Energetic Decomposition with the Generalized-Born and Poisson-Boltzmann Solvent Models: Lessons from Association of G-Protein Components

Continuum electrostatic models have been shown to be powerful tools in providing insight into the energetics of biomolecular processes. While the Poisson-Boltzmann (PB) equation provides a theoretically rigorous approach to computing electrostatic free energies of solution in such a model, computational cost makes its use for large ensembles of states impractical. The generalized-Born (GB) approximation provides a much faster alternative, although with a weaker theoretical framework. While much attention has been given to how GB recapitulates PB energetics for the overall stability of a biomolecule or the affinity of a complex, little attention has been given to how the contributions of individual functional groups are captured by the two methods. Accurately capturing these individual electrostatic components is essential both for the development of a mechanistic understanding of biomolecular processes and for the design of variant sequences and structures with desired properties. Here, we present a detailed comparison of the group-wise decomposition of both PB and GB electrostatic free energies of binding, using association of various components of the heterotrimeric-G-protein complex as a model. We find that, while net binding free energies are strongly correlated in the two models, the correlations of individual group contributions are highly variable; in some cases, strong correlation is seen, while in others, there is essentially none. Structurally, the GB model seems to capture the magnitude of direct, short-range electrostatic interactions quite well but performs more poorly with moderate-range "action-at-a-distance" interactions GB has a tendency to overestimate solvent screening over moderate distances, and to underestimate the costs of desolvating charged groups somewhat removed from the binding interface. Despite this, however, GB does seem to be quite effective as a predictor of those groups that will be computed to be most significant in a PB-based model.