Protein—protein binding supersites

The lack of a deep understanding of how proteins interact remains an important roadblock in advancing efforts to identify binding partners and uncover the corresponding regulatory mechanisms of the functions they mediate. Understanding protein-protein interactions is also essential for designing specific chemical modifications to develop new reagents and therapeutics. We explored the hypothesis of whether protein interaction sites serve as generic biding sites for non-cognate protein ligands, just as it has been observed for small-molecule-binding sites in the past. Using extensive computational docking experiments on a test set of 241 protein complexes, we found that indeed there is a strong preference for non-cognate ligands to bind to the cognate binding site of a receptor. This observation appears to be robust to variations in docking programs, types of non-cognate protein probes, sizes of binding patches, relative sizes of binding patches and full-length proteins, and the exploration of obligate and non-obligate complexes. The accuracy of the docking scoring function appears to play a role in defining the correct site. The frequency of interaction of unrelated probes recognizing the binding interface was utilized in a simple prediction algorithm that showed accuracy competitive with other state of the art methods.Author summary: Protein–protein interactions are key to understand the molecular level mechanisms of regulation in the cell. However, there is still a limited understanding of what distinguishes a protein-protein binding site from the rest of the surface. This lack of knowledge is manifested in the relatively low accuracy of computational methods that try to predict protein interfaces. In this work we report a new conceptual insight about protein interfaces. Our results suggest that protein interfaces serve as generic binding sites to any ligand. This also means that in the absence of the known binding partner it is still possible to define protein interfaces by extensive docking studies of randomly selected, unrelated ligands, as they have a strong tendency to bind to the cognate binding site. This insight was leveraged in a new binding interface prediction algorithm that alone outperforms state of the art approaches that often combine a variety of features.