ABSTRACT
The emergence of SARS-CoV-2 variants diminished the efficacy of current antiviral drugs and vaccines. Hence, identifying highly conserved sequences and potentially druggable pockets for drug development was a promising strategy against SARS-CoV-2 variants. In viral infection, the receptor-binding domain (RBD) proteins are essential in binding to the host receptor. Others, Heparan sulfate (HS), widely distributed on the surface of host cells, is thought to play a central role in the viral infection cycle of SARS-CoV-2. Therefore, it might be a reasonable strategy for antiviral drug design to interfere with the RBD in the HS binding site. In this study, we used computational approaches to analyze multiple sequences of coronaviruses and reveal important information about the binding of HS to RBD in the SARS-CoV-2 spike protein. Our results showed that the potential hot-spots, including R454 and E471, in RBD, exhibited strong interactions in the HS-RBD binding region. Therefore, we screened different compounds in the natural product database towards these hot-spots to find potential antiviral candidates using LibDock, Autodock vina and furthermore applying the MD simulation in AMBER20. The results showed three potential natural compounds, including Acetoside (ACE), Hyperoside (HYP), and Isoquercitrin (ISO), had a strong affinity to the RBD. Our results demonstrate a feasible approach to identify potential antiviral agents by evaluating the binding interaction between viral glycoproteins and host receptors. The present study provided the applications of the structure-based computational approach for designing and developing of new antiviral drugs against SARS-CoV-2 variants.
