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

Xiongwu Wu, Ana Damjanovic, Bernard R Brooks
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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.

典型集合中生物分子系统的高效无偏采样:自导朗温动力学综述》。
本综述全面介绍了自导朗格文动力学(SGLD)和自导分子动力学(SGMD)方法及其应用。文中还包括示例系统,为这些方法在模拟研究中的最佳应用提供指导。SGMD/SGLD 具有更强的能力来克服能量障碍,并将罕见事件加速到可承受的时间尺度。事实证明,在参数适中的情况下,SGLD 能够以分子动力学(MD)或朗格文动力学(LD)跨越 10 kT 能量障碍的速度常规跨越 20 kT 能量障碍。这些方法的核心是以直接方式使用力和力矩的局部平均值,从而保留典型集合。使用这种局部平均的结果是,当接近势垒时,低频运动从高频自由度 "借用 "能量,然后在越过势垒后返回多余的能量。这种自导效应也会导致加速扩散,从而提高构象采样效率。使用 SGLD 得出的集合与典型集合的偏差较小,这种偏差可以通过即时或后处理的重新加权程序进行校正,从而为加速自由度数量有限的系统提供出色的典型集合。由于重新加权程序一般不涉及大小问题,一种较新的方法 SGLDfp 使用力矩和力的局部平均值来保留集合,而无需重新加权。SGLDfp 方法具有尺寸广阔性,可用于加速大型系统中的低频运动,或在有明确溶剂的系统中加速溶剂扩散。由于这些方法直接而简单,它们可以与许多其他采样方法或自由能方法结合使用,只需替换通常由 MD 或 LD 采样的自由度积分即可。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Advances in Chemical Physics
Advances in Chemical Physics PHYSICS, ATOMIC, MOLECULAR & CHEMICAL-
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0
审稿时长
1.0 months
期刊介绍: A landmark in publishing and science, Advances in Chemical Physics is an international forum for the review and critical evaluation of the science that has propelled every area of the discipline. Each volume contains discussions of aspects of the state of diverse subjects in chemical physics and related fields, with chapters written by top researchers in the field from around the world. The series now comprises more than 150 volumes covering the period from the mid 1960’s to the present. Collectively, they represent the history of modern chemical physics. Discussions of all areas of chemical physics, with extensions to biophysics and soft matter physics can be found in these volumes.
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