Nursing

Gene

Context (archaeology)

2019-20 coronavirus outbreak

Virtual screening

Biology

Virology

Antiretroviral therapy

Lopinavir

Biochemistry

Computer science

Viral load

DOCK

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)

Outbreak

Drug

Docking (animal)

Computational biology

Drug discovery

Coronavirus disease 2019 (COVID-19)

Protein Data Bank (RCSB PDB)

Paleontology

Bioinformatics

Protease

Enzyme

Infectious disease (medical specialty)

Pharmacology

Medicine

Chemistry

In silico

Virus

Disease

Pathology

Informatics in Medicine Unlocked

The aim of this study was to develop an appropriate anti-viral drug against the SARS-CoV-2 virus. An immediately qualifying strategy would be to use existing powerful drugs from various virus treatments. The strategy in virtual screening of antiviral databases for possible therapeutic effect would be to identify promising drug molecules, as there is currently no vaccine or treatment approved against COVID-19. Targeting the main protease (pdb id: 6LU7) is gaining importance in anti-CoV drug design. In this conceptual context, an attempt has been made to suggest an in silico computational relationship between US-FDA approved drugs, plant-derived natural drugs, and Coronavirus main protease (6LU7) protein. The evaluation of results was made based on Glide (Schrödinger) dock score. Out of 62 screened compounds, the best docking scores with the targets were found for compounds: lopinavir, amodiaquine, and theaflavin digallate (TFDG). Molecular dynamic (MD) simulation study was also performed for 20 ns to confirm the stability behaviour of the main protease and inhibitor complexes. The MD simulation study validated the stability of three compounds in the protein binding pocket as potent binders.

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