Identification of potential natural inhibitors of the receptor-binding domain of the SARS-CoV-2 spike protein using a computational docking approach.

Abstract

Background: The novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the only zoonotic-origin CoV to reach the pandemic stage, to which neither an effective vaccine nor a specific therapy is available. The spike glycoprotein harbors the receptor-binding domain (RBD) that mediates the virus's entry to host cells. This study aimed to identify novel inhibitors that target the spike protein's RBD domain through computational screening of chemical and natural compounds. Method: The spike protein was modeled from the recently reported electron microscopy protein structure (PDB ID: 6VSB) and the previously described SARS-CoV protein structure (PDB ID: 6ACD and 6ACJ). Virtual lab bench CLC Drug Discovery was used to computationally screen for potential inhibitory effects of currently prescribed drugs (n = 22), natural antiviral drugs (n = 100), and natural compounds (n = 35032). Quantitative Structure-Activity Relationship (QSAR) studies were also performed to determine the leading binders known for their antiviral activity. Results: Among the drugs currently used to treat SARS-CoV2, hydroxychloroquine and favipiravir were identified as the best binders with an average of four H-bonds, with a binding affinity of − 36.66 kcal/mol and a minimum interaction energy of − 6.63 kcal/mol. In an evaluation of antiviral compounds, fosamprenavir and abacavir showed effective binding of five H-bonds, with an average binding affinity of − 18.75 kcal.mol− 1 and minimum interaction energy of − 3.57 kcal/mol. Furthermore, screening of 100 natural antiviral compounds predicted potential binding modes of glycyrrhizin, nepritin, punicalagin, epigallocatechin gallate, and theaflavin (average binding affinity of − 49.88 kcal/mol and minimum interaction energy of − 4.35 kcal/mol). Additionally, the study reports a list of 25 natural compounds that showed effective binding with an improved average binding affinity of − 51.46 kcal/mol. Conclusions: Using computational screening, we identified potential SARS-CoV-2 S glycoprotein inhibitors that bind to the RBD region. Using structure-based design and combination-based drug therapy, the identified molecules could be used to generate anti-SARS-CoV-2 drug candidates.