A population-based evolutionary algorithm for sampling minima in the protein energy surface

Sameh Saleh, Brian S. Olson, Amarda Shehu
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引用次数: 6

Abstract

Obtaining a structural characterization of the biologically active (native) state of a protein is a long standing problem in computational biology. The high dimensionality of the conformational space and ruggedness of the associated energy surface are key challenges to algorithms in search of an ensemble of low-energy decoy conformations relevant for the native state. As the native structure does not often correspond to the global minimum energy, diversity is key. We present a memetic evolutionary algorithm to sample a diverse ensemble of conformations that represent low-energy local minima in the protein energy surface. Conformations in the algorithm are members of an evolving population. The molecular fragment replacement technique is employed to obtain children from parent conformations. A greedy search maps a child conformation to its nearest local minimum. Resulting minima and parent conformations are merged and truncated back to the initial population size based on potential energies. Results show that the additional minimization is key to obtaining a diverse ensemble of decoys, circumvent premature convergence to sub-optimal regions in the conformational space, and approach the native structure with IRMSDs comparable to state-of-the-art decoy sampling methods.
基于种群的蛋白质能量面最小采样进化算法
获得蛋白质的生物活性(天然)状态的结构表征是计算生物学中长期存在的问题。构象空间的高维性和相关能量面的坚固性是搜索与自然状态相关的低能诱饵构象集合的算法面临的关键挑战。由于原生结构往往不符合全局最小能量,多样性是关键。我们提出了一种模因进化算法来采样不同的构象集合,这些构象代表蛋白质能量表面的低能局部最小值。算法中的构象是不断进化的种群中的成员。采用分子片段置换技术从母体构象中获得子代。贪婪搜索将子构象映射到它最近的局部最小值。由此产生的最小构象和母构象被合并并截断回基于势能的初始种群大小。结果表明,额外的最小化是获得多样化诱饵集合的关键,避免过早收敛到构象空间的次优区域,并使用可与最先进的诱饵采样方法相比较的irmsd接近天然结构。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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