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 often require extensive computational time.
• Accurately modeling conformational changes in protein targets remains a challenge.

Purpose of the Study:

• To introduce a novel computational method, hydrogen-exchange experimental structure prediction (HX-ESP).
• To improve the accuracy and efficiency of predicting ligand binding modes.
• To address limitations of traditional MD simulations for targets with significant conformational flexibility.

Main Methods:

• Integrating hydrogen exchange (HX) experimental data with MD simulations.
• Developing the HX-ESP workflow for structure prediction.
• Benchmarking HX-ESP against X-ray crystallography data for PAK1 and MAP4K1 (HPK1) targets.

Main Results:

• HX-ESP accurately predicts ligand binding modes, including those requiring large conformational changes.
• The method outperforms flexible docking, particularly for ligands inducing significant target adjustments.
• HX-ESP reduces the computational time required for binding predictions compared to traditional MD.

Conclusions:

• HX-ESP offers a significant advancement in computational modeling for drug discovery.
• The method accelerates the identification of effective therapeutic candidates.
• Objective guidance of simulations with experimental HX data enhances prediction accuracy.