Protein-protein interactions (PPIs) play a major role in many biological processes and they represent an important class of targets for therapeutic intervention. However, targeting PPIs is challenging because often no convenient natural substrates are available as starting point for small-molecule design. Here, we explored the characteristics of protein interfaces in five non-redundant datasets of 174 protein-protein (PP) complexes, and 161 protein-ligand (PL) complexes from the ABC database, 436 PP complexes, and 196 PL complexes from the PIBASE database and a dataset of 89 PL complexes from the Timbal database. In all cases, the small molecule ligands must bind at the respective PP interface. We observed similar amino acid frequencies in all three datasets. Remarkably, also the characteristics of PP contacts and overlapping PL contacts are highly similar.

Detecting appropriate ligand binding pockets on protein surfaces has several important applications in the drug discovery process. In pocket sets identified by two software packages, PASS and Fpocket, we found a sizable number of protein-ligand complexes where more than one pocket overlaps with the ligand. In such cases, it would be desirable if a merged set of contacting pockets would represent the small molecule. Thus, we tested three clustering approaches to merge the given pockets, a classical clustering method and two methods based on algorithms from graph theory. We found that hierarchical clustering, as well as an approach based on the concept of maximum flow, could be favorably used for clustering pockets predicted either by PASS or by Fpocket.