Claire A. de March

The paper trains a deep learning model to predict odorant receptor-ligand interactions using structural and functional data, revealing structural determinants of ligand selectivity.

The paper uses engineered odorant receptors to uncover structural principles of odor discrimination by solving cryoEM structures of consensus ORs and comparing their ligand recognition properties.

The paper identifies odorant antagonists that reduce perception of malodors by interacting with olfactory receptors.

The paper identifies odorant ligands for over 500 mouse olfactory receptors and shows that molecular properties of odorants predict receptor responses and selectivity.

The paper spatially maps olfactory receptors to glomeruli in the mouse olfactory bulb using targeted transcriptomics.

The paper uses machine learning to predict olfactory receptor-odorant pairs and basal activity from sequence data, identifying key residues that determine selectivity and activity.