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001008229 0247_ $$2doi$$a10.1021/acs.jcim.3c00380
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001008229 0247_ $$2ISSN$$a1520-5142
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001008229 0247_ $$2ISSN$$a44.2004)
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001008229 0247_ $$2Handle$$a2128/34584
001008229 0247_ $$2pmid$$a37145455
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001008229 1001_ $$0P:(DE-Juel1)169976$$aAlfonso-Prieto, Mercedes$$b0$$eCorresponding author$$ufzj
001008229 245__ $$aMachine Learning-Based Modeling of Olfactory Receptors in Their Inactive State: Human OR51E2 as a Case Study
001008229 260__ $$aWashington, DC$$bAmerican Chemical Society$$c2023
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001008229 500__ $$aOpen access publication; open access fee paid by Italy. FZJ author supported in part by the DFG Research Unit FOR2518 “Functional Dynamics of Ion Channels and Transporters – DynIon” (291198853), Project P6 (329460521).
001008229 520__ $$aAtomistic-level investigation of olfactory receptors (ORs) is a challenging task due to the experimental/computational difficulties in the structural determination/prediction for members of this family of G-protein coupled receptors. Here, we have developed a protocol that performs a series of molecular dynamics simulations from a set of structures predicted de novo by recent machine learning algorithms and apply it to a well-studied receptor, the human OR51E2. Our study demonstrates the need for simulations to refine and validate such models. Furthermore, we demonstrate the need for the sodium ion at a binding site near D2.50 and E3.39 to stabilize the inactive state of the receptor. Considering the conservation of these two acidic residues across human ORs, we surmise this requirement also applies to the other 400 members of this family. Given the almost concurrent publication of a CryoEM structure of the same receptor in the active state, we propose this protocol as an in silico complement to the growing field of ORs structure determination.
001008229 536__ $$0G:(DE-HGF)POF4-5241$$a5241 - Molecular Information Processing in Cellular Systems (POF4-524)$$cPOF4-524$$fPOF IV$$x0
001008229 536__ $$0G:(GEPRIS)291198853$$aDFG project 291198853 - FOR 2518: Funktionale Dynamik von Ionenkanälen und Transportern - DynIon - (291198853)$$c291198853$$x1
001008229 536__ $$0G:(GEPRIS)329460521$$aDFG project 329460521 - Protonentransfer und Substraterkennung in SLC17-Transportern (329460521)$$c329460521$$x2
001008229 588__ $$aDataset connected to CrossRef, Journals: juser.fz-juelich.de
001008229 7001_ $$0P:(DE-HGF)0$$aCapelli, Riccardo$$b1$$eCorresponding author
001008229 773__ $$0PERI:(DE-600)1491237-5$$a10.1021/acs.jcim.3c00380$$gVol. 63, no. 10, p. 2911 - 2917$$n10$$p2911 - 2917$$tJournal of chemical information and modeling$$v63$$x0095-2338$$y2023
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001008229 9141_ $$y2023
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