4.6 Review

Gaussian accelerated molecular dynamics: Principles and applications

Publisher

WILEY
DOI: 10.1002/wcms.1521

Keywords

drug binding; free energy calculations; enhanced sampling; membrane proteins; protein; nucleic acid complexes

Funding

  1. American Heart Association [17SDG33370094]
  2. Extreme Science and Engineering Discovery Environment (XSEDE) [TG-MCB160059, TG-MCB180049]
  3. National Energy Research Scientific Computing Center (NERSC) [M2874]
  4. National Institute of Health [R01EY027440, R01GM132572]
  5. National Science Foundation [CHE-1905374]

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Gaussian accelerated molecular dynamics (GaMD) is a robust computational method that accelerates biomolecular simulations by adding a harmonic boost potential, allowing for energetic reweighting via cumulant expansion to accurately reconstruct free energy landscapes. Hybrid schemes with other enhanced sampling methods have been introduced to further improve sampling and free energy calculations, enabling efficient characterization of biomolecular dynamics and interactions.
Gaussian accelerated molecular dynamics (GaMD) is a robust computational method for simultaneous unconstrained enhanced sampling and free energy calculations of biomolecules. It works by adding a harmonic boost potential to smooth biomolecular potential energy surface and reduce energy barriers. GaMD greatly accelerates biomolecular simulations by orders of magnitude. Without the need to set predefined reaction coordinates or collective variables, GaMD provides unconstrained enhanced sampling and is advantageous for simulating complex biological processes. The GaMD boost potential exhibits a Gaussian distribution, thereby allowing for energetic reweighting via cumulant expansion to the second order (i.e., Gaussian approximation). This leads to accurate reconstruction of free energy landscapes of biomolecules. Hybrid schemes with other enhanced sampling methods, such as the replica-exchange GaMD (rex-GaMD) and replica-exchange umbrella sampling GaMD (GaREUS), have also been introduced, further improving sampling and free energy calculations. Recently, new selective GaMD algorithms including the Ligand GaMD (LiGaMD) and Peptide GaMD (Pep-GaMD) enabled microsecond simulations to capture repetitive dissociation and binding of small-molecule ligands and highly flexible peptides. The simulations then allowed highly efficient quantitative characterization of the ligand/peptide binding thermodynamics and kinetics. Taken together, GaMD and its innovative variants are applicable to simulate a wide variety of biomolecular dynamics, including protein folding, conformational changes and allostery, ligand binding, peptide binding, protein-protein/nucleic acid/carbohydrate interactions, and carbohydrate/nucleic acid interactions. In this review, we present principles of the GaMD algorithms and recent applications in biomolecular simulations and drug design. This article is categorized under: Structure and Mechanism > Computational Biochemistry and Biophysics Molecular and Statistical Mechanics > Molecular Dynamics and Monte-Carlo Methods Molecular and Statistical Mechanics > Free Energy Methods

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