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Theses

Numerical modeling of olfaction

Abstract : Humans have ~400 genes encoding odorant receptors (ORs) that get differentially activated by a virtually infinite space of small organic molecules. The combinatorial code resulting from this activation could allow the human nose to discriminate more than one trillion different olfactory stimuli. But how is the percept encoded in the structure of a molecule? To understand how our nose decrypts the structure of molecules, numerical models were used to study the main protagonists of olfaction: ORs and odorants. These approaches included machine-learning methods to explore and exploit existing data on ORs, and molecular modeling to understand the mechanisms behind molecular recognition. In this thesis I first review the structure-odor relationships from a chemist's point of view. Then, I explain how I developed a machine learning protocol which was validated by predicting new ligands for four ORs. In addition, molecular modeling was used to understand how molecular recognition takes place in ORs. In particular, a conserved vestibular binding site in a class of human ORs was discovered, and the role of the orthosteric binding cavity was studied. The application of these techniques allows upgrading computer aided deorphanization of ORs. My thesis also establishes the basis for testing computationally the combinatorial code of smell perception. Finally, it lays the groundwork for predicting the physiological response triggered upon odorant stimulation. Altogether, this work anchors the structure-odor relationship in the post-genomic era, and highlights the possibility to combine different computational approaches to study smell.
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Caroline Bushdid. Numerical modeling of olfaction. Other. Université Côte d'Azur, 2018. English. ⟨NNT : 2018AZUR4091⟩. ⟨tel-02010618⟩

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