Journal Article

FZJ-2023-02263

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png 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
American Chemical Society Washington, DC

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Please use a persistent id in citations: http://hdl.handle.net/2128/34584  doi:10.1021/acs.jcim.3c00380

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. 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.

Note: Open 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).

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Contributing Institute(s):

1. Computational Biomedicine (INM-9)
2. Computational Biomedicine (IAS-5)

Research Program(s):

1. 5241 - Molecular Information Processing in Cellular Systems (POF4-524) (POF4-524)
2. DFG project 291198853 - FOR 2518: Funktionale Dynamik von Ionenkanälen und Transportern - DynIon - (291198853) (291198853)
3. DFG project 329460521 - Protonentransfer und Substraterkennung in SLC17-Transportern (329460521) (329460521)

Appears in the scientific report 2023

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Database coverage:
; ; ; Clarivate Analytics Master Journal List ; Current Contents - Physical, Chemical and Earth Sciences ; IF >= 5 ; JCR ; SCOPUS ; Web of Science Core Collection

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