Knowledge-based scoring function derived from atomic tessellation of macromolecular structures for prediction of protein-ligand binding affinity

Structure-guided drug design efforts stand to benefit greatly from insights provided by accurate predictive models of binding affinity. Here we use Delaunay tessellation to analyze atomic four-body interactions in a diverse set of high-resolution crystal structures for three hundred protein-ligand complexes, each with an experimentally determined dissociation constant. Two hundred of these complexes are randomly selected to derive an empirical function for predicting the standard Gibbs free energy of binding (ΔG). In each case, structural atomic coordinates are used to generate tessellations for the protein-ligand complex as well as for the isolated protein without the bound ligand. An all-atom four-body knowledge-based statistical potential is used to generate a topological score based on each tessellation, and binding affinity is calculated as an empirical linear function of the difference between the topological scores of the complex and the isolated protein. Experimental and calculated ΔG values are in good agreement, with a correlation coefficient (r) of 0.79 and a standard error of 1.98 kcal/mol. To evaluate the method, tessellation-based topological score differences are calculated for the remaining one hundred complexes, and the linear transformation derived from the training data is used to predict their binding affinities. Again, the correlation coefficient is 0.79 between experimental and calculated binding energies for the test set, with a standard error of 1.93 kcal/mol.