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首页> 外文期刊>Critical Reviews in Biomedical Engineering >A Comparative Study on Single and Multiple Point Crossovers in a Genetic Algorithm for Coarse Protein Modeling
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A Comparative Study on Single and Multiple Point Crossovers in a Genetic Algorithm for Coarse Protein Modeling

机译：粗蛋白质建模遗传算法中单点交流的比较研究

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The protein structure prediction problem is a holy grail for life science researchers. Computational protein structure prediction involves the folding of protein sequence (string) into the tertiary structure, called the native protein structure. The hydrophobic polar (HP) model is one of the basic models used to investigate the protein folding mechanism at the coarse level. In the HP model, the protein structure prediction problem is defined as an optimization problem, where the protein sequence must be folded over a lattice space such that the protein structure exhibits the lowest value of free energy. However, with the HP model, protein structure prediction is a nondeterministic polynomial (NP)-complete problem and is, therefore, simulated using meta-heuristic algorithms. Simulation of the HP model results in the formation of various protein structures called protein conformations. In this article, we present a case study on the application of a genetic algorithm to simulate the HP model based protein structure prediction. In this work, we employ the two versions of crossover functions (single-point vs. multiple-point crossovers) to generate protein conformations. The conformations were assessed based on the presence of hydrophobic contacts identified in the experimental structure. The sensitivity, specificity, and accuracy of simulation algorithm (genetic algorithm) were compared, and the significance of the parameters was statistically evaluated using the paired t-test. Our results indicate that the multipoint crossover operator enhanced the performance of genetic algorithm compared to genetic algorithm with single-point crossover. Also, multipoint crossover reduced the generation of false conformations, which results in a significant reduction in computational cost.
机译：蛋白质结构预测问题是生命科学研究人员的圣杯。计算蛋白质结构预测涉及将蛋白质序列（串）折叠成三级结构，称为天然蛋白质结构。疏水性极性（HP）模型是用于研究粗水平的蛋白质折叠机构的基本模型之一。在HP模型中，蛋白质结构预测问题被定义为优化问题，其中蛋白质序列必须在格子空间上折叠，使得蛋白质结构表现出自由能的最低值。然而，利用HP模型，蛋白质结构预测是非定义性多项式（NP） - 符合方法，因此，使用Meta-heuristic算法模拟。 HP模型的模拟导致形成称为蛋白质构象的各种蛋白质结构。在本文中，我们提出了一种遗传算法在模拟基于HP模型的蛋白质结构预测的情况下的案例研究。在这项工作中，我们采用了两个版本的交叉功能（单点与多点交叉遍及）来产生蛋白质构象。基于在实验结构中鉴定的疏水触点的存在来评估构象。比较模拟算法（遗传算法）的灵敏度，特异性和准确性，使用配对T检验统计评估参数的重要性。我们的结果表明，与单点交叉的遗传算法相比，多点交叉操作者增强了遗传算法的性能。此外，多点交叉减少了错误构象的产生，这导致计算成本显着降低。

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