Computational Neurophysiology Group, Institute of Complex Systems 4, Forschungszentrum Jülich, Jülich, Germany.
Department of Biochemistry and Biophysics, Science for Life Laboratory, Stockholms Universitet, Stockholm, Sweden.
Biophys J. 2019 Jan 8;116(1):4-11. doi: 10.1016/j.bpj.2018.11.3126. Epub 2018 Dec 1.
We introduce a computational toolset, named GROmaρs, to obtain and compare time-averaged density maps from molecular dynamics simulations. GROmaρs efficiently computes density maps by fast multi-Gaussian spreading of atomic densities onto a three-dimensional grid. It complements existing map-based tools by enabling spatial inspection of atomic average localization during the simulations. Most importantly, it allows the comparison between computed and reference maps (e.g., experimental) through calculation of difference maps and local and time-resolved global correlation. These comparison operations proved useful to quantitatively contrast perturbed and control simulation data sets and to examine how much biomolecular systems resemble both synthetic and experimental density maps. This was especially advantageous for multimolecule systems in which standard comparisons like RMSDs are difficult to compute. In addition, GROmaρs incorporates absolute and relative spatial free-energy estimates to provide an energetic picture of atomistic localization. This is an open-source GROMACS-based toolset, thus allowing for static or dynamic selection of atoms or even coarse-grained beads for the density calculation. Furthermore, masking of regions was implemented to speed up calculations and to facilitate the comparison with experimental maps. Beyond map comparison, GROmaρs provides a straightforward method to detect solvent cavities and average charge distribution in biomolecular systems. We employed all these functionalities to inspect the localization of lipid and water molecules in aquaporin systems, the binding of cholesterol to the G protein coupled chemokine receptor type 4, and the identification of permeation pathways through the dermicidin antimicrobial channel. Based on these examples, we anticipate a high applicability of GROmaρs for the analysis of molecular dynamics simulations and their comparison with experimentally determined densities.
我们引入了一个名为 GROmaρs 的计算工具包,用于从分子动力学模拟中获取和比较时间平均密度图。GROmaρs 通过快速将原子密度多高斯扩展到三维网格上来高效地计算密度图。它通过在模拟过程中进行原子平均定位的空间检查来补充现有的基于映射的工具。最重要的是,它允许通过计算差异图和局部和时间分辨的全局相关来比较计算的和参考的地图(例如实验)。这些比较操作对于定量对比扰动和对照模拟数据集以及检查生物分子系统与合成和实验密度图的相似程度非常有用。这对于多分子系统特别有利,因为像 RMSD 这样的标准比较很难计算。此外,GROmaρs 还包含绝对和相对空间自由能估计,以提供原子定位的能量图。这是一个基于 GROMACS 的开源工具包,因此允许对密度计算进行静态或动态选择原子甚至粗粒珠。此外,还实现了掩蔽区域以加快计算速度并促进与实验地图的比较。除了地图比较之外,GROmaρs 还提供了一种简单的方法来检测生物分子系统中的溶剂腔和平均电荷分布。我们利用所有这些功能来检查水通道蛋白系统中脂质和水分子的定位、胆固醇与 G 蛋白偶联趋化因子受体 4 的结合以及通过皮肤肽抗菌通道的渗透途径的识别。基于这些示例,我们预计 GROmaρs 将高度适用于分析分子动力学模拟及其与实验确定的密度的比较。