PT  - JOURNAL ARTICLE
AU  - Mercedes Alfonso-Prieto
AU  - Riccardo Capelli
TI  - Machine Learning-based Modeling of Olfactory Receptors: Human OR51E2 as a Case Study
AID  - 10.1101/2023.02.22.529484
DP  - 2023 Jan 01
TA  - bioRxiv
PG  - 2023.02.22.529484
4099  - http://biorxiv.org/content/early/2023/02/22/2023.02.22.529484.short
4100  - http://biorxiv.org/content/early/2023/02/22/2023.02.22.529484.full
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.