Abstract
Structural refinement of proteins involves the minimization of a target function that combines X-ray data with a set of restraints enforcing stereochemistry and packing. Electrostatic interactions are not ordinarily included in the target function, partly because they cannot be calculated reliably without a description of dielectric screening by solvent in the crystal. With the recent development of accurate implicit solvent models to describe this screening, the question arises as to whether a more detailed target function including electrostatic and solvation terms can yield more accurate structures or somewhat different structures of equivalent accuracy. The Generalized Born (GB) model is one such model that describes the solvent as a dielectric continuum, taking into account its heterogeneous distribution within the crystal. It is used here for X-ray refinements of three protein structures with experimental diffraction data to 2.4, 2.9 and 3.2 A, respectively. In each case, a higher resolution structure is available for comparison. The new target function includes stereochemical restraints, van der Waals, Coulomb and solvation interactions, along with the usual X-ray pseudo-energy term, which employs the likelihood estimator of Pannu and Read. Multiple simulated-annealing refinements were performed in torsion-angle space with a conventional target function and the new GB target function, yielding ensembles of refined structures. The new target function yields structures of similar accuracy, as measured by the free R factor, map/model correlations and deviations from the high-resolution structures. About 10% of side-chain conformations differ between the two sets of refinements, in the sense that the two ensembles of conformations do not completely overlap. Over 75% of the differences correspond to surface side chains. For one of the proteins, the GB set has a greater dispersion, indicating that for this case the conventional target function overestimates the true precision. As GB parameterization continues to improve, we expect that this approach will become increasingly useful.