Computational Guided Identification of Novel Potent Inhibitors of NTD-N-Protein of SARS-CoV-2

The Coronavirus Disease 2019 (COVID-19), caused by the SARS-CoV-2 virus has raised severe health problems in china and across the world as well. CoVs encode the nucleocapsid protein (N-protein), an essential RNA-binding protein that performs different roles throughout the virus replication cycle and forms the ribonucleoprotein complex with viral RNA using the N-terminal domain (NTD) of N-protein. Recent studies have shown that NTD-N-protein is a legitimate target for the development of antiviral drugs against human CoVs. Owing to the importance of NTD, the present study focuses on targeting the NTD-N-protein from SARS-CoV-2 to identify the potential compounds. The pharmacophore model has been developed based on the guanosine monophosphate (GMP), a RNA substrate and further pharmacophore-based virtual screening was performed against ZINC database. The screened compounds were filtered by analysing the in silico ADMET properties and drug-like properties. The pharmacokinetically screened compounds (ZINC000257324845, ZINC000005169973, and ZINC000009913056) were further scrutinized through computational approaches including molecular docking and molecular dynamics simulations and revealed that these compounds exhibited good binding affinity as compared to GMP and provide stability to their respective complex with the NTD. Our findings could disrupt the binding of viral RNA to NTD, which may inhibit the essential functions of NTD. These findings may further provide an impetus to develop the novel and potential inhibitor against SARS-CoV-2.