氨基酸广义信息因子分析标度组合支持向量机模型用于β-turns预测及特征分析

Using factor analysis scales of generalized amino acid information for prediction and characteristic analysis of β-turns in proteins based on a support vector machine model

提出一种新的组合方法用于β-turns预测和特征分析.该方法包括两步:如何表征β-turns特征和如何构建其预测模型.第一步应用氨基酸广义信息因子分析标度表征蛋白质中β-turns的结构特征,该标度涉及氨基酸的疏水性、α-螺旋与转角倾向、体积性质、构成特征、局部柔性及静电性.第二步以426个蛋白质为训练集样本,通过留1/7法交互验证,基于支持向量机建立β-turns预测模型.该模型分别成功地预测547和823个蛋白的β-turns.所得结果与所对比方法结果相当,更重要的是,SVM模型提供了一些关于β-turns特征的重要结构信息.该组合方法可以进一步尝试用于蛋白质结构预测及特征分析.

This paper offered a new combined approach to predict and characterize β-turns in proteins.The approach concluded two key steps,i.e.,how to represent the features of β-turns and how to develop a predictor.The first step was to use factor analysis scales of generalized amino acid information （FASGAI）,involving hydrophobicity,alpha and turn propensities,bulky properties,compositional characteristics,local flexibility and electronic properties,to represent the features of β-turns in proteins.The second step was to construct a support vector machine （SVM） predictor of β-turns based on 426 training proteins by a sevenfold cross validation test.The SVM predictor thus successfully predicted β-turns on 547 and 823 proteins by an external validation test,respectively.Our results were compared with the previously best known β-turn prediction methods and were shown to give comparative performance.Most importantly,the SVM model provided some important information related to β-turn residues in proteins.Satisfying results demonstrated that the present combination approach showed great application prospect in the prediction of protein structures.