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J Biomol Struct Dyn ; 40(15): 7143-7166, 2022 09.
Article in English | MEDLINE | ID: covidwho-1124342

ABSTRACT

The global pandemic due to the novel Severe Acute Respiratory Syndrome Corona Virus 2 (SARS-CoV-2) has taken more than a million lives. Lack of definitive vaccine/drugs against this highly contagious virus has accelerated exploratory research on novel natural and synthetic inhibitors. Tea is a rich source of bioactives and known to have antiviral properties. In this study, an in silico strategy involving ADMET property screening, receptor-ligand docking and molecular dynamic (MD) simulation was employed to screen potential tea bio-active inhibitors against three selected targets (RdRp, 3CLpro and PLpro) of SARS-CoV-2. Among the 70 tea bioactives screened, theaflavin 3,3'-di-gallate (TF3), Procyanidin B2 and Theaflavin 3-gallate (TF2a) exhibited highest binding affinities towards RdRp, 3CLpro/Mpro and PLpro targets of SARS-CoV-2 with low docking scores of -14.92, -11.68 and -10.90 kcal/mol, respectively. All of them showed a substantial number of hydrogen bonds along with other interactions in and around the active sites. Interestingly, the top bioactives in our study showed higher binding affinities compared with known antiviral drugs. Further, the top protein-ligand complexes showed less conformational changes during binding when subjected to MD simulation for 100 nanoseconds. The MMPBSA results revealed that RdRp-TF3, 3CLpro-Procyanidin B2 and PLpro-TF2a complexes were stable with binding free energies of -93.59 ± 43.97, -139.78 ± 16.51 and -96.88 ± 25.39 kJ/mol, respectively. Our results suggest that theaflavin 3,3'-digallate, Theaflavin 3-gallate and Procyanidin B2 found in black tea have the potential to act as inhibitors for selected targets of SARS-CoV-2 and can be considered as drug candidates in future studies against COVID-19.


Subject(s)
COVID-19 Drug Treatment , SARS-CoV-2 , Antiviral Agents/chemistry , Coronavirus 3C Proteases , Coronavirus Papain-Like Proteases , Coronavirus RNA-Dependent RNA Polymerase , Humans , Ligands , Molecular Docking Simulation , Molecular Dynamics Simulation , RNA-Dependent RNA Polymerase , Tea
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