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A parallel hybrid genetic algorithm for protein structure prediction on the computational grid

机译：计算网格上蛋白质结构预测的并行混合遗传算法

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摘要

Solving the structure prediction problem for complex proteins is difficult and computationally expensive. In this paper, we propose a bicriterion parallel hybrid genetic algorithm (GA) in order to efficiently deal with the problem using the computational grid. The use of a near-optimal metaheuristic, such as a GA, allows a significant reduction in the number of explored potential structures. However, the complexity of the problem remains prohibitive as far as large proteins are concerned, making the use of parallel computing on the computational grid essential for its efficient resolution. A conjugated gradient-based Hill Climbing local search is combined with the GA in order to intensify the search in the neighborhood of its provided configurations. In this paper we consider two molecular complexes: the tryptophan-cage protein (Brookhaven Protein Data Bank ID 1L2Y) and α-cyclodextrin. The experimentation results obtained on a computational grid show the effectiveness of the approach.
机译：解决复杂蛋白质的结构预测问题是困难的，并且在计算上是昂贵的。在本文中，我们提出了一种双标准并行混合遗传算法（GA），以便使用计算网格有效地解决该问题。使用近乎理想的元启发式算法（例如GA）可以显着减少所探查的潜在结构的数量。但是，就大型蛋白质而言，问题的复杂性仍然令人望而却步，这使得在计算网格上使用并行计算对其有效解决至关重要。基于共轭梯度的Hill Hill Climbing局部搜索与GA组合在一起，以便在其提供的配置附近加强搜索。在本文中，我们考虑了两种分子复合物：色氨酸笼蛋白（Brookhaven蛋白质数据库ID 1L2Y）和α-环糊精。在计算网格上获得的实验结果表明了该方法的有效性。

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