Decoding Smell: Structural Insights and Molecular Mechanisms of Olfactory Receptors

Abstract

Olfactory receptors (ORs), the largest family of G protein-coupled receptors, are central to odor detection yet remain poorly understood in terms of their trafficking, structural logic, and regulatory dynamics. This thesis combines molecular biology and computational approaches to advance understanding of OR biology at multiple levels. Through building transcriptomic analysis for a genetic screen, novel accessory proteins were identified that enhance OR cell surface expression, including Syt1, which physically interacts with an OR and suggests a new mechanism for receptor stabilization. Using computational structural predictions and deep learning, a convolutional neural network was developed to predict OR’s chemical ligand response, demonstrating that binding cavity features underlie recognition and providing the first structure-based olfactory map. Finally, studies of iRhom2/ADAM17 signaling revealed an activity-dependent feedback mechanism that shapes OR gene expression in response to odor stimulation. Together, these findings illuminate fundamental principles of OR function and regulation, while offering tools and frameworks with broad implications for sensory neuroscience outside of OR research.