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

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.