Decoding Smell from Receptor Structure

Abstract

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.