In summary, within the framework of the 3D-ORco project, the development of research tools and protocols for the expression, solubilization with detergents and nanodiscs,, as well as that of purification of recombinant AgamORco using chromatographic techniques, was achieved, and can be applied as-is in large scale. Reliable models of the AgamORco and AgamORco•ligand complexes were also created, and the Receptor-ligand interactions that are perceived as critical for odorant recognition by the receptor, were proposed. Conclusions were drawn about the relationship between ligand structure and inhibition mechanism (orthosteric or allosteric in relation to the agonist binding site). Furthermore, functional studies revealed new potent natural antagonists of ORco, which were confirmed as repellents by in vivo bioassays. Moreover, these studies guided the creation of a virtual platform for the selection of orthosteric antagonists. To the best of our knowledge, our study is the first one that combines a pharmacophore with a SVM model for identification of AgamORco antagonists and specifically orthosteric ones that are advantageous for future site-specific ORco structure-based screening as compared to blind-docking trials. To this end, our pipeline can constitute the first ligand-based step for screening large chemical libraries and proposing candidates for subsequent site-specific molecular docking and MD simulations against AgamORco homology models. Such a structure-based approach, utilizing the 3D model of AgamORco produced in the framework  of 3D-ORco,  is currently underway for seeking both novel active compounds and gaining structural insights on ligand recognition mechanism by the receptor.

In conclusion, the goals of the 3D-ORco, namely the understanding of the rules governing the recognition of odor molecules by the 7-transmembrane olfactory co-receptor ORco and, furthermore, the discovery of multiple new effective disruptors of host-seeking behavior, were fully achieved.

Kostas Iatrou, Trias Thireou, Georgia Kythreoti, Panagiota Liggri, Antonios Michaelakis, Dimitrios Papachristos, Katerina Tsitsanou, Spyros Zographos, and Stefan Schulz (2024). Natural anosmia-inducing compounds for control of mosquito biting behaviors: Machine learning-assisted determination of structural determinants for ORco ligands antagonizing odorant receptor function. Annual meeting of the American Society for Biochemistry and Molecular Biology (Discover BMB 2024), March 23–26, San Antonio, USA. Topic Category: Drug Discovery. Abstract ID: 1161. J Biol. Chem. (2024) 300, Issue 3, (Suppl. 9), S168. DOI: 10.1016/j.jbc.2024.106064  | Read Abstract 

Trias Thireou, Georgia Kythreoti, Panagiota G.V. Liggri, Antonios Michaelakis, Dimitrios P. Papachristos, Katerina E. Tsitsanou, Spyros E. Zographos, Kostas Iatrou (2023). Combining a pharmacophore and an artificial intelligence approach to predict ORco ligands antagonizing mosquito odorant receptor function. 30th Meeting of the Hellenic Society for Neuroscience (HSfN), 24-26 November, National Center for Scientific Research “Demikritos”, Athens, Greece. (Poster PP054)  | Read Abstract