CHARMM-GUI-Based Induced Fit Docking Workflow to Generate Reliable Protein-Ligand Binding Modes

Area of Science:

• Computational Chemistry
• Structural Biology
• Drug Discovery

Background:

• Molecular docking is crucial for structure-based drug discovery, predicting ligand-protein interactions.
• The induced fit effect, where protein binding sites change conformation, poses a challenge for traditional docking methods.

Purpose of the Study:

• To present a straightforward CHARMM-GUI induced fit docking (CGUI-IFD) workflow.
• To generate reliable protein-ligand binding modes by accounting for receptor flexibility.

Main Methods:

• The CGUI-IFD workflow refines ligand-binding site (LBS) conformations.
• It employs rigid receptor docking followed by high-throughput molecular dynamics (MD) simulations.
• Binding stability and energy are evaluated using RMSD and MM/GBSA.

Main Results:

• The CGUI-IFD workflow achieved an 80% success rate on a benchmark dataset of 258 cross-docking pairs.
• This success rate was defined as RMSD within 2.5 Å of experimental structures.
• The workflow demonstrated reliability in predicting binding modes for various protein targets.

Conclusions:

• The CGUI-IFD workflow provides a robust method for generating accurate ligand binding modes.
• It effectively addresses the induced fit challenge in molecular docking.
• This approach is expected to be valuable for cross-docking applications in drug discovery.