Methods such as energy minimization, protein-ligand vdW surface complementarity, and MM-GBSA are widely used in hit-to-lead and lead optimization to predict protein-ligand complex geometries and relative binding affinities given a common congeneric core. While quick to compute, their limited treatment of water’s role in protein-ligand binding and of ligand conformational ensembles in solution and protein makes them primarily enthalpically-driven with little accounting of system entropy. However, water displacement is commonly recognized as a primary driving force for protein-ligand binding and both water displacement and ligand conformational biasing are responsible for “magic methyls” suggesting such enthalpically-driven methods should be out-performed by methods that include these entropic effects. FEP+ predicts relative free energies of binding with explicit accounting of both water and ligand conformational entropy while docking with Glide accounts for both effects approximately using fast conformational search and an empirical, fitted scoring function, GlideScore. Correlations to experimental binding affinities for SAR from congeneric series across sixteen targets were examined using FEP+, docking with Glide, MM-GBSA, energy minimization, and protein-ligand vdW surface complementarity. FEP+ significantly outperforms all other methods examined in predicting relative free energy differences for congeneric series including GlideScore which shows little correlation. However, in a test of pose prediction using a dataset of crystal complex geometries, docking was demonstrated to be superior to protein-ligand vdW surface complementarity and energy minimization in the prediction of poses for the same ligand. These results suggest great care should be taken when using methods that don’t explicitly account for entropy in predicting relative free energies of ligands and at least approximately account for entropy in predicting protein-ligand poses.

Using the right tool for the job: Accounting for entropy is needed to accurately predict protein ligand binding affinities and complex geometries for congeneric series

Macromolecular Chemistry: The Second Century

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ACS Spring 2020 National Meeting & Expo

The official archive and repository for all ACS Meetings & Expositions abstracts and select posters or presentations.