Protein structure prediction has been a long-standing problem for the past decades. In particular, the loop region structure remains an obstacle in forming an accurate protein tertiary structure because of its flexibility. In this study, Rama torsion angle and secondary structure feature-guided differential evolution named RSDE is proposed to predict three-dimensional structure with the exploitation on the loop region structure. In RSDE, the structure of the loop region is improved by the following: loop-based cross operator, which interchanges configuration of a randomly selected loop region between individuals, and loop-based mutate operator, which considers torsion angle feature into conformational sampling. A stochastic ranking selective strategy is designed to select conformations with low energy and near-native structure. Moreover, the conformational resampling method, which uses previously learned knowledge to guide subsequent sampling, is proposed to improve the sampling efficiency. Experiments on a total of 28 test proteins reveals that the proposed RSDE is effective and can obtain native-like models.

中文翻译：

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用于蛋白质结构预测的循环增强构象重采样方法。

在过去的几十年中，蛋白质结构的预测一直是一个长期存在的问题。特别地，由于其柔性，环区结构仍然是形成准确的蛋白质三级结构的障碍。在这项研究中，拉玛扭转角和二级结构特征指导的微分演化被称为RSDE，旨在利用环区域结构来预测三维结构。在RSDE中，通过以下方面改进了环区域的结构：基于环的交叉算子（在个体之间交换随机选择的环区域的配置）和基于环的变异算子（其将扭角特征考虑到构象采样中）。设计了一种随机排名选择策略，以选择低能量和近自然结构的构象。此外，提出了一种构象重采样方法，该方法利用先前学习的知识来指导后续采样，以提高采样效率。对总共28种测试蛋白进行的实验表明，提出的RSDE是有效的，并且可以获得类似天然的模型。