RexDash: A Comprehensive Dashboard for Analyzing the Technical Performance of Replica Exchange Molecular Dynamics Simulations

Authors

  • SOHAM JAIN School of Systems Biology, George Mason University, Fairfax, VA
  • Christopher Lockhart School of Systems Biology, George Mason University, Fairfax, VA

DOI:

https://doi.org/10.13021/jssr2023.3926

Abstract

Replica exchange molecular dynamics (REMD) simulations have emerged as an effective tool to explore the conformational ensemble of biomolecular systems. By initiating several molecular dynamics simulations under different conditions and periodically swapping structures generated from adjacent conditions, REMD enhances sampling from simulations and facilitates the computation of thermodynamic properties. However, despite REMD’s advantages, there is neither a standardized technique nor a widely adopted toolkit to ensure that REMD simulations are performing as expected. We address these limitations by developing RexDash, an extensive dashboard that features various metrics for assessing the technical performance of REMD simulations. Currently, the dashboard implements Python as the front-end framework to display plots for exchange rates, replica mixing parameters, replica trajectories, and potential energy distributions. RexDash utilizes the Plotly graphing library to render these metrics for REMD data supplied by the user in comma-separated value format. In addition, RexDash employs HTML and Flask, a backend web framework that enables visualization of REMD simulation results by deploying the webpage as an online server. To validate and test metrics generated by the dashboard, we conducted REMD simulations of alanine dipeptide, a standard model system for molecular simulations. RexDash will provide future researchers and simulation practitioners with a readily available resource to analyze the technical setup of their REMD simulations and, therefore, is an important first step in the standardization of REMD results. 

Published

2023-10-27

Issue

Section

College of Science: School of Systems Biology

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