Galaxy workflows for fragment-based virtual screening: a case study on the SARS-CoV-2 main protease

Area of Science:

• Computational Chemistry
• Bioinformatics
• Drug Discovery

Background:

• Protein-ligand docking and free energy calculations are crucial for drug discovery.
• Existing computational workflows can be complex to implement and manage.
• The SARS-CoV-2 main protease is a key target for antiviral drug development.

Purpose of the Study:

• To present user-friendly workflows for protein-ligand docking and free energy calculations.
• To integrate widely used open-source tools (rDock, GROMACS) into a workflow management system (Galaxy).
• To demonstrate the utility of these workflows for high-throughput virtual screening.

Main Methods:

• Development of Galaxy workflows integrating rDock and GROMACS.
• Execution of workflows via Galaxy's graphical interface and command line.
• High-throughput virtual screening of approximately 50,000 compounds against the SARS-CoV-2 main protease.

Main Results:

• Successfully established and demonstrated functional protein-ligand docking and free energy calculation workflows.
• Executed a large-scale virtual screening campaign against the SARS-CoV-2 main protease.
• Validated the workflows' capability for efficient analysis of drug-like compounds.

Conclusions:

• The presented Galaxy workflows offer an accessible and efficient platform for computational drug discovery.
• These workflows facilitate the application of advanced computational methods like docking and free energy calculations.
• The successful screening against SARS-CoV-2 protease highlights the potential for rapid identification of antiviral drug candidates.