Running Gaussian-accelerated Molecular Dynamics Simulations in NAMD [Article v1.0].

Haley M Michel, Marcelo D Polêto, Justin A Lemkul
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Abstract

Gaussian-accelerated molecular dynamics (GaMD) simulations are an advanced technique that enhances the sampling of configurational space by applying biasing potentials that reduce energy barriers, enabling faster exploration of the free energy landscape. This tutorial demonstrates the application of GaMD to the alanine dipeptide, serving as an accessible model system, and guides users through all GaMD simulation stages: conventional MD, GaMD equilibration, GaMD production, and reweighting. Users will gain practical insights into the preparation of input files, monitoring of GaMD convergence, and analysis of free energy profiles using PyReweighting. We make a particular effort to connect the underlying theory with the GaMD workflow. This tutorial is intended for users with prior molecular dynamics experience, Linux and command-line navigation, and with basic Python knowledge. The step-by-step instructions and accompanying scripts aim to streamline the GaMD workflow, making it accessible for the broader research community to explore enhanced sampling for a range of biomolecular systems.

在NAMD中运行高斯加速分子动力学模拟[第v1.0篇]。
高斯加速分子动力学(GaMD)模拟是一种先进的技术,它通过应用减少能量障碍的偏置势来增强构型空间的采样,从而更快地探索自由能景观。本教程演示了GaMD对丙氨酸二肽的应用,作为一个可访问的模型系统,并指导用户通过所有GaMD模拟阶段:常规MD, GaMD平衡,GaMD生产和重新加权。用户将获得实际的见解,准备输入文件,监测GaMD收敛,并使用PyReweighting分析自由能剖面。我们特别努力将基础理论与gad工作流程联系起来。本教程面向具有先前分子动力学经验、Linux和命令行导航以及具有基本Python知识的用户。逐步说明和随附的脚本旨在简化GaMD工作流程,使其可用于更广泛的研究界,以探索一系列生物分子系统的增强采样。
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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