Antivirals virtual screening to SARS-CoV-2 non-structural proteins

Area of Science:

• Computational drug discovery
• Virology and infectious diseases
• Pharmacology

Background:

• The COVID-19 pandemic, caused by SARS-CoV-2, necessitates rapid development of effective treatments.
• Drug repurposing offers a faster approach to identify potential therapeutics.
• SARS-CoV-2 non-structural proteins (Nsps) are critical targets for antiviral drug development.

Purpose of the Study:

• To virtually screen existing antiviral drugs for potential COVID-19 treatment applications.
• To identify novel drug candidates by evaluating their binding affinity to SARS-CoV-2 Nsps.
• To characterize drug-target interactions for future drug design optimization.

Main Methods:

• Virtual screening of 22 antiviral drugs, azithromycin, and ivermectin against seven SARS-CoV-2 Nsps.
• Molecular docking and molecular dynamics simulations were employed to assess drug-receptor interactions.
• Drug-receptor binding energy was the primary metric for evaluating drug efficacy.

Main Results:

• Paritaprevir demonstrated promising multi-target activity, with favorable binding energies to multiple Nsps, notably ADRP (-36.2 kcal mol⁻¹) and Mpro (-32.2 kcal mol⁻¹).
• Simeprevir was identified as a potent inhibitor of Mpro, a key enzyme in viral replication, with a binding energy of -37.2 kcal mol⁻¹.
• Characterization of hot spot residues at drug-receptor interfaces was performed to guide future drug derivative design.

Conclusions:

• Paritaprevir and simeprevir are promising candidates for COVID-19 drug repurposing, targeting essential SARS-CoV-2 proteins.
• The study highlights the potential of computational methods in accelerating the discovery of antiviral therapies.
• Further research into these drug candidates and their derivatives could lead to improved COVID-19 treatments.