Molecular docking and dynamic simulations for antiviral compounds against SARS-CoV-2: A computational study

Area of Science:

• Computational drug discovery
• Virology
• Medicinal chemistry

Background:

• No approved treatments or vaccines exist for COVID-19.
• Targeting the SARS-CoV-2 main protease (6LU7) is a key strategy for antiviral drug design.
• Existing antiviral drugs and natural compounds are potential therapeutic candidates.

Purpose of the Study:

• To virtually screen US-FDA approved drugs and plant-derived natural compounds for antiviral activity against SARS-CoV-2.
• To identify potent inhibitors of the SARS-CoV-2 main protease (6LU7) using computational methods.

Main Methods:

• Virtual screening of 62 compounds against the SARS-CoV-2 main protease (6LU7) using Glide docking.
• Evaluation of docking scores to rank compound efficacy.
• Molecular dynamic (MD) simulations (20 ns) to assess the stability of protein-inhibitor complexes.

Main Results:

• Lopinavir, amodiaquine, and theaflavin digallate (TFDG) exhibited the best docking scores.
• MD simulations confirmed the stable binding of these three compounds within the protease's active site.
• These compounds show potential as effective SARS-CoV-2 inhibitors.

Conclusions:

• Lopinavir, amodiaquine, and TFDG are promising candidates for further development as anti-SARS-CoV-2 therapeutics.
• In silico methods, including molecular docking and MD simulations, are effective for identifying potential antiviral agents.
• This study provides a computational basis for developing new treatments against COVID-19.