RT Journal Article
SR Electronic
T1 Machine Learning-based Modeling of Olfactory Receptors: Human OR51E2 as a Case Study
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2023.02.22.529484
DO 10.1101/2023.02.22.529484
A1 Mercedes Alfonso-Prieto
A1 Riccardo Capelli
YR 2023
UL http://biorxiv.org/content/early/2023/02/22/2023.02.22.529484.abstract
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 extensive 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.Competing Interest StatementThe authors have declared no competing interest.