TY  - JOUR
AU  - Alfonso-Prieto, Mercedes
AU  - Capelli, Riccardo
TI  - Machine Learning-Based Modeling of Olfactory Receptors in Their Inactive State: Human OR51E2 as a Case Study
JO  - Journal of chemical information and modeling
VL  - 63
IS  - 10
SN  - 0095-2338
CY  - Washington, DC
PB  - American Chemical Society
M1  - FZJ-2023-02263
SP  - 2911 - 2917
PY  - 2023
N1  - 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).
AB  - 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.
LB  - PUB:(DE-HGF)16
C6  - 37145455
UR  - <Go to ISI:>//WOS:000985641000001
DO  - DOI:10.1021/acs.jcim.3c00380
UR  - https://juser.fz-juelich.de/record/1008229
ER  -