距离约束和二面角优化的蛋白质结构预测方法

Distance Constraint and Dihedral Angle Optimized Protein Structure Prediction Method

预测蛋白质三维结构对了解其生物功能、疾病发病机理研究、药物研发等具有重大意义.为了提高蛋白质结构预测的精度,提出了一种基于距离约束和二面角优化的蛋白质结构预测方法(Distance Constraint and Dihedral Angle Optimized Protein Structure Prediction Method, DCDA).首先,对预测的残基间距离分布图进行筛选,进而构建基于残基间距离分布的构象评估模型,指导构象选择;然后,在片段组装大范围搜索构象空间的基础上,利用基于二面角差分进化采样策略增强结构灵活的Loop区域采样,进一步提高拓扑结构的精度,增强近天然态构象采样能力.在15个测试蛋白的预测结果表明,DCDA能够达到较高的预测精度,是一种有效的蛋白质结构预测方法.

Predicting the three-dimensional structure of a protein is of great significance for understanding its biological functions, research on disease pathogenesis, and drug development.In order to improve the accuracy of protein structure prediction, a protein structure prediction method(DCDA)based on distance constraint and dihedral angle optimization is proposed.First, the predicted distance distribution map between residues is screened, and then a conformation evaluation model based on the distance distribution between residues is constructed to guide conformation selection;then, based on the large-scale search of conformational space in fragment assembly, the use of dihedral-based the differential evolution sampling strategy enhances the sampling of the loop area with flexible structure, further improves the accuracy of the topological structure, and enhances the sampling ability of the near-natural state conformation.The prediction results of 15 test proteins show that DCDA can achieve high prediction accuracy, and it is an effective protein structure prediction method.