10:00 - 12:00
Sat-S11
Goethe Hall
Chair/s:
Antonella Di Pizio, Sébastien Fiorucci
Computational approaches are widely used to get insights into the chemistry and biology of chemosensation. The ECRO Special Interest Group Computational Chemosensation aims to gather researchers working in computational chemosensation, to facilitate their interaction and advance computational techniques for chemical senses, but also promote the potential of computational works to promote collaborations with experimentalists. The proposed symposium is the first initiative of the group and aims to highlight computationally guided advancements in chemosensation, ranging from machine learning based predictors, to the use of computer-aided drug design tools for ligand discovery, to the multiscale simulations of chemosensory receptors, to network analyses of proteins and signaling events. Works on both taste and smell will be presented in the symposium. We expect that bringing together computational researchers from different fields will provide stimulating and fruitful discussions about future perspectives. Moreover, during the symposium, the ECRO special interest group will be introduced to the audience.
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Odorant binding and receptor activation deciphered at the molecular level
Sat-S11-002
Presented by: Jérémie Topin
Matej Hladiš, Jody Pacalon, Maxence Lalis, Sébastien Fiorucci, Jérémie Topin
Université Côte d'Azur
Chemical sensations are triggered by the activation of transmembrane receptors expressed on the surface of sensory neurons which belong to the family of G-protein coupled receptors or ion channels. Their activation mechanism can be divided into two main events: the binding of the ligand to the receptor and, in the case of an agonist, the activation of the complex.
The combination of numerical approaches with experimental methods allows to decipher at the atomic scale the mechanisms of ligand binding and receptor activation. Our work reveals that agonist-induced activation of odorant receptors can be predicted. Numerical simulations identify functional molecular switches that encode agonist detection and downstream signaling mechanisms within chemical receptors.[1]
From a general perspective, establishing a relationship between the structure of a molecule and the structure of the olfactory receptors (ORs) it activates has long been a challenge. We take advantage of recent advances in representation learning and combine them with graph neural networks (GNN) to build a receptor-ligand prediction model. To the best of our knowledge, this is the first receptor ligand prediction model that takes into account an entire protein sequence. This prediction model has been evaluated on a set of more than 46,000 receptor-molecule pairs and achieves a Matthews correlation coefficient of 0.6. Thus, our protocol paves the way to decipher the combinatorial code associated with odor perception.

[1] aL. Charlier, J. Topin, C. Ronin, S.-K. Kim, W. A. Goddard, R. Efremov, J. Golebiowski, Cellular and molecular life sciences 2012, 69, 4205-4213; bC. A. de March, J. Topin, E. Bruguera, G. Novikov, K. Ikegami, H. Matsunami, J. Golebiowski, Angewandte Chemie 2018, 130, 4644-4648; cJ. Topin, C. Bouysset, J. Pacalon, Y. Kim, M.-R. Rhyu, S. Fiorucci, J. Golebiowski, Cellular and Molecular Life Sciences 2021, 78, 7605-7615.
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