A Computational Workflow for Refining AF2 Models in Drug Design Using Kinetic and Thermodynamic Binding Calculations: A Case Study for the Unresolved Inactive human Adenosine A3 Receptor

A drug design pipeline that considers both thermodynamic and kinetic binding data of ligands against a receptor will enable the computational design of improved drug molecules. Here we studied a congeneric set of ligands that bind to structurally unresolved G protein coupled receptor (GPCR), the inactive human adenosine A3 receptor (hA3R). We tested three available homology models from which two have been generated from experimental structures of hA1R or hA2AR and one model was a multi-state alpha-fold2 (AF2)-derived model. We then applied alchemical calculations with thermodynamic integration coupled to MD simulations (TI/MD) to calculate the experimental relative binding free energies, and residence time (τ)-random accelerated MD simulations (τ-RAMD) to calculate the relative residence times (RTs) for antagonists. While the TI/MD calculations produce for the three homology models good Pearson correlation coefficient, correspondingly, r = 0.74, 0.62, 0.67 and mean unsigned error (mue) = 0.94, 1.31, 0.81 kcal mol-1 the τ-RAMD method showed r = 0.92, 0.52 for the first two models but failed to produce accurate results for the multi-state AF2-derived model. Subsequent optimization of the AF2-derived model by re-orientation of side chain of R1735.34 located in the extracellular loop 2 (EL2), that blocks ligand’s unbinding, the computational model showed r = 0.84 for kinetic data and improved performance for thermodynamic data (r = 0.81, mue = 0.56 kcal mol-1). Overall, after refining the multi state-AF2 model with physics-based tools, we were able to show strong correlation between predicted and experimental ligand relative residence times and affinities, achieving a level of accuracy comparable to an experimental structure. The computational workflow used can be applied to other receptor so helping to rank candidate drugs in a congeneric series, enabling prioritization of leads with stronger binding affinities and longer residence times.