A Computational Approach for Identifying Potential Phytochemicals Against Non-structural Protein 1 (Nsp1) of SARS-CoV-2

Abstract

Aim and objective: A recent study has revealed that non-structural protein 1 (Nsp1) of the SARS-CoV-2 is one of the novel targets for developing new antiviral drugs. To date, there is no significant exact medication available to treat Covid-19. As a result, both the death toll and the number of people affecting by this disease are increasing with each passing day. 35 phytochemicals having antiviral properties were taken to get the best compounds against Nsp1 Materials and Methods: As no PDB structure of this protein is available, homology modeling was done to predict the probable structure. After homology modeling, the best model was taken according to C-score and TM- score and then validated using different web servers. After validation, docking of these compounds was done using AutoDock vina, vega zz, and PyRx, and consensus docking score was considered to select molecules after docking. Finally, the orbitals energy calculation of these compounds was done to check their activity and the binding interactions of these molecules also analyzed.

Results: Molecules having a consensus score of -8kcal/mol or more negative were kept for further study and it was seen that 16 molecules had the given criteria. Then, drug-likeness filtration was done according to Lipinski's rule of five and 11 molecules remained. Out of these 11 molecules, 5 molecules had satisfactory ADMET properties. Calculation of orbital energy revealed their activity.

Conclusion: It is expected that this research might be helpful for the development of new antiviral drugs active against SARS-CoV-2 targeting Nsp1.

Keywords: ADMET; Homology modelling; I-TASSER.; Nsp1; SARS-CoV-2; molecular docking.