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首页> 外文期刊>IEEE/ACM transactions on computational biology and bioinformatics >Enhancing Protein Conformational Space Sampling Using Distance Profile-Guided Differential Evolution
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Enhancing Protein Conformational Space Sampling Using Distance Profile-Guided Differential Evolution

机译：使用距离剖面引导的差分进化增强蛋白质构象空间采样

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摘要

De novo protein structure prediction aims to search for low-energy conformations as it follows the thermodynamics hypothesis that places native conformations at the global minimum of the protein energy surface. However, the native conformation is not necessarily located in the lowest-energy regions owing to the inaccuracies of the energy model. This study presents a differential evolution algorithm using distance profile-based selection strategy to sample conformations with reasonable structure effectively. In the proposed algorithm, besides energy, the residue-residue distance is considered another measure of the conformation. The average distance errors of decoys between the distance of each residue pair and the corresponding distance in the distance profiles are first calculated when the trial conformation yields a larger energy value than that of the target. Then, the distance acceptance probability of the trial conformation is designed based on distance profiles if the trial conformation obtains a lower average distance error compared with that of the target conformation. The trial conformation is accepted to the next generation in accordance with its distance acceptance probability. By using the dual constraints of energy and distance in guiding sampling, the algorithm can sample conformations with lower energies and more reasonable structures. Experimental results of 28 benchmark proteins show that the proposed algorithm can effectively predict near-native protein structures.
机译：从头蛋白质结构预测的目的是寻找低能构象，因为它遵循热力学假设，该假设将天然构象置于蛋白质能表面的全局最小值。但是，由于能量模型的不精确性，天然构象不一定位于最低能量区域。本研究提出了一种基于基于距离分布的选择策略的差分进化算法，可以有效地采样具有合理结构的构象。在提出的算法中，除能量外，残基-残基距离被认为是构象的另一种度量。当试验构象产生的能量值大于目标能量值时，首先计算每个残基对的距离与距离分布中的相应距离之间的诱饵的平均距离误差。然后，如果试验构型获得的平均距离误差小于目标构型的平均距离误差，则根据距离轮廓设计试验构型的距离接受概率。试验构型根据其距离接受概率被下一代接受。通过在引导采样中使用能量和距离的双重约束，该算法可以采样具有较低能量和更合理结构的构象。 28种基准蛋白质的实验结果表明，该算法可以有效预测近天然蛋白质的结构。

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