Odorant receptors (ORs) are the largest subfamily within class A G protein-coupled receptors (GPCRs). No experimental structural data of any OR is available to date and atomic-level insights are likely to be obtained by means of molecular modeling. In this article, we critically align sequences of ORs with those GPCRs for which a structure is available. Here, an alignment consistent with available site-directed mutagenesis data on various ORs is proposed. Using this alignment, the choice of the template is deemed rather minor for identifying residues that constitute the wall of the binding cavity or those involved in G protein recognition.
Odorant Receptor (OR) genes and proteins represent more than 2% of our genome and 4% of our proteome 25 and constitute the largest sub-group of G Protein-Coupled Receptors (GPCRs). The mechanism underlying OR activation remains poorly understood, as they do not share some of the highly conserved motifs critical for activation of non-olfactory GPCRs. By combining site-directed mutagenesis, heterologous expression, and molecular dynamics simulations that capture the conformational change of constitutively active mutants, we tentatively identified crucial residues for the function of these receptors using the mouse MOR256-3 (Olfr124) as a model. The toggle-switch for sensing agonists in-volves a highly conserved tyrosine residue in helix VI. The ionic-lock is located between the `DRY' motif in helix III and a positively charged `R/K' residue in helix VI. This study provides an unprecedented model that captures the main mechanisms of odorant receptor activation.
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.
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