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Multidimensional Replica Exchange Simulations for Efficient Constant pH and Redox Potential Molecular Dynamics.

Vinícius Wilian D Cruzeiro1, Adrian E Roitberg1

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Multidimensional replica exchange simulations now support pH and redox potential (E) in AMBER. This enhanced sampling technique improves simulation convergence for molecular dynamics, offering atomic-level insights beyond experimental capabilities.

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Area of Science:

  • Computational Chemistry
  • Biophysics
  • Molecular Dynamics

Background:

  • Efficient computational methods are crucial for atomic-level insights complementing experimental data.
  • Replica Exchange (REMD) accelerates converged results in molecular dynamics simulations.
  • Existing multidimensional REMD simulations primarily focused on temperature and Hamiltonian dimensions.

Purpose of the Study:

  • To implement and validate pH and redox potential (E) as dimensions in multidimensional REMD simulations within the AMBER software.
  • To assess the impact of these new dimensions on sampling convergence and computational performance.
  • To investigate the influence of temperature, pH, and redox potential on molecular systems.

Main Methods:

  • Implementation of E,pH-REMD, E,T-REMD, and E,T,pH-REMD in AMBER, including GPU acceleration.
  • Simulation of N-acetylmicroperoxidase-8 (NAcMP8) attached to a histidine peptide.
  • Comparison of multidimensional REMD results against one-dimensional REMD and non-REMD simulations.

Main Results:

  • Two-dimensional REMD simulations demonstrated improved sampling convergence over one-dimensional REMD.
  • Three-dimensional REMD further enhanced convergence compared to two-dimensional REMD.
  • Multidimensional REMD exhibited comparable computational performance to one-dimensional REMD, despite requiring more replicas.

Conclusions:

  • The implemented multidimensional REMD approach effectively enhances sampling convergence for molecular simulations.
  • This method provides valuable atomic-level data for systems under varying pH and redox conditions.
  • Computational results align with theoretical predictions, validating the approach.