Laboratory of Computational Biology, National Heart, Lung, and Blood Institute (NHLBI), National Institutes of Health (NIH), Bethesda, Maryland 20892, USA.
Center for Materials Genome, Korea Research Institute of Standards and Science, Daejeon 34113, Republic of Korea.
Nat Commun. 2017 May 26;8:15443. doi: 10.1038/ncomms15443.
Global searching for reaction pathways is a long-standing challenge in computational chemistry and biology. Most existing approaches perform only local searches due to computational complexity. Here we present a computational approach, Action-CSA, to find multiple diverse reaction pathways connecting fixed initial and final states through global optimization of the Onsager-Machlup action using the conformational space annealing (CSA) method. Action-CSA successfully overcomes large energy barriers via crossovers and mutations of pathways and finds all possible pathways of small systems without initial guesses on pathways. The rank order and the transition time distribution of multiple pathways are in good agreement with those of long Langevin dynamics simulations. The lowest action folding pathway of FSD-1 is consistent with recent experiments. The results show that Action-CSA is an efficient and robust computational approach to study the multiple pathways of complex reactions and large-scale conformational changes.
全局搜索反应途径是计算化学和生物学中的一个长期挑战。由于计算复杂性，大多数现有方法仅执行局部搜索。在这里，我们提出了一种计算方法，即 Action-CSA，通过使用构象空间退火 (CSA) 方法对 Onsager-Machlup 作用进行全局优化，来寻找连接固定初始和最终状态的多条不同的反应途径。Action-CSA 通过途径的交叉和突变成功克服了大的能量障碍，并找到了小系统的所有可能途径，而无需对途径进行初始猜测。多条途径的等级顺序和跃迁时间分布与长 Langevin 动力学模拟的结果非常吻合。FSD-1 的最低作用折叠途径与最近的实验结果一致。结果表明，Action-CSA 是一种有效的、稳健的计算方法，可用于研究复杂反应和大规模构象变化的多条途径。
