Computational Design of a Novel Inhibitor Against COVID

Abstract

Background: In recent years, in silico computational approaches have tremendously guided computational medicinal chemists and research scientists to analyze protein structures, kinetics, functions, and molecular interactions of the administered drugs. Objective: This study aimed to identify a novel inhibitor against SARS-CoV-2 using human CD26 and modeled spike protein through suitable in silico approaches. Methods: In this work, molecular docking and molecular dynamics simulation experiments were conducted to gain insights into the binding affinity and stability, respectively. The docked complex of CD26 with modeled spike protein showed higher binding affinity than the complex of CD26 with resolved spike protein due to the existence of strong interactions with the crucial amino acid residues of the target proteins. Results: The results of the molecular dynamics simulation demonstrated that CD26 with the modeled spike protein docked complex showed good stability when compared with the resolved protein. Conclusion: From this computational finding, it was also suggested that the structure was stable and would rapidly guide the discovery of potential inhibitors against COVID-19.