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Multiscale Sampling of a Heterogeneous Water/Metal Catalyst Interface using Density Functional Theory and Force-Field Molecular Dynamics
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Constant-pH Hybrid Nonequilibrium Molecular Dynamics-Monte Carlo Simulation Method.

Yunjie Chen1, Benoît Roux1

  • 1Department of Biochemistry and Molecular Biology, Department of Chemistry, University of Chicago , Chicago, Illinois 60637, United States.

Journal of Chemical Theory and Computation
|August 25, 2015
PubMed
Summary

A new computational method enables explicit solvent simulations of complex molecules at constant pH. This advanced technique efficiently handles protonation and deprotonation for large molecular systems, improving simulation accuracy.

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Area of Science:

  • Computational Chemistry
  • Molecular Dynamics
  • Biophysics

Background:

  • Simulating molecular systems at constant pH is crucial for understanding biological processes.
  • Existing methods face challenges with computational cost and accuracy for large systems.

Purpose of the Study:

  • To develop an efficient computational method for explicit solvent simulations at constant pH.
  • To enable accurate modeling of protonation/deprotonation events in complex molecular systems.

Main Methods:

  • A hybrid scheme combining nonequilibrium molecular dynamics (neMD) and Metropolis Monte Carlo (MC) criteria.
  • A two-step approach separating protonation state assignment and neMD switching for enhanced efficiency.
  • Utilizing intrinsic pKa values and symmetric two-ends momentum reversal for detailed balance.

Main Results:

  • The method successfully simulates protonation/deprotonation of ionizable sites in complex molecular systems.
  • Tested on single amino acids and protein systems (turkey ovomucoid third domain, lysozyme).
  • Demonstrated a linear increase in computational cost with the number of titratable sites, allowing scalability.

Conclusions:

  • The developed hybrid neMD-MC method provides an efficient and accurate approach for constant-pH simulations.
  • This method is well-suited for studying large and complex molecular systems, advancing computational biochemistry.