A parallel multi-objective ab initio approach for protein structure prediction

David Becerra, A. Sandoval, Daniel Restrepo-Montoya, L. F. Niño
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引用次数: 23

Abstract

Protein structure prediction is one of the most important problems in bioinformatics and structural biology. This work proposes a novel and suitable methodology to model protein structure prediction with atomic-level detail by using a parallel multi-objective ab initio approach. In the proposed model, i) A trigonometric representation is used to compute backbone and side-chain torsion angles of protein atoms; ii) The Chemistry at HARvard Macromolecular Mechanics (CHARMm) function optimizes and evaluates the structures of the protein conformations; iii) The evolution of protein conformations is directed by optimization of protein energy contributions using the multi-objective genetic algorithm NSGA-II; and iv) The computation process is sped up and its effectiveness improved through the implementation of an island model of the evolutionary algorithm. The proposed model was validated on a set of benchmark proteins obtaining very promising results.
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蛋白质结构预测的并行多目标从头算方法
蛋白质结构预测是生物信息学和结构生物学中的重要问题之一。这项工作提出了一种新的和合适的方法来模拟蛋白质结构预测与原子水平的细节,使用并行多目标从头算方法。在该模型中,i)使用三角函数表示来计算蛋白质原子的主链和侧链扭转角;ii)哈佛大学大分子力学(CHARMm)的化学功能优化和评估蛋白质构象的结构;iii)利用多目标遗传算法NSGA-II优化蛋白质能量贡献,指导蛋白质构象的进化;iv)通过实现进化算法的孤岛模型,加快了计算速度,提高了算法的有效性。该模型在一组基准蛋白上进行了验证,获得了非常有希望的结果。
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