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Takase, D.

Publications and source records attributed to Takase, D..

2 recordsLinked to original sources

Allosteric Gating Mechanism Regulates Odorant Selectivity and Antagonism in Odorant Receptors

Odorant receptors (ORs) belong to class A G protein coupled receptors that detect diverse small molecules, yet the steps that link odorant association to receptor mediated selectivity remains incompletely defined. Here we combined 1.26 milliseconds of all-atom odorant association Molecular Dynamics simulations with Markov state modeling and cell-based cAMP measurements to examine two human ORs receptors that recognize chemically distinct odorants. In the class I receptor OR51E2, propionate associates via two extracellular pathways gated for selectivity by residues in the extracellular loop 2 and 3. The longer alkyl chain heptanoate occupies this gate and reduces propionate association and signaling which is consistent with the observed antagonist behavior of heptanoate. Pocket-expanding mutations at F155 and L158 allosterically regulate ECL2-ECL3 gate by permitting longer-chain fatty acids to adopt fully inserted poses that support gate closure, while also attenuating propionate responses. In the class II receptor OR1A1, hydrophobic odorants partition into the membrane and reach the orthosteric site mostly through multiple transmembrane paths. A mutational scan of gating residues and analysis of intermediate-state occupancies suggest that an orthosteric substitution at G108 can allosterically bias odorant association path choice. Together, these results support a model in which odorant association paths and gate residence, together with allosteric coupling between the orthosteric site and entry gates, contribute to odorant specificity and antagonism in odorant receptors.

biophysics↗

Decoding Smell from Receptor Structure

Olfaction enables animals to detect and discriminate a vast array of chemicals, yet how odorant receptors (ORs) encode ligand selectivity remains unclear. Although recent advances in protein structure prediction have expanded access to OR structures, linking these to function at scale has been challenging. Here, we combined AlphaFold3-predicted receptor structures with protein language model embeddings and in vivo pS6-IP-Seq measurements of olfactory sensory neuron activation across a chemically diverse odor panel to train a deep learning model of OR-ligand interactions. The resulting framework predicts receptor responses, organizes receptors by functional similarity independent of sequence, and identifies structural determinants of ligand selectivity. These findings establish a structure-based map of OR function and provide a foundation for predictive and interpretable models of olfactory coding.

molecular biology↗