Accuracy Comparison of Generalized Born Models in the Calculation of Electrostatic Binding Free Energies

The need for accurate
yet efficient representation of the aqueous
environment in biomolecular modeling has led to the development of
a variety of generalized Born (GB) implicit solvent models. While
many studies have focused on the accuracy of available GB models in
predicting solvation free energies, a systematic assessment of the
quality of these models in binding free energy calculations, crucial
for rational drug design, has not been undertaken. Here, we evaluate
the accuracies of eight common GB flavors (GB-HCT, GB-OBC, GB-neck2,
GBNSR6, GBSW, GBMV1, GBMV2, and GBMV3), available in major molecular
dynamics packages, in predicting the electrostatic binding free energies
(ΔΔGel) for
a diverse set of 60 biomolecular complexes belonging to four main
classes: protein–protein, protein-drug, RNA-peptide, and small
complexes. The GB flavors are examined in terms of their ability to
reproduce the results from the Poisson–Boltzmann (PB) model,
commonly used as accuracy reference in this context. We show that
the agreement with the PB of ΔΔGel estimates varies widely between different
GB models and also across different types of biomolecular complexes,
with R2 correlations ranging from 0.3772
to 0.9986. A surface-based “R6” GB model recently implemented
in AMBER shows the closest overall agreement with reference PB (R2 = 0.9949, RMSD = 8.75 kcal/mol). The RNA-peptide
and protein-drug complex sets appear to be most challenging for all
but one model, as indicated by the large deviations from the PB in ΔΔGel. Small neutral
complexes present the least challenge for most of the GB models tested.
The quantitative demonstration of the strengths and weaknesses of
the GB models across the diverse complex types provided here can be
used as a guide for practical computations and future development
efforts.