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Humans detect odors with the help of specialized cells located in the upper part of the nasal cavity, called olfactory receptor neurons (ORNs). ORNs possess hair-like structures called cilia, which are receptive to sensations from the inhaled air. When an odorant molecule binds to a specific receptor on the cell of the cilia, it leads to a series of events that ultimately cause the ORN to send electrical signals to the olfactory bulb in the brain through the olfactory nerves.
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Updated: May 22, 2026

High-throughput Analysis of Mammalian Olfactory Receptors: Measurement of Receptor Activation via Luciferase Activity

High-throughput Analysis of Mammalian Olfactory Receptors: Measurement of Receptor Activation via Luciferase Activity

Published on: June 2, 2014

Beyond modeling: all-atom olfactory receptor model simulations.

Peter C Lai¹, Chiquito J Crasto

¹Division of Research, Department of Genetics, University of Alabama at Birmingham Birmingham, AL, USA.

Frontiers in Genetics

|May 8, 2012

Summary

This summary is machine-generated.

Simulating olfactory receptor (OR) and odorant interactions provides molecular insights beyond static docking. This computational method models dynamic environments, advancing the structural biology of GPCRs.

Keywords:

GPCR ligand binding lipid bilayer molecular dynamics olfactory receptor protein modeling

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High-throughput Analysis of Mammalian Olfactory Receptors: Measurement of Receptor Activation via Luciferase Activity

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Area of Science:

Computational structural biology
Molecular modeling
G protein-coupled receptor (GPCR) research

Background:

Olfactory receptors (ORs) are GPCRs crucial for the sense of smell.
OR-odorant interactions initiate the olfaction process.
Static docking has limitations in understanding these molecular interactions.

Purpose of the Study:

To demonstrate the advantages of simulating dynamic OR-odorant interactions.
To present a computational protocol for modeling OR-odorant interactions.
To establish a model for GPCR computational structural biology.

Main Methods:

Development of a computationally derived model of an olfactory receptor.
Simulation of interactions between the OR model and an odorant molecule.
Analysis of the dynamic environment of OR-odorant interactions.

Main Results:

Revealed specific advantages of dynamic simulations over static docking for OR-odorant interactions.
Provided a detailed computational protocol for OR modeling and simulation.
Generated molecular-level insights into the OR-odorant binding process.

Conclusions:

Dynamic simulations offer superior insights into OR-odorant interactions.
The developed methodology serves as a model for GPCR computational structural biology.
This approach can guide future experimental investigations in olfaction and GPCRs.