Prediction of Tyrosine Sulfation with mRMR Feature Selection and Analysis

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），涉及分泌蛋白和跨膜蛋白在通过高尔基体时的过程。在本研究中，我们基于最近邻算法，采用最大相关最小冗余（mRMR）方法，并结合增量特征选择（IFS）技术，开发了一种预测蛋白质酪氨酸硫酸化的新方法。该预测模型融合了序列保守性、残差无序性和氨基酸因素等特征，总计229个特征，用于预测酪氨酸硫酸化位点。在这229个特征中，经过筛选，145个特征被认为是最优特征集。使用此145特征集，预测模型实现了90.01%的预测准确率。特征分析表明，序列的保守性、无序性以及氨基酸的物理化学和生化性质均对硫酸化过程有所贡献。针对特定位点的特征分析显示，除硫酸化位点本身的特征外，其周围位点的特征也对硫酸化位点的确定起着至关重要的作用。本文中的详细特征分析或许有助于深入理解硫酸化机制，并指导相关实验验证。