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A Drug Repurposing Approach to Identify Therapeutics by Screening Pathogen Box Exploiting SARS-CoV-2 Main Protease.

Rashmi TyagiAnubrat PaulV Samuel RajKrishna Kumar OjhaSunil KumarAditya K PandaAnurag ChaurasiaManoj Kumar Yadav
Published in: Chemistry & biodiversity (2023)
Coronavirus disease-19 (COVID-19) is caused by severe acute respiratory syndrome coronavirus -2 (SARS-CoV-2) and is responsible for a higher degree of morbidity and mortality worldwide. There is a smaller number of approved therapeutics available to target the SARS-CoV-2 virus, and the virus is evolving at a fast pace. So, there is a continuous need for new therapeutics to combat COVID-19. The main protease (M pro ) enzyme of SARS-CoV-2 is essential for replication and transcription of the viral genome, thus could be a potent target for the treatment of COVID-19. In the present study, we performed an in-silico screening analysis of 400 diverse bioactive inhibitors with proven antibacterial and antiviral properties against M pro drug target. Ten compounds showed a higher binding affinity for M pro than the reference compound (N3), with desired physicochemical properties. Furthermore, in-depth docking and superimposition revealed that three compounds (MMV1782211, MMV1782220, and MMV1578574) are actively interacting with the catalytic domain of M pro . In addition, the molecular dynamics simulation study showed a solid and stable interaction of MMV178221-M pro complex compared to the other two molecules (MMV1782220, and MMV1578574). In line with this observation, MM/PBSA free energy calculation also demonstrated the highest binding free energy of -115.8 kJ/mol for MMV178221-M pro compound. In conclusion, the present in silico analysis revealed MMV1782211 as a possible and potent molecule to target the M pro and must be explored in vitro and in vivo to combat the COVID-19.
Keyphrases
  • sars cov
  • respiratory syndrome coronavirus
  • anti inflammatory
  • coronavirus disease
  • molecular dynamics
  • small molecule
  • molecular docking
  • emergency department
  • data analysis