Mapping the combinatorial coding between olfactory receptors and perception with deep learning

Abstract

The sense of smell remains poorly understood, especially in contrast to visual and auditory coding. At the core of our sense of smell is the olfactory information flow, in which odorant molecules activate a subset of our olfactory receptors and combinations of unique receptor activations code for unique odors. Understanding this relationship is crucial for unraveling the mysteries of human olfaction and its potential therapeutic applications. Despite this, predicting molecule-OR interactions remains incredibly difficult. Here, we develop a novel, biologically-inspired approach denoted MolOR that first maps odorant molecules to their respective olfactory receptor (OR) activation profiles and subsequently predicts their odor percepts. Despite a lack of overlap between molecules with OR activation data and percept annotations, our joint model improves percept prediction by leveraging the OR activation profile of each odorant as auxiliary features in predicting its percepts. We extend this cross receptor-percept approach, showing that sets of molecules with very different structures but similar percepts, a common challenge for chemosensory prediction, have similar predicted OR activation profiles. Lastly, we further probe the odorant-OR models predictive ability, showing it can distinguish binding patterns across unique OR families, as well as between protein-coding genes or frequently occuring pseudogenes in the human olfactory subgenome. This work may aid in the potential discovery of novel odorant ligands targeting functions of orphan ORs, and in further characterizing the relationship between chemical structures and percepts. In doing so, we hope to advance our understanding of olfactory perception and the design of new odorants with desired perceptual qualities.