Long Time Scale Ensemble Methods in Molecular Dynamics: Ligand–Protein Interactions and Allostery in SARS-CoV‑2 Targets

• Published in: Journal of chemical theory and computation Vol. 19; no. 11; pp. 3359 - 3378
• Main Authors: Bhati, Agastya P., Hoti, Art, Potterton, Andrew, Bieniek, Mateusz K., Coveney, Peter V.
• Format: Journal Article
• Language: English
• Published: United States American Chemical Society 13.06.2023
• Subjects: Adaptive sampling; Adenosine; Binding sites; Biomolecular Systems; Chemistry; Chymotrypsin; Computational chemistry; Crystallography; Energy methods; Free energy; INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY; Ligands; Molecular dynamics; Papain; Peptides and proteins; Physics; Protease; Proteins; Ribose; Screening assays; Severe acute respiratory syndrome coronavirus 2; Simulation; Substrates; Time
• ISSN: 1549-9618; 1549-9626; 1549-9626
• DOI: 10.1021/acs.jctc.3c00020

We subject a series of five protein–ligand systems which contain important SARS-CoV-2 targets, 3-chymotrypsin-like protease (3CLPro), papain-like protease, and adenosine ribose phosphatase, to long time scale and adaptive sampling molecular dynamics simulations. By performing ensembles of ten or twelve 10 μs simulations for each system, we accurately and reproducibly determine ligand binding sites, both crystallographically resolved and otherwise, thereby discovering binding sites that can be exploited for drug discovery. We also report robust, ensemble-based observation of conformational changes that occur at the main binding site of 3CLPro due to the presence of another ligand at an allosteric binding site explaining the underlying cascade of events responsible for its inhibitory effect. Using our simulations, we have discovered a novel allosteric mechanism of inhibition for a ligand known to bind only at the substrate binding site. Due to the chaotic nature of molecular dynamics trajectories, regardless of their temporal duration individual trajectories do not allow for accurate or reproducible elucidation of macroscopic expectation values. Unprecedentedly at this time scale, we compare the statistical distribution of protein–ligand contact frequencies for these ten/twelve 10 μs trajectories and find that over 90% of trajectories have significantly different contact frequency distributions. Furthermore, using a direct binding free energy calculation protocol, we determine the ligand binding free energies for each of the identified sites using long time scale simulations. The free energies differ by 0.77 to 7.26 kcal/mol across individual trajectories depending on the binding site and the system. We show that, although this is the standard way such quantities are currently reported at long time scale, individual simulations do not yield reliable free energies. Ensembles of independent trajectories are necessary to overcome the aleatoric uncertainty in order to obtain statistically meaningful and reproducible results. Finally, we compare the application of different free energy methods to these systems and discuss their advantages and disadvantages. Our findings here are generally applicable to all molecular dynamics based applications and not confined to the free energy methods used in this study.