计算机科学 ›› 2020, Vol. 47 ›› Issue (7): 161-165.doi: 10.11896/jsjkx.190600100
李章维1, 肖璐倩1, 郝小虎1, 周晓根2, 张贵军1
LI Zhang-wei1, XIAO Lu-qian1, HAO Xiao-hu1, ZHOU Xiao-gen2, ZHANG Gui-jun1
摘要: 蛋白质能量模型的不精确性导致数学上的最优解并不一定对应其稳定的天然态结构,同时其巨大的构象空间使得现有方法也极易收敛到局部最优解。针对蛋白质结构能量模型不精确和高维构象空间采样可靠性低的问题,在进化算法的基础上,提出了一种基于二面角相似度的蛋白质构象多模态优化方法。首先,执行模态探测,将Rosetta粗粒度能量模型作为筛选高质量新个体的标准,进行种群更新,增加种群构象的多样性;然后,建立二面角相似度模型,用于评价不同构象间的相似程度,以满足多模态优化算法中相似个体快速判定的要求,并基于排挤更新策略实现模态增强,获得结构更为合理的构象。10个测试蛋白质的实验结果表明:所提算法能够达到较高的预测精度,并且可以使种群具有良好的模态分布,得到尽可能多的高质量局部极值解,从而获得一些较好的蛋白质亚稳态结构。
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