Identification of New Allosteric Modulators for the mGlu2 Receptor by using a Ligand-based Drug Discovery Approach

Purpose: The human mGlu receptors are G protein-coupled receptors located within the central nervous system. These receptors normally bind to glutamate, which is the primary excitatory neurotransmitter in the body. The receptors can then assist in modulating the transmission of excitatory signals within the brain. These characteristics help to make the mGlu2 receptor a potential, novel target for future drug development, particularly for the treatment of certain neurologic or neuropsychiatric disorders, such as schizophrenia or depression. However, most allosteric ligands bind non-selectively on both mGlu2 and mGlu3 receptors. A pharmacological tool that assists with distinguishing ligands specific to mGlu2 and mGlu3 receptor subtypes will be pivotal to speed-up the drug discovery process. Our purpose in this study is to find novel ligands of potential allosteric modulators for the mGlu2 receptor by using already identified modulators through a ligand-based drug designing approach.

Methods: The potential allosteric ligands for the mGlu2 receptor were obtained by performing similarity searches on the online databases, ZINC and Drugbank. The original compounds used as the basis for the similarity searches came from a previously compiled list of Top 39 ZINC mGlu2 ligands (from the Liu Lab). Once the ligands were downloaded, they were converted into the appropriate file formats for molecular docking. Due to time constraints, it was decided that we would only dock the compounds whose original ligands had <10 results obtained from similar searching through ZINC. The selected ligands were then docked using Autodock Vina and visualized using Pymol. The Top 3 ligands were then determined based on their presence within the mGlu2 allosteric binding pocket and their predicted binding affinity for the receptor. Additionally, these ligands were also analyzed using a previously developed machine learning model. Specifically, the machine learning model would predict mGlu2 ligand likeness and binding affinity for each of the obtained ligands.

Results: A total of 1507 allosteric ligands were obtained for the mGlu2 receptor through the similarity searches. Machine learning model analysis of the similar ligands deemed that 88.89% of them were more likely to be mGlu2 ligands. Additionally, 83.50% of the ligands were deemed to have a high predicted binding affinity for the mGlu2 receptor. A total of 46 compounds were docked to the mGlu2 receptor using Autodock Vina, and their predicted binding affinities were obtained. The Top 3 similar ligands for the mGlu2 receptor, listed in order, exhibited binding affinities of -12.5 kcal/mol, -12.3 kcal/mol and -11.0 kcal/mol.

Conclusion: We were able to identify 1507 potential ligands for the mGlu2 receptor through similarity searches. Through further molecular docking of 46 of the similar ligands, we have determined three specific allosteric ligands for the mGlu2 receptor that are comparable or slightly better to their original counterparts. However, we believe additional research and investigation is required for validation of their potential efficacy. Future studies should involve analysis of the specific protein-ligand interactions that exist between the mGlu2 receptor and the three similar allosteric ligands, followed by comparison with the interactions present in their original counterparts.