We have compared solvation free energies obtained from a number of approximate solvation models with an accurate solution of the Poisson−Boltzmann equation for a large data set of peptide structures, ranging from a single amino acid to a peptide sequence of length nine. The models are assessed for their ability to predict relative energetics of different peptide conformations (of the same sequence) as determined from the Poisson−Boltzmann results. We find that the widely used distance dependent dielectric model yields qualitatively erroneous results; in contrast, the generalized Born model of Still and co-workers, an approximation to the Poisson−Boltzmann equation, provides reasonably good solvation free energies and performs rather well in rank ordering of conformations. A surface area based model produces results of intermediate quality. Our results suggest that the generalized Born model is presently the clearly preferred alternative if one wishes to carry out molecular dynamics simulations with a fast, approximate solvation model.

We present the first analytical gradient Poisson−Boltzmann methodology which is routinely applicable to large biomolecular systems, such as proteins with sizes ranging from 500 to 5000 atoms. Full minimizations of several such systems in the gas phase and in solution are contrasted. Because the solvation free energy term is slowly varying with conformation, it can be evaluated infrequently in the simulation, and hence, reasonable computation times can be obtained even for large solvated systems.