A conformational ensemble model of food odorant–receptor interaction: from structural motifs to AI-based predictive frameworks

Food odorant perception is increasingly understood in the context of dynamic olfactory receptors (ORs), which are G protein-coupled receptors (GPCRs), sampling conformational ensembles rather than fixed binding pockets. This review translates and integrates this established dynamic-receptor paradigm into food olfaction by linking odorant structural motifs, receptor conformational reweighting, matrix-dependent delivery, and mixture-level outcomes such as masking and synergy. Recent cryo-EM structures and mutagenesis studies, including OR52-family receptors and the reversibly covalent aldehyde receptor OR6A2, illustrate how ligand binding can redistribute receptor states without, by itself, distinguishing conformational selection from induced fit. Current AI models are discussed as useful structure-to-receptor or structure-to-perception predictors that still lack explicit receptor dynamics and realistic food-matrix exposure. A multiscale framework is proposed to connect these layers quantitatively and identify the data required for future predictive models.