Computational protein-ligand docking and virtual drug screening with the AutoDock suite

Area of Science:

• Computational chemistry
• Molecular modeling
• Drug discovery

Background:

• Computational docking predicts ligand-target binding conformations and energies.
• Docking is crucial for studying biomolecular interactions and structure-based drug design.
• Methods enable rapid virtual screening of large compound libraries.

Purpose of the Study:

• To provide a protocol for docking and virtual screening using the AutoDock suite.
• To demonstrate applications including basic docking, virtual screening, and advanced techniques.
• To cover selective receptor flexibility, active site prediction, and explicit hydration docking.

Main Methods:

• Utilizes the AutoDock program suite for molecular docking simulations.
• Includes protocols for basic ligand-target docking and virtual screening of compound libraries.
• Incorporates advanced methods such as selective receptor flexibility and explicit hydration.

Main Results:

• Successfully demonstrates the application of AutoDock for various docking scenarios.
• Provides a comprehensive protocol executable within approximately 5 hours.
• Highlights the utility of docking in structure-based drug design and virtual screening.

Conclusions:

• AutoDock offers a robust and efficient platform for molecular docking and virtual screening.
• The protocol facilitates structure-based drug design by predicting binding interactions.
• The methods are applicable to a wide range of biomolecular targets and ligand libraries.