Exploring the therapeutic nature of limonoids and triterpenoids against SARS-CoV-2 by targeting nsp13, nsp14, and nsp15 through molecular docking and dynamic simulations

Abstract

Background and aim

The ongoing global pandemic due to SARS-CoV-2 caused a medical emergency. Since December 2019, the COVID-19 disease is spread across the globe through physical contact and respiratory droplets. Coronavirus caused a severe effect on the human immune system where some of the non-structural proteins (nsp) are involved in virus-mediated immune response and pathogenesis. To suppress the viral RNA replication mechanism and immune-mediated responses, we aimed to identify limonoids and triterpenoids as antagonists by targeting helicases (nsp13), exonuclease (nsp14), and endoribonuclease (nsp15) of SARS-CoV-2 as therapeutic proteins.

Experimental procedure

In silico molecular docking and drug-likeness of a library of 369 phytochemicals from limonoids and triterpenoids were performed to screen the potential hits that binds effectively at the active site of the proteins target. In addition, the molecular dynamics simulations of the proteins and their complexes with the potential hits were performed for 100 ns by using GROMACS.

Results and conclusion

The potential compounds 26-deoxyactein and 25-O-anhydrocimigenol 3-O-beta- d-xylopyranoside posing strong interactions with a minimum binding energy of −10.1 and −9.5 kcal/mol, respectively and sustained close contact with nsp13 for 100 ns. The nsp14 replication fork activity was hindered by the tomentosolic acid, timosaponin A-I, and shizukaol A with the binding affinity score of −9.2, −9.2, and −9.0 kcal/mol, respectively. The nsp15 endoribonuclease catalytic residues were inhibited potentially by limonin, 25-O-anhydrocimigenol 3-O-alpha- l-arabinopyranoside, and asperagenin posing strong binding affinity scores of −9.0, −8.8, and −8.7 kcal/mol, respectively. Computationally predicted potential phytochemicals for SARS-CoV-2 are known to possess various medicinal properties.

Graphical abstract