Laboratory of Computational Biology, National Heart, Lung, and Blood Institute (NHLBI), National Institutes of Health (NIH), Bethesda, Maryland 20892, USA.
J Chem Phys. 2011 Nov 28;135(20):204101. doi: 10.1063/1.3662489.
The self-guided Langevin dynamics (SGLD) is a method to accelerate conformational searching. This method is unique in the way that it selectively enhances and suppresses molecular motions based on their frequency to accelerate conformational searching without modifying energy surfaces or raising temperatures. It has been applied to studies of many long time scale events, such as protein folding. Recent progress in the understanding of the conformational distribution in SGLD simulations makes SGLD also an accurate method for quantitative studies. The SGLD partition function provides a way to convert the SGLD conformational distribution to the canonical ensemble distribution and to calculate ensemble average properties through reweighting. Based on the SGLD partition function, this work presents a force-momentum-based self-guided Langevin dynamics (SGLDfp) simulation method to directly sample the canonical ensemble. This method includes interaction forces in its guiding force to compensate the perturbation caused by the momentum-based guiding force so that it can approximately sample the canonical ensemble. Using several example systems, we demonstrate that SGLDfp simulations can approximately maintain the canonical ensemble distribution and significantly accelerate conformational searching. With optimal parameters, SGLDfp and SGLD simulations can cross energy barriers of more than 15 kT and 20 kT, respectively, at similar rates for LD simulations to cross energy barriers of 10 kT. The SGLDfp method is size extensive and works well for large systems. For studies where preserving accessible conformational space is critical, such as free energy calculations and protein folding studies, SGLDfp is an efficient approach to search and sample the conformational space.
自导向朗之万动力学（SGLD）是一种加速构象搜索的方法。该方法的独特之处在于，它可以根据分子运动的频率选择性地增强和抑制分子运动，从而在不改变能量表面或提高温度的情况下加速构象搜索。它已被应用于许多长时间尺度事件的研究，如蛋白质折叠。最近对 SGLD 模拟中构象分布的理解进展使得 SGLD 也成为定量研究的一种准确方法。SGLD 配分函数提供了一种将 SGLD 构象分布转换为正则系综分布并通过重新加权计算系综平均性质的方法。基于 SGLD 配分函数，本文提出了一种基于力-动量的自导向朗之万动力学（SGLDfp）模拟方法，以直接对正则系综进行采样。该方法在其导向力中包含相互作用力，以补偿基于动量的导向力引起的扰动，从而可以近似地对正则系综进行采样。通过几个示例系统，我们证明了 SGLDfp 模拟可以近似保持正则系综分布并显著加速构象搜索。使用最优参数，SGLDfp 和 SGLD 模拟可以以相似的速率分别跨越超过 15 kT 和 20 kT 的能量势垒，而 LD 模拟跨越 10 kT 的能量势垒。SGLDfp 方法是尺寸扩展性的，适用于大型系统。对于保存可及构象空间至关重要的研究，如自由能计算和蛋白质折叠研究，SGLDfp 是一种有效的搜索和采样构象空间的方法。
