Agonists of G-Protein-Coupled Odorant Receptors Are Predicted from Chemical Features.

Journal Article (Journal Article)

Predicting the activity of chemicals for a given odorant receptor is a longstanding challenge. Here the activity of 258 chemicals on the human G-protein-coupled odorant receptor (OR)51E1, also known as prostate-specific G-protein-coupled receptor 2 (PSGR2), was virtually screened by machine learning using 4884 chemical descriptors as input. A systematic control by functional in vitro assays revealed that a support vector machine algorithm accurately predicted the activity of a screened library. It allowed us to identify two novel agonists in vitro for OR51E1. The transferability of the protocol was assessed on OR1A1, OR2W1, and MOR256-3 odorant receptors, and, in each case, novel agonists were identified with a hit rate of 39-50%. We further show how ligands' efficacy is encoded into residues within OR51E1 cavity using a molecular modeling protocol. Our approach allows widening the chemical spaces associated with odorant receptors. This machine-learning protocol based on chemical features thus represents an efficient tool for screening ligands for G-protein-coupled odorant receptors that modulate non-olfactory functions or, upon combinatorial activation, give rise to our sense of smell.

Full Text

Duke Authors

Cited Authors

  • Bushdid, C; de March, CA; Fiorucci, S; Matsunami, H; Golebiowski, J

Published Date

  • May 3, 2018

Published In

Volume / Issue

  • 9 / 9

Start / End Page

  • 2235 - 2240

PubMed ID

  • 29648835

Pubmed Central ID

  • PMC7294703

Electronic International Standard Serial Number (EISSN)

  • 1948-7185

Digital Object Identifier (DOI)

  • 10.1021/acs.jpclett.8b00633


  • eng

Conference Location

  • United States