Olfactory receptors (ORs) are the largest subfamily of G-protein-coupled receptors (GPCRs). ORs evolved to interact with the outside world by detecting volatile molecules called odorants.
Recently, ORs have been found in various tissues, involved in many biological processes including tumors progression. Consequently, ORs gained interest as new targets for drug discovery. Despite their potential, the possibilities for a comprehensive understanding of ORs functions is limited by the lack of experimental 3D conformations.
Our goal is to develop and test a pipeline that combines a machine learning (ML)-based structure determination protocol and enhanced sampling techniques to build realistic models. This will allow for a mechanistic understanding of the relevant processes occurring in a receptor at atomistic detail.
Our test case will be the human olfactory receptor 51E2 (hOR51E2), which has been experimentally studied for its role in prostate cancer. We will build a reliable model of the system from the structure obtained by ML algorithms (e.g., AlphaFold) and docking of known ligands. We will verify the structure with extensive molecular dynamics simulations and characterize both the binding and activation/inactivation of the system.
From the proposed protocol, significant impact is expected for structural biology, medicinal chemistry, and neurobiology.