Machine Learning-based Modeling of Olfactory Receptors in their Inactive State: Human OR51E2 as a Case Study

Alfonso-Prieto, Mercedes; Capelli, Riccardo

doi:10.1101/2023.02.22.529484

Preprint

FZJ-2024-02363

Machine Learning-based Modeling of Olfactory Receptors in their Inactive State: Human OR51E2 as a Case Study

Alfonso-Prieto, M. (Corresponding author)FZJ* ; Capelli, R. (Corresponding author)

2023
Cold Spring Harbor Laboratory, NY Cold Spring Harbor

bioRxiv beta (2023) [10.1101/2023.02.22.529484]

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Please use a persistent id in citations: doi:10.1101/2023.02.22.529484 doi:10.34734/FZJ-2024-02363

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 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.

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ddc:570

Note: Preprint publicly available; peer-reviewed version was published in Journal of Chemical Information and Modeling (doi: 10.1021/acs.jcim.3c00380) as open access paper

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Appears in the scientific report 2024

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Creative Commons Attribution-NonCommercial-NoDerivs CC BY-NC-ND 4.0