Metadynamics-Based Approaches for Modeling the Hypoxia-Inducible Factor 2α Ligand Binding Process.

Area of Science:

• Computational chemistry
• Molecular dynamics simulations
• Biophysics

Background:

• Studying ligand-protein interactions is crucial for drug discovery.
• Enhanced-sampling molecular dynamics methods are used to investigate these processes.
• Simulating binding to complex targets like hypoxia-inducible factor 2α presents challenges.

Purpose of the Study:

• To develop and validate a new computational protocol for studying ligand binding.
• To combine steered molecular dynamics (SMD) and metadynamics for efficient pathway exploration.
• To apply the method to challenging ligand binding systems.

Main Methods:

• A hybrid protocol integrating steered molecular dynamics (SMD) and metadynamics.
• Utilizing path collective variable (PCV) formalism with two collective variables (CVs).
• Application to the binding of two ligands to hypoxia-inducible factor 2α.

Main Results:

• Identification of preferred ligand entrance pathways.
• Characterization of bound and intermediate states using free-energy surfaces.
• Obtained binding affinities consistent with experimental data.

Conclusions:

• The combined SMD-metadynamics approach is effective for complex ligand binding studies.
• The method provides insights into binding mechanisms and affinities.
• This protocol can be applied to similar challenging biological systems.