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Abstract 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 BiophysicsMolecular and Statistical Mechanics > Molecular Dynamics and Monte‐Carlo MethodsMolecular and Statistical Mechanics > Free Energy Methods
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Analysis of transition rates from variational flooding using analytical theory
Variational flooding is an enhanced sampling method for obtaining kinetic rates from molecular dynamics simulations. This method is inspired by the idea of conformational flooding that employs a boost potential acting along a chosen reaction coordinate to accelerate rare events. In this work, we show how the empirical distribution of crossing times from variational flooding simulations can be modeled with analytical Kramers’ time-dependent rate (KTR) theory. An optimized bias potential that fills metastable free energy basins is constructed from the variationally enhanced sampling (VES) method. This VES-derived flooding potential is then augmented by a switching function that determines the fill level of the boost. Having a prescribed time-dependent fill rate of the flooding potential gives an analytical expression for the distribution of crossing times from KTR theory that is used to extract unbiased rates. In the case of a static boost potential, the distribution of barrier crossing times follows an expected exponential distribution, and unbiased rates are extracted from a series of boosted simulations at discrete fill levels. Introducing a time-dependent boost that increases the fill level gradually over the simulation time leads to a simplified procedure for fitting the biased distribution of crossing times to analytical theory. We demonstrate the approach for the paradigmatic cases of alanine dipeptide in vacuum, the asymmetric SN2 reaction, and the folding of chignolin in explicit solvent.
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- Award ID(s):
- 2102189
- PAR ID:
- 10555910
- Publisher / Repository:
- AIP
- Date Published:
- Journal Name:
- The Journal of Chemical Physics
- Volume:
- 161
- Issue:
- 19
- ISSN:
- 0021-9606
- Subject(s) / Keyword(s):
- Variationally Enhanced Sampling Kinetics Enhanced Sampling Molecular Dynamics
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1063/5.0238289
