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.
Finding pharmaceutically relevant target conformations from an arbitrary set of protein conformations remains a challenge in structure-based virtual screening (SBVS). The growth in the number of available conformations, either experimentally determined or computationally derived, obscures the situation further. While the inflated conformation space potentially contains viable druggable targets, the increase of conformational complexity, as a consequence, poses a selection problem. To address this challenge, we took advantage of machine learning methods, namely an over-sampling and a binary classification procedure, and present a novel method to select druggable receptor conformations. Specifically, we trained a binary classifier on a set of nuclear receptor conformations, wherein each conformation was labeled with an enrichment measure for a corresponding SBVS. The classifier enabled us to formulate suggestions and identify enriching SBVS targets for six of seven nuclear receptors. Further, the classifier can be extended to other proteins of interest simply by feeding new training data sets to the classifier. Our work, thus, provides a methodology to identify pharmaceutically interesting receptor conformations for nuclear receptors and other drug targets.