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

Abstract

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 ( ΔΔ G) 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 ΔΔ G estimates varies widely between different GB models and also across different types of biomolecular complexes, with R 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 ( R = 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 ΔΔ G. 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.

Generalized Born Implicit Solvent Models for Biomolecules

“…The specific choice of GB “flavor” depends on one’s needs: a protein folding simulation may call for one flavor of the GB model(84, 79), while a study of a protein in a membrane environment will need quite another(71, 20). The model one may prefer for estimates of ligand binding energies(47), can be different from GB flavors one may recommend for simulations of the DNA(118, 16, 80). Yet a different approach is needed if the molecular charge distribution is described by (polarizable) multipoles, rather than by fixed point charges.…”

“…(32)] A version of the model, GBNSR6(1, 27) (“Numerical Surface R6 GB”), is available in Amber. It has been tested in calculation of small molecule hydration energies(1) and in protein ligand binding(45), where its accuracy is noteworthy(47).…”

Theoretical Study of the β-Cyclodextrin Inclusion Complex Formation of Eugenol in Water

“…The number densities of presence or number of guest positions in each volume element was defined by a grid [ 36 , 37 ]. There are several generalized Born Models to calculate electrostatic binding free energies [ 40 ], but the electrostatic contribution to is nearly constant inside the cavity ( Figure 1 ) and thus in the trajectories. Moreover the van der Waals term is about times greater than the electrostatic potential energy, making the process of inclusion complex formation essentially dependent on this contribution.…”

MMGB/SA Consensus Estimate of the Binding Free Energy Between the Novel Coronavirus Spike Protein to the Human ACE2 Receptor

“…Generally speaking, GB models have shown to be computationally less expensive than the PB models, although the deterioration of the accuracy has always been a concern. Here, we employed a grid-based surface GB model called GBNSR6, 37 which, in a recent study, 38 was shown to be the most accurate among several GB models in terms of the ability to approximate Δ G pol relative to the numerical PB. In this work, Δ G pol is calculated with the ALPB modification 39 , 40 of the generalized Born 41 model: where ϵ in = 1 and ϵ out = 80 are the dielectric constants of the solute and the solvent, respectively, β = ϵ in / ϵ out , α = 0.571412, and A is the electrostatic size of the molecule, which is essentially the overall size of the structure that can be computed analytically.…”

“…Generally speaking, GB models have shown to be computationally less expensive than the PB models, although the deterioration of the accuracy has always been a concern. Here, we employed a grid-based surface GB model called GBNSR6, 37 which, in a recent study, 38 was shown to be the most accurate among several GB models in terms of the ability to approximate ∆G pol relative to the numerical PB. In this work, ∆G pol is calculated with the ALPB modification 39,40 of the generalized Born 41 model:…”