Machine Learning-Based Modeling of Olfactory Receptors in Their Inactive State: Human OR51E2 as a Case Study.

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 D^2.50 and E^3.39 to stabilize the inactive state of the receptor. Considering the conservation of these two acidic residues across human ORs, w

SUBMITTER: Alfonso-Prieto M 

PROVIDER: S-EPMC10207261 | biostudies-literature | 2023 May

REPOSITORIES: biostudies-literature