Non-bonded force field parameters from MBIS partitioning of the molecular electron density improve binding affinity predictions of the T4-lysozyme double mutant

Binding affinity prediction using computer simulation has been increasingly incorporated in drug discovery projects. Its wide application, however, is limited by the prediction accuracy of the free energy calculations. The main error sources are force fields used to describe molecular interactions and incomplete sampling of the configurational space. To improve the quality of the force field, we developed a Python-based computational workflow. The workflow described here uses the Minimal Basis Iterative Stockholder method (MBIS) to determine atomic charges and Lennard-Jones parameters from the polarized molecular density. This is done by performing electronic structure calculations on various configurations of the ligand, both when it is bound and unbound. In addition, we have validated a simulation procedure that accounts for the protein and the ligand degrees of freedom to precisely calculate binding free energies. This was achieved by comparing the self-adjusted mixture sampling and non-equilibrium thermodynamic integration methods using various protein and ligand conformations. The accuracy of predicting binding affinity is improved by using MBIS derived force field parameters and the validated simulation procedure. This improvement surpasses the chemical precision for the eight aromatic ligands reaching a root mean square error (RMSE) of 0.7 kcal/mol.