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正则系综中生物分子系统的高效无偏采样:自引导朗之万动力学综述

Efficient and Unbiased Sampling of Biomolecular Systems in the Canonical Ensemble: A Review of Self-Guided Langevin Dynamics.

作者信息

Wu Xiongwu, Damjanovic Ana, Brooks Bernard R

机构信息

Laboratory of Computational Biology, National Heart, Lung, and Blood Institute (NHLBI), National Institutes of Health(NIH), 5635 Fishers Lane, Room T900, Bethesda, MD 20892-9314.

出版信息

Adv Chem Phys. 2012 Jan 31;150:255-326. doi: 10.1002/9781118197714.ch6.

Abstract

This review provides a comprehensive description of the self-guided Langevin dynamics (SGLD) and the self-guided molecular dynamics (SGMD) methods and their applications. Example systems are included to provide guidance on optimal application of these methods in simulation studies. SGMD/SGLD has enhanced ability to overcome energy barriers and accelerate rare events to affordable time scales. It has been demonstrated that with moderate parameters, SGLD can routinely cross energy barriers of 20 kT at a rate that molecular dynamics (MD) or Langevin dynamics (LD) crosses 10 kT barriers. The core of these methods is the use of local averages of forces and momenta in a direct manner that can preserve the canonical ensemble. The use of such local averages results in methods where low frequency motion "borrows" energy from high frequency degrees of freedom when a barrier is approached and then returns that excess energy after a barrier is crossed. This self-guiding effect also results in an accelerated diffusion to enhance conformational sampling efficiency. The resulting ensemble with SGLD deviates in a small way from the canonical ensemble, and that deviation can be corrected with either an on-the-fly or a post processing reweighting procedure that provides an excellent canonical ensemble for systems with a limited number of accelerated degrees of freedom. Since reweighting procedures are generally not size extensive, a newer method, SGLDfp, uses local averages of both momenta and forces to preserve the ensemble without reweighting. The SGLDfp approach is size extensive and can be used to accelerate low frequency motion in large systems, or in systems with explicit solvent where solvent diffusion is also to be enhanced. Since these methods are direct and straightforward, they can be used in conjunction with many other sampling methods or free energy methods by simply replacing the integration of degrees of freedom that are normally sampled by MD or LD.

摘要

本综述全面描述了自引导朗之万动力学(SGLD)和自引导分子动力学(SGMD)方法及其应用。文中包含示例系统,为这些方法在模拟研究中的最佳应用提供指导。SGMD/SGLD克服能量障碍和将罕见事件加速到可承受时间尺度的能力有所增强。已经证明,在适度参数下,SGLD能够以分子动力学(MD)或朗之万动力学(LD)穿越10kT障碍的速率,常规穿越20kT的能量障碍。这些方法的核心是以直接方式使用力和动量的局部平均值,从而能够保持正则系综。使用这种局部平均值会产生这样的方法:当接近障碍时,低频运动从高频自由度“借用”能量,然后在越过障碍后返还多余能量。这种自引导效应还会导致加速扩散,以提高构象采样效率。使用SGLD得到的系综与正则系综略有偏差,这种偏差可以通过即时或后处理重加权程序进行校正,该程序为具有有限数量加速自由度的系统提供了出色的正则系综。由于重加权程序通常不具有广延性,一种更新的方法SGLDfp使用动量和力的局部平均值来保持系综而无需重加权。SGLDfp方法具有广延性,可用于加速大型系统中的低频运动,或用于具有显式溶剂且溶剂扩散也需增强的系统。由于这些方法直接明了,它们可以通过简单地替换通常由MD或LD采样的自由度积分,与许多其他采样方法或自由能方法结合使用。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1bb3/3731171/691e39117a48/nihms412884f1a.jpg

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