Metadynamics-based enhanced sampling protocol for virtual screening: case study for 3CLpro protein for SARS-CoV-2.

Area of Science:

• Computational chemistry and molecular modeling
• Drug discovery and medicinal chemistry
• Virology and infectious diseases

Background:

• Computational methods are crucial for identifying potential drug candidates with high protein affinity.
• Traditional screening methods like docking can miss effective drug compounds.
• Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) requires novel therapeutic strategies.

Purpose of the Study:

• To develop an integrated computational approach for identifying high-affinity ligands for SARS-CoV-2 targets.
• To specifically target the main protease (3CLpro) of SARS-CoV-2.
• To identify and validate novel chemical entities with high binding specificity.

Main Methods:

• Integration of meta-dynamics (enhanced sampling) with all-atom molecular dynamics simulations.
• Exploration of the free energy surface of the SARS-CoV-2 3CLpro binding site in explicit solvent.
• Analysis of 17 prescreened ligands, comparing binding affinities against known inhibitors (N3, 13b α-ketoamide).

Main Results:

• The combined simulation approach successfully explored ligand-protein interactions and binding free energy landscapes.
• Ligands were ranked based on calculated free energy and scoring functions, identifying top candidates.
• The study identified a specific compound with high binding affinity and specificity for SARS-CoV-2 3CLpro.

Conclusions:

• The integrated meta-dynamics and molecular dynamics method is effective for discovering potent drug candidates.
• A promising SARS-CoV-2 3CLpro inhibitor was identified through this computational strategy.
• The identified compound warrants further experimental validation for potential therapeutic use.