这项研究中，我们开发了一种新的基于最邻近算法的蛋白质酪氨酸硫酸化预测方法，该方法采用最大相关性最小冗余（mRMR）方法和增量特征选择（IFS）。我们整合了序列保守性、残基无序性和氨基酸因素等特征，总共 229 个特征，用于预测酪氨酸硫酸化位点。从这 229 个特征中，选择了 145 个特征作为预测的最优特征。使用最优的 145 个特征集，预测模型的预测准确率达到了 90.01%。特征分析表明，氨基酸的保守性、无序性和理化/生化性质都有助于硫酸化过程。位点特异性特征分析表明，除了源自硫酸化位点本身的特征外，源自其周围位点的特征对硫酸化位点的确定也有很大的贡献。本文详细的特征分析可能有助于更深入地了解硫酸化机制，并指导相关的实验验证。
