A new distributed modified extremal optimization for optimizing protein structure alignment
A new distributed modified extremal optimization for optimizing protein structure alignment
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DOI：
10.1109/iwcia.2015.7449472
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发表时间：
2015-11
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通讯作者：
Keiichi Tamura;H. Kitakami;Tatsuhiro Sakai;Yoshifumi Takahashi
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作者：
Keiichi Tamura;H. Kitakami;Tatsuhiro Sakai;Yoshifumi Takahashi
识别蛋白质中的相似结构已成为后基因组时代最具吸引力的研究课题之一。蛋白质结构比对类似于序列比对，它根据蛋白质的三维构象确定两个蛋白质结构之间的结构同源性。用于优化蛋白质结构比对的最简单但最健壮的技术之一是接触图重叠最大化问题(CMO问题)。在本文中，我们重点研究了CMO问题的启发式算法。在我们以前的工作中，我们提出了一种基于分布式修改极值优化(DMEO)的仿生启发式算法来解决CMO问题。DMEO是基于种群的改进极值优化算法(PMEO)和孤岛模型的混合体。DMEO增强了种群的多样性，然而，个体进化是非常单调的，因为它的进化是基于贪婪的移动方法。针对这一问题，我们提出了一种新颖的仿生启发式算法，即具有不同进化策略的DMEO算法(DMEODES)。DMEODES也是基于岛屿模型；然而，一些被称为热点岛屿的岛屿有不同的进化策略。为了评估DMEODES，我们使用了实际的蛋白质结构。实验结果表明，DMEODES的性能优于DMEO。
Identifying similar structures in proteins has emerged as one of the most attractive research topics in the post-genome era. Protein structure alignment, which is similar to sequence alignment, identifies the structural homology between two protein structures according to their three-dimensional conformation. One of the simplest yet most robust techniques for optimizing protein structure alignment is the contact map overlap maximization problem (the CMO problem). In this paper, we focus on heuristics for the CMO problem. In our previous work, we proposed a bio-inspired heuristic using distributed modified extremal optimization (DMEO) for the CMO problem. DMEO is a hybrid of population-based modified extremal optimization (PMEO) and the island model. DMEO enhances population diversity; however, individual evolution is extremely monotonous because evolutions of it is based on the greedy moving approach. To address this issue, we propose a novel bio-inspired heuristic, i.e., DMEO with different evolutionary strategy (DMEODES). DMEODES is also based on the island model; however, some of the islands, called hot-spot islands, have a different evolutionary strategy. To evaluate DMEODES, we used actual protein structures. Experimental results showed that DMEODES outperforms DMEO.