Wu Xiongwu, Brooks Bernard R
Laboratory of Computational Biology, National Heart, Lung and Blood Institute (NHLBI), National Institutes of Health (NIH), Bethesda, MD 20892, USA.
Int J Mol Sci. 2025 Feb 25;26(5):1969. doi: 10.3390/ijms26051969.
Self-guided (SG) molecular simulation methods, namely self-guided molecular dynamics (SGMD) and self-guided Langevin dynamics (SGLD), enhance conformational search by promoting low-frequency motion. A simple local time averaging scheme is used to extract low-frequency properties with little overhead in computing costs. For molecular processes to form ordered structures like ligand binding and protein folding, it is believed that concerted motions play crucial roles. To enhance the concerted motion in molecular systems, we propose a spatial averaging scheme to extract the concerted motion of a local region. Applying guiding forces based on spatial averaging, self-guided molecular simulations can enhance concerted motion and reach ordered structures more efficiently. Through simulations of amyloid fibril peptides, we demonstrated that the spatial averaging in self-guided Langevin dynamics results in accelerated β-sheet formation.
自引导（SG）分子模拟方法，即自引导分子动力学（SGMD）和自引导朗之万动力学（SGLD），通过促进低频运动来增强构象搜索。一种简单的局部时间平均方案被用于以几乎不增加计算成本的方式提取低频特性。对于诸如配体结合和蛋白质折叠等形成有序结构的分子过程，人们认为协同运动起着关键作用。为了增强分子系统中的协同运动，我们提出了一种空间平均方案来提取局部区域的协同运动。基于空间平均应用引导力，自引导分子模拟可以增强协同运动并更有效地达到有序结构。通过对淀粉样纤维肽的模拟，我们证明了自引导朗之万动力学中的空间平均导致β-折叠的加速形成。
