Protein-protein docking by shape-complementarity and property matching

We present a computational approach to protein-protein docking based on surface shape complementarity ("ProBinder"). Within this docking approach, we implemented a new surface decomposition method that considers local shape features on the protein surface. This new surface shape decomposition results in a deterministic representation of curvature features on the protein surface, such as "knobs," "holes," and "flats" together with their point normals. For the actual docking procedure, we used geometric hashing, which allows for the rapid, translation-, and rotation-free comparison of point coordinates. Candidate solutions were scored based on knowledge-based potentials and steric criteria. The potentials included electrostatic complementarity, desolvation energy, amino acid contact preferences, and a van-der-Waals potential. We applied ProBinder to a diverse test set of 68 bound and 30 unbound test cases compiled from the Dockground database. Sixty-four percent of the protein-protein test complexes were ranked with an root mean square deviation (RMSD) < 5 A to the target solution among the top 10 predictions for the bound data set. In 82% of the unbound samples, docking poses were ranked within the top ten solutions with an RMSD < 10 A to the target solution.