Targeting SARS-CoV-2 Spike Protein/ACE2 Protein-Protein Interactions: a Computational Study

Area of Science:

• Virology
• Drug Discovery
• Computational Chemistry

Background:

• The SARS-CoV-2 spike (S) glycoprotein mediates viral entry into host cells by binding to the angiotensin-converting enzyme 2 (ACE2) receptor.
• The receptor-binding domain (RBD) of the S protein is crucial for this ACE2 interaction.
• Protein-protein interactions (PPIs) between the SARS-CoV-2 RBD and human ACE2 present a viable therapeutic target to inhibit viral entry.

Purpose of the Study:

• To identify potential inhibitors of the SARS-CoV-2 RBD-ACE2 interaction using computational methods.
• To explore structure-based virtual screening combined with machine learning for drug discovery.
• To find a promising scaffold for developing novel SARS-CoV-2 infection treatments.

Main Methods:

• Employed machine learning approaches to support structure-based virtual screening.
• Screened the ZINC database to identify potential protein-protein interaction (PPI) inhibitors.
• Performed binding mode analysis on identified promising scaffolds.

Main Results:

• Successfully identified a promising scaffold through computational screening.
• The identified scaffold demonstrated potential as an inhibitor of the SARS-CoV-2 RBD-ACE2 interaction.
• The computational protocol proved effective for discovering PPI inhibitors.

Conclusions:

• Structure-based virtual screening, supported by machine learning, is an effective strategy for discovering SARS-CoV-2 entry inhibitors.
• The identified scaffold serves as a valuable starting point for developing new therapeutic agents against SARS-CoV-2.
• Targeting the RBD-ACE2 interaction offers a promising avenue for antiviral drug development.