Poster Presentation The 43rd Lorne Conference on Protein Structure and Function 2018

Homology modelling of ectopic olfactory receptors (#140)

amara jabeen 1 , Shoba Ranganathan 1
  1. Molecular Sciences, Macquarie University, North Ryde, NSW, Australia

Olfactory receptors (ORs) are the largest sub-category of G-protein-coupled receptors (GPCRs) and constitute 2% of the human genome, yet only a few of these are known as proteins [1]. In addition to their role in olfaction, they are involved in various physiological and pathophysiological processes. Some ectopically expressed ORs have been found to be associated with various diseases including prostate, liver and colorectal cancers. The ligand binding niche of human ORs are to date largely unknown and uncovering cognate ligands will allow a better understanding of their potential to be used in therapeutics and in the fragrance industry. Heterologous expression of ORs is very challenging and has significantly restricted functional studies. To date, only a few ORs have been expressed heterologously and have site-directed mutagenesis data available. This information can serve as the starting point towards discovery of ligand binding niches of all other ORs, resulting in reliance on in silico studies.

In order to build homology models for these ectopic ORs, we present the workflow for one specific OR, using the general approach developed for GPCRs [2]. This model will be used for virtual ligand screening and molecular docking to uncover putative ligands and ligand binding sites. The outcome of this study will be helpful in understanding the mechanism of chemosensory responses and designing new therapeutics for OR associated diseases.

  1. Baker MS, Ahn SB, Mohamedali A, Islam MT, Cantor D, Verhaert PD, Fanayan S, Sharma S, Nice EC, Connor M, Ranganathan S. Accelerating the search for the missing proteins in the human proteome. Nat Commun. 2017, 8:14271.
  2. Jabeen A, Mohamedali A, Ranganathan S. Protocol for protein structure modelling. In Encylopedia of Bioinformatics and Computational Biology, Elsevier, 2017, accepted.