In silico Identification of Novel SARS-CoV-2 Main Protease and Nonstructural Protein 13 (nsp13) Inhibitors through Consensus Docking and Free Binding Energy Calculations
Emilio Mateev, Maya Georgieva, Alexander Zlatkov · Combinatorial Chemistry & High Throughput Screening · 2022
BACKGROUND: A new strain of a novel disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been recently declared a pandemic by the World Health Organization (WHO). The virus results in significant mortality and morbidity across the planet; therefore, novel treatments are urgently required. Recently deposited crystallographic structures of SARS-CoV-2 proteins have ignited the interest in virtual screenings of large databases. OBJECTIVE: In the current study, we evaluated the inhibitory capacity of the IMPPAT phytochemical database (8500 compounds) and the SuperDRUG2 dataset (4000 compounds) in SARS-CoV-2 main protease and helicase Nsp13 through consensus-based docking simulations. METHODS: Glide and GOLD 5.3 were implemented in the in silico process. Further MM/GBSA calculations of the top 10 inhibitors in each protein were carried out to investigate the binding free energy of the complexes. An analysis of the major ligand-protein interactions was also conducted. RESULTS: After the docking simulations, we acquired 10 prominent phytochemicals and 10 FDAapproved drugs capable of inhibiting Nsp5 and Nsp13. Delphinidin 3,5,3'-triglucoside and hirsutidin 3-O-(6-O-p-coumaroyl)glucoside demonstrated the most favorable binding free energies against Nsp5 and Nsp13, respectively. CONCLUSION: In conclusion, the analysis of the results identified that the phytochemicals demonstrated enhanced binding capacities compared to the FDA-approved database.