Large-Scale G Protein-Coupled Olfactory Receptor–Ligand Pairing

G protein-coupled
receptors (GPCRs) conserve common structural
folds and activation mechanisms, yet their ligand spectra and functions
are highly diverse. This work investigated how the amino-acid sequences
of olfactory receptors (ORs)the largest GPCR familyencode
diversified responses to various ligands. We established a proteochemometric
(PCM) model based on OR sequence similarities and ligand physicochemical
features to predict OR responses to odorants using supervised machine
learning. The PCM model was constructed with the aid of site-directed
mutagenesis, in vitro functional assays, and molecular
simulations. We found that the ligand selectivity of the ORs is mostly
encoded in the residues up to 8 Å around the orthosteric pocket.
Subsequent predictions using Random Forest (RF) showed a hit rate
of up to 58%, as assessed by in vitro functional
assays of 111 ORs and 7 odorants of distinct scaffolds. Sixty-four
new OR–odorant pairs were discovered, and 25 ORs were deorphanized
here. The best model demonstrated a 56% deorphanization rate. The
PCM-RF approach will accelerate OR–odorant mapping and OR deorphanization.