ArticleWiley interdisciplinary reviews. Computational molecular science
Gaussian accelerated molecular dynamics (GaMD): principles and applications.
Article in Wiley interdisciplinary reviews. Computational molecular science. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 138 papers.
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138 citing papers in PubMed.
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- Boosting Drug Discovery: Expanding the Applicability of Fragment Dissolved Molecular Dynamics to Accelerate Binding Mode Elucidation.Journal of chemical information and modeling · 2025Article
- Markovian state models uncover casein kinase 1 dynamics that govern circadian period.Biophysical journal · 2025Article
- Reaction Engineering of In Vitro Natural Product Biosynthesis: Challenges and Strategies.Chembiochem : a European journal of chemical biology · 2025Review
- The Effects of Heparin Binding and Arg596 Mutations on the Conformation of Thrombin-Antithrombin Michaelis Complex, Revealed by Enhanced Sampling Molecular Dynamics Simulations.International journal of molecular sciences · 2025Article
- In Silico exploration of Karanjin for anti-obesity potential through ADMET profiling, network pharmacology, molecular docking, and molecular dynamics simulations.Scientific reports · 2025Article
- Investigation of the Substrate Selection Mechanism of Poly (A) Polymerase Based on Molecular Dynamics Simulations and Markov State Model.International journal of molecular sciences · 2025Article
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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.
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