The generalized Born (GB) model is one of the fastest implicit solvent models and it has become widely adopted for Molecular Dynamics (MD) simulations. This speed comes with tradeoffs, and many reports in the literature have pointed out weaknesses with GB models. Because the quality of a GB model is heavily affected by empirical parameters used in calculating solvation energy, here, we refit these parameters for the recently developed GB-Neck model, in order to improve the accuracy of solvation energy and effective radii calculation. Comparing to other pairwise GB models like GB-OBC and GB-Neck, the new GB models have better agreement to Poisson-Boltzmann (PB) in terms of reproducing solvation energies for a variety of systems from protein to nucleic acid. For the protein, secondary structure preferences are also in much better agreement with explicit solvent simulations. We also obtain near-quantitative reproduction of experimental structure and thermal stability profiles for several model peptides. For the nucleic acid, simulations maintain stable trajectories for various DNA and RNA duplexes through MD simulations and also correctly fold a DNA hairpin and an A-form RNA duplex from a B-form conformation (DNA) and an incorrect duplex structure (RNA).

Improved generalized Born solvent model parameters for protein and nucleic acid simulations

244th National Meeting (2012)

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