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Three‐Dimensional Pharmacophore Design and Biochemical Screening Identifies Substituted 1,2,4‐Triazoles as Inhibitors of the Annexin A2–S100A10 Protein Interaction
Author(s) -
Reddy Tummala R. K.,
Li Chan,
Fischer Peter M.,
Dekker Lodewijk V.
Publication year - 2012
Publication title -
chemmedchem
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.817
H-Index - 100
eISSN - 1860-7187
pISSN - 1860-7179
DOI - 10.1002/cmdc.201200107
Subject(s) - pharmacophore , docking (animal) , virtual screening , chemistry , binding site , drug discovery , combinatorial chemistry , protein–protein interaction , annexin , stereochemistry , target protein , computational biology , biochemistry , biology , in vitro , medicine , nursing , gene
Protein interactions are increasingly appreciated as targets in small‐molecule drug discovery. The interaction between the adapter protein S100A10 and its binding partner annexin A2 is a potentially important drug target. To obtain small‐molecule starting points for inhibitors of this interaction, a three‐dimensional pharmacophore model was constructed from the X‐ray crystal structure of the complex between S100A10 and annexin A2. The pharmacophore model represents the favourable hydrophobic and hydrogen bond interactions between the two partners, as well as spatial and receptor site constraints (excluded volume spheres). Using this pharmacophore model, UNITY flex searches were carried out on a 3D library of 0.7 million commercially available compounds. This resulted in 568 hit compounds. Subsequently, GOLD docking studies were performed on these hits, and a set of 190 compounds were purchased and tested biochemically for inhibition of the protein interaction. Three compounds of similar chemical structure were identified as genuine inhibitors of the binding of annexin A2 to S100A10. The binding modes predicted by GOLD were in good agreement with their UNITY‐generated conformations. We synthesised a series of analogues revealing areas critical for binding. Thus computational predictions and biochemical screening can be used successfully to derive novel chemical classes of protein–protein interaction blockers.