TY  - EJOUR
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  - bioRxiv beta
CY  - Cold Spring Harbor
PB  - Cold Spring Harbor Laboratory, NY
M1  - FZJ-2024-02363
PY  - 2023
N1  - 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
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.
LB  - PUB:(DE-HGF)25
DO  - DOI:10.1101/2023.02.22.529484
UR  - https://juser.fz-juelich.de/record/1024690
ER  -