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Machine Learning-based Modeling of Olfactory Receptors: Human OR51E2 as a Case Study

View ORCID ProfileMercedes Alfonso-Prieto, View ORCID ProfileRiccardo Capelli
doi: https://doi.org/10.1101/2023.02.22.529484
Mercedes Alfonso-Prieto
†Computational Biomedicine, Institute for Advanced Simulation IAS-5/Institute for Neuroscience and Medicine INM-9, Forschungszentrum Jülich GmbH, Wilhelm-Johnen-Straße, D-52428 Jülich, Germany
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  • ORCID record for Mercedes Alfonso-Prieto
Riccardo Capelli
‡Dipartimento di Bioscienze, Università degli Studi di Milano, Via Celoria 26, I-20133 Milan, Italy
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  • For correspondence: riccardo.capelli@unimi.it
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Abstract

Atomistic-level investigation of olfactory receptors (ORs) is a challenging task due to the experimental/computational difficulties in the structural determination/prediction for members of this family of G-protein coupled receptors. Here we have developed a protocol that performs a series of molecular dynamics simulations from a set of structures predicted de novo by recent machine learning algorithms and apply it to a well-studied receptor, the human OR51E2. Our study demonstrates the need for extensive simulations to refine and validate such models. Furthermore, we demonstrate the need for the sodium ion at a binding site near D2.50 and E3.39 to stabilize the inactive state of the receptor. Considering the conservation of these two acidic residues across human ORs, we surmise this requirement also applies to the other ~400 members of this family.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • ↵* E-mail: m.alfonso-prieto{at}fz-juelich.de

  • In the new version of the manuscript, we performed two new replica simulation per starting model, to strengthen the statistics about our findings. Figures 2 and 3 update accordingly, some rewriting in the results and in the final discussion.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted March 09, 2023.
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Machine Learning-based Modeling of Olfactory Receptors: Human OR51E2 as a Case Study
Mercedes Alfonso-Prieto, Riccardo Capelli
bioRxiv 2023.02.22.529484; doi: https://doi.org/10.1101/2023.02.22.529484
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Machine Learning-based Modeling of Olfactory Receptors: Human OR51E2 as a Case Study
Mercedes Alfonso-Prieto, Riccardo Capelli
bioRxiv 2023.02.22.529484; doi: https://doi.org/10.1101/2023.02.22.529484

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