Target identification for repurposed drugs active against SARS-CoV-2 via high-throughput inverse docking

Area of Science:

• Computational drug discovery
• Virology
• Pharmacology

Background:

• Drug repurposing is crucial for rapid pandemic response.
• High-throughput screening identified potential SARS-CoV-2 drugs but lacked target identification.
• Understanding drug-target interactions is vital for developing effective antiviral therapies.

Purpose of the Study:

• To develop and validate a computational inverse-docking protocol for identifying drug targets against SARS-CoV-2.
• To screen 152 existing drugs for potential SARS-CoV-2 activity and predict their molecular targets.
• To provide insights for rational drug design against viral pathogens.

Main Methods:

• Utilized a novel computational inverse-docking protocol with all-atom protein structures.
• Employed a combination of docking methods to rank potential drug targets.
• Validated the protocol using known drug-target pairs, including antiviral and non-antiviral compounds.

Main Results:

• Identified human enzymes TMPRSS2 and PIKfyve, and viral enzymes Helicase and PLpro as common preferential targets for 152 repurposed drugs.
• Correlated TMPRSS2 targeting with serine protease inhibitors and PIKfyve targeting with tyrosine kinase inhibitors.
• Structural analysis provided insights into drug-target binding mechanisms.

Conclusions:

• The inverse-docking protocol effectively predicts drug targets for repurposed antiviral compounds.
• Identified key host and viral targets that can guide future drug development strategies.
• Findings support rational drug design for novel antiviral therapies against SARS-CoV-2 and other pathogens.