We introduce a Gaussian-accelerated molecular dynamics (GaMD), deep learning (DL), and free energy profiling workflow (GLOW) to predict molecular determinants and map free energy landscapes of biomolecules. All-atom GaMD-enhanced sampling simulations are first performed on biomolecules of interest. Structural contact maps are then calculated from GaMD simulation frames and transformed into images for building DL models using a convolutional neural network. Important structural contacts are further determined from DL models of attention maps of the structural contact gradients, which allow us to identify the system reaction coordinates. Finally, free energy profiles are calculated for the selected reaction coordinates through energetic reweighting of the GaMD simulations. We have also successfully demonstrated GLOW for the characterization of activation and allosteric modulation of a G protein-coupled receptor, using the adenosine A 1 receptor (A 1 AR) as a model system. GLOW findings are highly consistent with previous experimental and computational studies of the A 1 AR, while also providing further mechanistic insights into the receptor function. In summary, GLOW provides a systematic approach to mapping free energy landscapes of biomolecules. The GLOW workflow and its user manual can be downloaded at http://miaolab.org/GLOW.

Presenilin-1 (PS1) is the catalytic subunit of γ-secretase which cleaves within the transmembrane domain of over 150 peptide substrates. Dominant missense mutations in PS1 cause early-onset familial Alzheimer’s disease (FAD); however, the exact pathogenic mechanism remains unknown. Here we combined Gaussian accelerated molecular dynamics (GaMD) simulations and biochemical experiments to determine the effects of six representative PS1 FAD mutations (P117L, I143T, L166P, G384A, L435F, and L286V) on the enzyme-substrate interactions between γ-secretase and amyloid precursor protein (APP). Biochemical experiments showed that all six PS1 FAD mutations rendered γ-secretase less active for the endoproteolytic (ε) cleavage of APP. Distinct low-energy conformational states were identified from the free energy profiles of wildtype and PS1 FAD-mutant γ-secretase. The P117L and L286V FAD mutants could still sample the “Active” state for substrate cleavage, but with noticeably reduced conformational space compared with the wildtype. The other mutants hardly visited the “Active” state. The PS1 FAD mutants were found to reduce γ-secretase proteolytic activity by hindering APP residue L49 from proper orientation in the active site and/or disrupting the distance between the catalytic aspartates. Therefore, our findings provide mechanistic insights into how PS1 FAD mutations affect structural dynamics and enzyme-substrate interactions of γ-secretase and APP.

Biomolecular binding kinetics including the association (k on ) and dissociation (k of f ) rates are critical parameters for therapeutic design of smallmolecule drugs, peptides, and antibodies. Notably, the drug molecule residence time or dissociation rate has been shown to correlate with their efficacies better than binding affinities. A wide range of modeling approaches including quantitative structure-kinetic relationship models, Molecular Dynamics simulations, enhanced sampling, and Machine Learning has been developed to explore biomolecular binding and dissociation mechanisms and predict binding kinetic rates. Here, we review recent advances in computational modeling of biomolecular binding kinetics, with an outlook for future improvements.

Gaussian accelerated molecular dynamics (GaMD) is a computational technique that provides both unconstrained enhanced sampling and free energy calculations of biomolecules. Here, we present the implementation of GaMD in the OpenMM simulation package and validate it on model systems of alanine dipeptide and RNA folding. For alanine dipeptide, 30 ns GaMD production simulations reproduced free energy profiles of 1000 ns conventional molecular dynamics (cMD) simulations. In addition, GaMD simulations captured the folding pathways of three hyperstable RNA tetraloops (UUCG, GCAA, and CUUG) and binding of the rbt203 ligand to the HIV-1 Tar RNA, both of which involved critical electrostatic interactions such as hydrogen bonding and base stacking. Together with previous implementations, GaMD in OpenMM will allow for wider applications in simulations of proteins, RNA, and other biomolecules.

Caffeine (CFF) is a common antagonist to the four subtypes of adenosine G-protein-coupled receptors (GPCRs), which are critical drug targets for treating heart failure, cancer, and neurological diseases. However, the pathways and mechanism of CFF binding to the target receptors remain unclear. In this study, we have performed all-atom-enhanced sampling simulations using a robust Gaussian-accelerated molecular dynamics (GaMD) method to elucidate the binding mechanism of CFF to human adenosine A2A receptor (A2AAR). Multiple 500–1,000 ns GaMD simulations captured both binding and dissociation of CFF in the A2AAR. The GaMD-predicted binding poses of CFF were highly consistent with the x-ray crystal conformations with a characteristic hydrogen bond formed between CFF and residue N6.55 in the receptor. In addition, a low-energy intermediate binding conformation was revealed for CFF at the receptor extracellular mouth between ECL2 and TM1. While the ligand-binding pathways of the A2AAR were found similar to those of other class A GPCRs identified from previous studies, the ECL2 with high sequence divergence serves as an attractive target site for designing allosteric modulators as selective drugs of the A2AAR.