Accurate Binding Free Energy Method from End-State MD Simulations - Akkus, Ebru; Tayfuroglu, Omer; Yildiz, Muslum; Kocak, Abdulkadir (Amer Chemical Soc, 2022)
Herein, we introduce a new strategy to estimate binding free energies using end-state molecular dynamics simulation trajectories. The method is adopted from linear interaction energy (LIE) and ANI-2x neural network potentials (machine learning) for the atomic simulation environment (ASE). It predicts the single-point interaction energies between ligand-protein and ligand- solvent pairs at the accuracy of the wb97x/6-31G* level for the conformational space that is sampled by molecular dynamics (MD) simulations. Our results on 54 protein-ligand complexes show that the method can be accurate and have a correlation of R = 0.87-0.88 to the experimental binding free energies, outperforming current end-state methods with reduced computational cost. The method also allows us to compare BFEs of ligands with different scaffolds. The code is available free of charge (documentation and test files) at https://github.com/otayfuroglu/deepQM.
Keywords: Molecular-Dynamics Simulations; Bennetts Acceptance Ratio; Thermodynamic Integration; Linear-Response; Inhibitors; Docking; Crystallography; Neuraminidase; Convergence; Performance