Analysis of knowledge-based protein–ligand potentials using a self-consistent method

Abstract

We propose a self-consistent approach to analyze knowledge-based atom–atom potentials used to calculate protein–ligand binding energies. Ligands complexed to actual protein structures were first built using the SMoG growth procedure (DeWitte & Shakhnovich, 1996) with a chosen input potential. These model protein–ligand complexes were used to construct databases from which knowledge-based protein–ligand potentials were derived. We then tested several different modifications to such potentials and evaluated their performance on their ability to reconstruct the input potential using the statistical information available from a database composed of model complexes. Our data indicate that the most significant improvement resulted from properly accounting for the following key issues when estimating the reference state: (1) the presence of significant nonenergetic effects that influence the contact frequencies and (2) the presence of correlations in contact patterns due to chemical structure. The most successful procedure was applied to derive an atom–atom potential for real protein–ligand complexes. Despite the simplicity of the model (pairwise contact potential with a single interaction distance), the derived binding free energies showed a statistically significant correlation (∼0.65) with experimental binding scores for a diverse set of complexes.