Evaluation of protein models by atomic solvation preference

Abstract

Important properties of globular proteins, such as the stability of the folded state, depend sensitively on interactions with solvent molecules. An excluded volume approximation to protein-solvent interaction, the solvent contact model, was used to derive atomic solvation preference parameters from a database of known protein structures. The ability of solvation preference to discriminate between correct and incorrect three-dimensional structures for a given sequence, or to identify the correct sequence placement in a given structure, was tested. Backbone co-ordinates were taken from experimentally known structures or hypothetical models and side-chain conformations (in rotamer space) were optimized by an efficient Monte Carlo algorithm using simulated annealing and simple potential functions. Discrimination by solvation preference was very clear between deliberately misfolded and correct globular models as well as between native-like and non-native-like topologies of combinatorially generated myoglobin models. Due to its statistical nature, the evaluation works best on entire protein models, while the identification of incorrect parts of models is more difficult. In one case locally incorrect chain tracing in a crystal structure was identified. The method is computationally fast compared to methods based on surface area calculations and is recommended for use as a diagnostic tool in model building based on sequence similarity, in folding simulations and in protein design.