蛋白质构象空间局部增强差分进化搜索方法

摘要/Abstract

摘要： 针对蛋白质构象空间搜索问题,提出一种蛋白质构象空间局部增强差分进化搜索方法。在差分进化算法框架下,采用Rosetta Score3粗粒度知识能量模型有效降低构象空间的搜索维数,加快算法收敛速度；引入基于知识的片段组装技术可以有效提高预测精度；利用Monte Carlo算法良好的局部搜索性能对种群做局部增强,以得到更为优良的局部构象；结合差分进化算法较强的全局搜索能力,可以对构象空间进行更为有效的采样。5个测试蛋白实验结果表明,所提算法具有较好的搜索性能和预测精度。

关键词: 蛋白质结构预测,差分进化算法,粗粒度能量模型,片段组装,Monte Carlo

Abstract: A local enhancement differential evolution searching method for protein conformational space was proposed to address the searching problem of protein conformational space.On the framework of differential evolution algorithm,Rosetta Score3 coarse-grained energy model was employed for decreasing the dimension of searching space and improving the convergence rate of algorithm.The knowledge-based fragment assembly technique was introduced for improving the accuracy of prediction.For getting better local near-native conformation,local enhancement operation was done with taking advantage of the well local search performance of Monte Carlo algorithm.The well global searching capacity of the differential evolution algorithm was combined for sampling the whole conformational space effectively.The experi-ment results on 5 test proteins verify the superior searching performance and prediction accuracy of the proposed method.

Key words: Protein structure prediction,Differential evolution algorithm,Coarse-grained energy model,Fragment-assembly,Monte Carlo

董辉,郝小虎,张贵军. 蛋白质构象空间局部增强差分进化搜索方法[J]. 计算机科学, 2015, 42(Z11): 22-26. https://doi.org/

DONG Hui, HAO Xiao-hu and ZHANG Gui-jun. Local Enhancement Differential Evolution Searching Method for Protein Conformational Space[J]. Computer Science, 2015, 42(Z11): 22-26. https://doi.org/

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