A computational study of cooperative binding to multiple SARS-CoV-2 proteins

Area of Science:

• Computational chemistry and virology
• Drug discovery and development

Background:

• Structure-based drug design for SARS-CoV-2 is advancing, but effective small-molecule antivirals are still needed.
• Existing research often focuses on single-protein targets, necessitating broader approaches.

Purpose of the Study:

• To investigate interactions between SARS-CoV-2 proteins and traditional Chinese medicine (TCM) compounds.
• To identify potential antiviral agents and explore cooperative inhibition mechanisms.

Main Methods:

• Ensemble docking and cooperative docking computational approaches.
• Extensive molecular simulations (over 16 microseconds).
• Screening of over 600 TCM compounds against eight SARS-CoV-2 proteins.

Main Results:

• Identified at least 9 TCM compounds with potential binding affinity to key SARS-CoV-2 proteins.
• Observed evidence of compounds simultaneously binding to the same target.
• Demonstrated potential for cooperative inhibition against viral proteins like Spike and RNA-dependent RNA polymerase.

Conclusions:

• Developed a novel computational methodology for assessing antiviral potential of small molecules.
• Highlighted a new strategy for developing cooperative compounds for cocktail therapeutics against SARS-CoV-2.
• Identified promising TCM-derived compounds for further investigation as antivirals.