g_wham-A Free Weighted Histogram Analysis Implementation Including Robust Error and Autocorrelation Estimates
g_wham-A Free Weighted Histogram Analysis Implementation Including Robust Error and Autocorrelation Estimates
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DOI：
10.1021/ct100494z
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发表时间：
2010-12-01
影响因子：
5.5
通讯作者：
van der Spoel, David
中科院分区：
文献类型：
--
作者：
Hub, Jochen S.;de Groot, Bert L.;van der Spoel, David
加权直方图分析方法（WHAM）是一种用于从一组伞形采样模拟中计算平均力势（PMFs）的标准技术。在这里，我们提出了一个新的WHAM实现，称为g_wham，它与GROMACS分子模拟套件一起免费分发。G_wham使用自举分析技术估计统计误差。支持三种引导方法：(i)基于伞直方图引导新轨迹，（ii）完全直方图的引导，以及（iii）完全直方图的贝叶斯引导，即通过向直方图分配随机权重进行引导。由于方法ii和方法iii只考虑完整的直方图作为独立的数据点，因此这些方法不需要精确计算自相关时间。我们证明，给定足够的采样，自举新的轨迹允许一个准确的误差估计。然而，在存在长自相关的情况下，（贝叶斯）对完整直方图的自举产生更可靠的误差估计，而对新轨迹的自举可能会低估误差。此外，我们强调，将自相关性纳入WHAM可以减少有限采样的偏差，特别是在计算溶剂化脂质膜或蛋白质通道等非均匀系统中的周期性PMFs时。
The Weighted Histogram Analysis Method (WHAM) is a standard technique used to compute potentials of mean force (PMFs) from a set of umbrella sampling simulations. Here, we present a new WHAM implementation, termed g_wham, which is distributed freely with the GROMACS molecular simulation suite. g_wham estimates statistical errors using the technique of bootstrap analysis. Three bootstrap methods are supported: (i) bootstrapping new trajectories based on the umbrella histograms, (ii) bootstrapping of complete histograms, and (iii) Bayesian bootstrapping of complete histograms, that is, bootstrapping via the assignment of random weights to the histograms. Because methods ii and iii consider only complete histograms as independent data points, these methods do not require the accurate calculation of autocorrelation times. We demonstrate that, given sufficient sampling, bootstrapping new trajectories allows for an accurate error estimate. In the presence of long autocorrelations, however, (Bayesian) bootstrapping of complete histograms yields a more reliable error estimate, whereas bootstrapping of new trajectories may underestimate the error. In addition, we emphasize that the incorporation of autocorrelations into WHAM reduces the bias from limited sampling, in particular, when computing periodic PMFs in inhomogeneous systems such as solvated lipid membranes or protein channels.