Computational Studies on T2Rs Agonist-Based Anti-COVID-19 Drug Design

Area of Science:

• * Virology and Drug Discovery
• * Computational Chemistry and Molecular Modeling

Background:

• * The global pandemic caused by SARS-CoV-2 necessitates urgent discovery of effective COVID-19 treatments.
• * COVID-19 symptoms include respiratory issues, and emerging evidence links SARS-CoV-2 infection to olfactory and gustatory dysfunction, potentially via neural pathways.
• * Taste receptors (T2Rs) and G-protein coupled receptors (GPCRs) are explored as potential therapeutic targets.

Purpose of the Study:

• * To identify a core molecule for designing novel anti-COVID-19 drugs or derivatives.
• * To investigate taste receptor agonist molecules for potential therapeutic applications against SARS-CoV-2.
• * To evaluate the interaction of candidate molecules with the SARS-CoV-2 main protease (6LU7).

Main Methods:

• * Computational docking studies were employed to assess molecular interactions.
• * Analysis focused on the binding affinity and interaction patterns of molecules with the SARS-CoV-2 6LU7 protease.
• * The study utilized a taste receptor agonist molecule as a basis for drug design exploration.

Main Results:

• * The antibiotic tobramycin demonstrated the most significant interaction with the SARS-CoV-2 main protease (6LU7) in computational docking.
• * Tobramycin's aromatic carbonyl groups formed strong intermolecular hydrogen bonding with the GLN189 amino acid.
• * The molecule achieved a glide score of -11.159, indicating a highly favorable binding interaction.

Conclusions:

• * Tobramycin shows promise as a lead compound for developing new COVID-19 therapeutics.
• * The findings support the potential of targeting molecular recognition mechanisms, similar to GPCRs, for drug design.
• * Further development of tobramycin-based derivatives is warranted for effective COVID-19 treatment.