In Silico Evaluation of ERQ Bioactive Tripeptide as an Anticancer Agent and an Inhibitor of SARS-CoV-2 Enzymes

Year 2024, , 34 - 41, 30.05.2024

Gözde Yılmaz Sefa Çelik Ayşen Erbölükbaş Özel Sevim Akyüz

https://doi.org/10.26650/EurJBiol.2024.1389569

Abstract

Objective: Short peptides play a significant role in exploring drugs with higher selectivity and fewer side effects in cancer and COVID-19 therapies. This study evaluated the anticancer and anti-COVID-19 activities of Glu-Arg-Gln (ERQ) tripeptide for the first time. To discover the potentiality of the tripeptide as an anticancer and as a SARS-CoV-2 inhibitor, molecular docking analysis of ERQ tripeptide with DNA (PDB ID: 1BNA) and a variety of SARS-CoV-2 enzymes, namely. Main protease (PDB IDs: 6M03, 6LU7) and Spike glycoprotein (PDB ID: 6VXX) were performed.
Materials and Methods: To determine the binding efficiency of ERQ to target DNA and proteins, molecular docking processes were carried out using the Autodock Vina program. The sorts of bonds and interacting residues in ERQ/DNA and ERQ/protein complexes were determined.
Results: Molecular docking simulations of ERQ tripeptide against 1BNA, 6M03, 6LU7, and 6VXX were performed, and the interactions between the docked ligand and target residues were determined. The binding mechanisms of ERQ with the receptors were clarified. The binding affinities of ERQ towards the targets were predicted to be between -6.3 and -6.7 kcal/mol. ERQ showed the highest binding affinity to Spike glycoprotein (6VXX), with an estimated binding energy of -6.7 kcal/mol.
Conclusion: Molecular docking simulations revealed the potential of ERQ tripeptide as an anticancer and anti-COVID-19 agent. High binding affinity against 1BNA (-6.4 kcal/mol), 6M03 (-6.3 kcal/mol), 6LU7 (-6.6 kcal/mol), and 6VXX (-6.7 kcal/mol) indicated that ERQ could be an excellent new natural therapy for the treatment of cancer and COVID-19.

Keywords

In silico, Molecular docking, Glu-Arg-Gln, ERQ, Tripeptide

Supporting Institution

Research funds of Istanbul University

Project Number

FDK-2023-39504

Thanks

This study was supported by the Research funds of Istanbul University (FDK-2023-39504).

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