Using the Maximum Entropy Principle to Combine Simulations and Solution Experiments

Abstract

Molecular dynamics (MD) simulations allow investigating the structural dynamics of biomolecular systems with unrivaled time and space resolution. However, in order to compensate for the inaccuracies of the utilized empirical force fields, it is becoming common to integrate MD simulations with experimental data obtained from ensemble measurements. We here review the approaches that can be used to combine MD and experiment under the guidance of the maximum entropy principle. We mostly focus on methods based on Lagrangian multipliers, either implemented as reweighting of existing simulations or through an on-the-fly optimization. We discuss how errors in the experimental data can be modeled and accounted for. Finally, we use simple model systems to illustrate the typical difficulties arising when applying these methods.

Submitted to Computation

References in corpus (10)

• A Variational Approach to Enhanced Sampling and Free Energy Calculations
• Effective Sample Size for Importance Sampling based on discrepancy measures
• Computer Folding of RNA Tetraloops: Identification of Key Force Field Deficiencies
• Combining simulations and solution experiments as a paradigm for RNA force field refinement
• Free Energy Landscape of GAGA and UUCG RNA Tetraloops
• Unravelling Mg${}^{2+}$-RNA binding with atomistic molecular dynamics
• Exploring RNA structure and dynamics through enhanced sampling simulations
• Shape Transitions and Chiral Symmetry Breaking in the Energy Landscape of the Mitotic Chromosome
• Empirical corrections to the Amber RNA force field with Target Metadynamics
• Improved Ab Initio Molecular Dynamics by Minimal Biasing with Experimental Data

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• MDRefine: a Python package for refining Molecular Dynamics trajectories with experimental data
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