Motivation: Protein–protein interactions (PPIs) are a promising, but challenging target for pharmaceutical intervention. One approach for addressing these difficult targets is the rational design of small-molecule inhibitors that mimic the chemical and physical properties of small clusters of key residues at the protein–protein interface. The identification of appropriate clusters of interface residues provides starting points for inhibitor design and supports an overall assessment of the susceptibility of PPIs to small-molecule inhibition.
Results: We extract Small-Molecule Inhibitor Starting Points (SMISPs) from protein-ligand and protein–protein complexes in the Protein Data Bank (PDB). These SMISPs are used to train two distinct classifiers, a support vector machine and an easy to interpret exhaustive rule classifier. Both classifiers achieve better than 70% leave-one-complex-out cross-validation accuracy and correctly predict SMISPs of known PPI inhibitors not in the training set. A PDB-wide analysis suggests that nearly half of all PPIs may be susceptible to small-molecule inhibition.
Supplementary information: Supplementary data are available at Bioinformatics online.