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001024690 1001_ $$0P:(DE-Juel1)169976$$aAlfonso-Prieto, Mercedes$$b0$$eCorresponding author$$ufzj
001024690 245__ $$aMachine Learning-based Modeling of Olfactory Receptors in their Inactive State: Human OR51E2 as a Case Study
001024690 260__ $$aCold Spring Harbor$$bCold Spring Harbor Laboratory, NY$$c2023
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001024690 500__ $$aPreprint publicly available; peer-reviewed version was published in Journal of Chemical Information and Modeling (doi: 10.1021/acs.jcim.3c00380) as open access paper
001024690 520__ $$aAtomistic-level investigation of olfactory receptors (ORs) is a challenging task due to the experimental/computational difficulties in the structural determination/prediction for members of this family of G-protein coupled receptors. Here we have developed a protocol that performs a series of molecular dynamics simulations from a set of structures predicted de novo by recent machine learning algorithms and apply it to a well-studied receptor, the human OR51E2. Our study demonstrates the need for simulations to refine and validate such models. Furthermore, we demonstrate the need for the sodium ion at a binding site near D2.50 and E3.39 to stabilize the inactive state of the receptor. Considering the conservation of these two acidic residues across human ORs, we surmise this requirement also applies to the other ∼400 members of this family.
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001024690 536__ $$0G:(GEPRIS)329460521$$aDFG project 329460521 - Protonentransfer und Substraterkennung in SLC17-Transportern (329460521)$$c329460521$$x2
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001024690 7001_ $$0P:(DE-Juel1)174546$$aCapelli, Riccardo$$b1$$eCorresponding author
001024690 773__ $$0PERI:(DE-600)2766415-6$$a10.1101/2023.02.22.529484$$tbioRxiv beta$$y2023
001024690 8564_ $$uhttps://doi.org/10.1101/2023.02.22.529484
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