Could MM-GBSA be accurate enough for calculation of absolute protein/ligand binding free energies?

• MM-GBSA is generally believed to be successful in predicting relative binding free energies for a congeneric series of ligands.
• We show that Prime 3.0, with a novel energy function, could be approaching the accuracy required for prediction of absolute free energies.
• The data-set used for validation was carefully selected to control for variability in the affinity data as well as error in the modeled protein/ligand complexes.
• A linear regression analysis of the predicted versus experimental affinities gave a regression R2 of 0.89 for our most accurate data-set.

Implicit solvation methods such as MM-GBSA, when applied to evaluating protein/ligand binding free energies, are widely believed to be accurate only for the estimation of relative binding free energies for a congeneric series of ligands. In this work, we show that the MM-GBSA flavor of Prime 3.0, VSGB-2.0, with a variable dielectric model and a novel energy function, could be approaching the accuracy required for evaluating absolute binding free energies, albeit, through a linear regression fit. The data-set used for validation includes 106 protein–ligand complexes that were carefully selected to control for variability in the affinity data as well as error in the modeled complexes. Through systematic analysis, we also quantify the degradation in the R2 of fit between experimental and calculated values with either greater variability in the affinity data or an increase in error in the modeled protein/ligand complexes. Limitations for its application in drug discovery are discussed along with the identification of areas for future development.

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