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

Calculations of ligand binding free energies and kinetic rates are important for drug design.However, such tasks have proven challenging in computational chemistry and biophysics. To address this challenge, we have developed a new computational method "LiGaMD", which selectively boosts the ligand non-bonded interaction potential energy based on the Gaussian accelerated molecular dynamics (GaMD) enhanced sampling technique. Another boost potential could be applied to the remaining potential energy of the entire system in a dual-boost algorithm (LiGaMD_Dual) to facilitate ligand binding. LiGaMD has been demonstrated on host-guest and protein-ligand binding model systems. Repetitive guest binding and unbinding in the βcyclodextrin host were observed in hundreds-of-nanosecond LiGaMD simulations. The calculated binding free energies of guest molecules with sufficient sampling agreed excellently with experimental data (< 1.0 kcal/mol error). In comparison with previous microsecond-timescale conventional molecular dynamics simulations, accelerations of ligand kinetic rate constants in LiGaMD simulations were properly estimated using Kramers' rate theory. Furthermore, LiGaMD allowed us to capture repetitive dissociation and binding of the benzamidine inhibitor in trypsin within 1 μs simulations. The calculated ligand binding free energy and kinetic rate constants compared well with the experimental data. In summary, LiGaMD provides a promising approach for characterizing ligand binding thermodynamics and kinetics simultaneously, which is expected to facilitate computer-aided drug design.

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Amyloid β-peptide, the principal component of characteristic cerebral plaques of Alzheimer’s disease (AD), is produced through intramembrane proteolysis of the amyloid precursor protein (APP) by γ-secretase. Despite the importance in the pathogenesis of AD, the mechanisms of intramembrane proteolysis and substrate processing by γ-secretase remain poorly understood. Here, complementary all-atom simulations using a robust Gaussian accelerated molecular dynamics (GaMD) method and biochemical experiments were combined to investigate substrate processing of wildtype and mutant APP by γ-secretase. The GaMD simulations captured spontaneous activation of γ-secretase, with hydrogen bonded catalytic aspartates and water poised for proteolysis of APP at the ε cleavage site. Furthermore, GaMD simulations revealed that familial AD mutations I45F and T48P enhanced the initial ε cleavage between residues Leu49–Val50, while M51F mutation shifted the ε cleavage site to the amide bond between Thr48–Leu49. Detailed analysis of the GaMD simulations allowed us to identify distinct low-energy conformational states of γ-secretase, different secondary structures of the wildtype and mutant APP substrate, and important active-site subpockets for catalytic function of the enzyme. The simulation findings were highly consistent with experimental analyses of APP proteolytic products using mass spectrometry and Western blotting. Taken together, the GaMD simulations and biochemical experiments have enabled us to elucidate the mechanisms of γ-secretase activation and substrate processing, which should facilitate rational computer-aided drug design targeting this functionally important enzyme.

The membrane-embedded γ-secretase complex processively cleaves within the transmembrane domain of amyloid precursor protein (APP) to produce 37-to-43-residue amyloid β-peptides (Aβ) of Alzheimer’s disease (AD). Despite its importance in pathogenesis, the mechanism of processive proteolysis by γ-secretase remains poorly understood. Here, mass spectrometry and Western blotting were used to quantify the efficiency of tripeptide trimming of wild-type (WT) and familial AD (FAD) mutant Aβ49. In comparison to WT Aβ49, the efficiency of tripeptide trimming was similar for the I45F, A42T, and V46F Aβ49 FAD mutants but substantially diminished for the I45T and T48P mutants. In parallel with biochemical experiments, all-atom simulations using a novel peptide Gaussian accelerated molecular dynamics (Pep-GaMD) method were applied to investigate the tripeptide trimming of Aβ49 by γ-secretase. The starting structure was the active γ-secretase bound to Aβ49 and APP intracellular domain (AICD), as generated from our previous study that captured the activation of γ-secretase for the initial endoproteolytic cleavage of APP (BhattaraiA. Bhattarai, A. ACS Cent. Sci.20206969983). Pep-GaMD simulations captured remarkable structural rearrangements of both the enzyme and substrate, in which hydrogen-bonded catalytic aspartates and water became poised for tripeptide trimming of Aβ49 to Aβ46. These structural changes required a positively charged N-terminus of endoproteolytic coproduct AICD, which could dissociate during conformational rearrangements of the protease and Aβ49. The simulation findings were highly consistent with biochemical experimental data. Taken together, our complementary biochemical experiments and Pep-GaMD simulations have enabled elucidation of the mechanism of tripeptide trimming of Aβ49 by γ-secretase.