Integrating Hydrogen Exchange with Molecular Dynamics for Improved Ligand Binding Predictions

Area of Science:

• Computational chemistry
• Structural biology
• Drug discovery

Background:

• Predicting ligand binding modes is crucial for drug discovery.
• Traditional molecular dynamics (MD) simulations face challenges with long timescales and conformational changes.
• Experimental data integration can enhance the accuracy of computational predictions.

Purpose of the Study:

• To introduce and validate Hydrogen-Exchange Experimental Structure Prediction (HX-ESP) for accurate ligand binding mode prediction.
• To demonstrate HX-ESP's effectiveness for targets requiring significant conformational changes.
• To improve the efficiency and accuracy of computational drug discovery methods.

Main Methods:

• Integrating hydrogen exchange (HX) data with molecular dynamics (MD) simulations.
• Benchmarking HX-ESP using ligands for PAK1 and MAP4K1 (HPK1).
• Comparing HX-ESP predictions with X-ray crystallography structures.

Main Results:

• HX-ESP successfully identified ligand binding modes for PAK1 and MAP4K1.
• The method accurately predicted binding modes across a range of ligand affinities.
• HX-ESP significantly outperformed flexible docking for ligands requiring large conformational adjustments.

Conclusions:

• HX-ESP accurately predicts ligand binding modes by integrating experimental HX data with MD simulations.
• This method overcomes limitations of traditional MD, especially for targets with conformational flexibility.
• HX-ESP enhances computational modeling accuracy, potentially accelerating therapeutic development.