Small-molecule inhibitor starting points learned from protein-protein interaction inhibitor structure.
BACKGROUND
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.
Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA 15260, USA. dkoes@pitt.edu
David Ryan
DR
Koes
Carlos J
CJ
Camacho
D030562:Databases, Protein; D006801:Humans; D008024:Ligands; D060066:Protein Interaction Maps / Q000187:drug effects*; D011506:Proteins / Q000037:antagonists & inhibitors* / Q000737:chemistry / Q000378:metabolism; D054852:Small Molecule Libraries; D012984:Software; D060388:Support Vector Machine
D008024:Ligands; D011506:Proteins; D054852:Small Molecule Libraries