Small-molecule inhibitor starting points learned from protein-protein interaction inhibitor structure

Bioinformatics. 2012 Mar 15;28(6):784-91. doi: 10.1093/bioinformatics/btr717. Epub 2011 Dec 30.

Abstract

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.

Publication types

  • Research Support, N.I.H., Extramural

MeSH terms

  • Databases, Protein
  • Humans
  • Ligands
  • Protein Interaction Maps / drug effects*
  • Proteins / antagonists & inhibitors*
  • Proteins / chemistry
  • Proteins / metabolism
  • Small Molecule Libraries
  • Software*
  • Support Vector Machine*

Substances

  • Ligands
  • Proteins
  • Small Molecule Libraries