Chemistry Department, Stanford University, Stanford, California 94305, USA.
Pacific Northwest National Laboratory, Richland, Washington 99352, USA.
J Chem Phys. 2017 Aug 21;147(7):074115. doi: 10.1063/1.4999097.
Molecular dynamics simulations are useful in obtaining thermodynamic and kinetic properties of bio-molecules, but they are limited by the time scale barrier. That is, we may not obtain properties' efficiently because we need to run microseconds or longer simulations using femtosecond time steps. To overcome this time scale barrier, we can use the weighted ensemble (WE) method, a powerful enhanced sampling method that efficiently samples thermodynamic and kinetic properties. However, the WE method requires an appropriate partitioning of phase space into discrete macrostates, which can be problematic when we have a high-dimensional collective space or when little is known a priori about the molecular system. Hence, we developed a new WE-based method, called the "Concurrent Adaptive Sampling (CAS) algorithm," to tackle these issues. The CAS algorithm is not constrained to use only one or two collective variables, unlike most reaction coordinate-dependent methods. Instead, it can use a large number of collective variables and adaptive macrostates to enhance the sampling in the high-dimensional space. This is especially useful for systems in which we do not know what the right reaction coordinates are, in which case we can use many collective variables to sample conformations and pathways. In addition, a clustering technique based on the committor function is used to accelerate sampling the slowest process in the molecular system. In this paper, we introduce the new method and show results from two-dimensional models and bio-molecules, specifically penta-alanine and a triazine trimer.
分子动力学模拟在获取生物分子的热力学和动力学性质方面非常有用，但它们受到时间尺度障碍的限制。也就是说，我们可能无法有效地获得这些性质，因为我们需要使用飞秒时间步长运行微秒或更长时间的模拟。为了克服这个时间尺度障碍，我们可以使用加权系综（WE）方法，这是一种强大的增强采样方法，可以有效地对热力学和动力学性质进行采样。然而，WE 方法需要将相空间适当地划分为离散的巨正则系综，当我们有一个高维的集体空间或对分子系统事先了解甚少时，这可能会成为一个问题。因此，我们开发了一种新的基于 WE 的方法，称为“并发自适应采样（CAS）算法”，以解决这些问题。CAS 算法不受限于仅使用一个或两个集体变量，与大多数依赖反应坐标的方法不同。相反，它可以使用大量的集体变量和自适应巨正则系综来增强高维空间中的采样。对于我们不知道正确的反应坐标是什么的系统，这一点尤其有用，在这种情况下，我们可以使用许多集体变量来采样构象和途径。此外，还使用基于承诺函数的聚类技术来加速分子系统中最慢过程的采样。在本文中，我们介绍了这种新方法，并展示了二维模型和生物分子（具体为五肽和三聚氰胺）的结果。
