Integrating Hydrogen Exchange with Molecular Dynamics for Improved Ligand Binding Predictions

We introduce hydrogen-exchange experimental structure prediction (HX-ESP), a method that integrates hydrogen exchange (HX) data with molecular dynamics (MD) simulations to accurately predict ligand binding modes, even for targets requiring significant conformational changes. Benchmarking HX-ESP by fitting two ligands to PAK1 and four ligands to MAP4K1 (HPK1) and comparing the results to X-ray crystallography structures, demonstrates that HX-ESP can identify binding modes across a range of affinities significantly outperforming flexible docking for ligands necessitating large conformational adjustments. By objectively guiding simulations with experimental HX data, HX-ESP overcomes the long time scales required for binding predictions using traditional MD. This advancement enhances the accuracy of computational modeling in drug discovery and thus will accelerate the development of effective therapeutics.

Authors

Publication Details

Journal
Journal of Chemical Information and Modeling
Published
2025-06-11
DOI
https://doi.org/10.1021/acs.jcim.5c00397
Citations
3
Primary Topic
Computational Drug Discovery Methods
Type
article
Field-Weighted Citation Impact
4.51

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Integrating Hydrogen Exchange with Molecular Dynamics for Improved Ligand Binding Predictions

Ping Wu, Weiru Wang, James R. Kiefer, Benjamin T. Walters et al.
3 citations
Journal of Chemical Information and Modeling
Computational Drug Discovery Methods
4.51
article

Integrating Hydrogen Exchange with Molecular Dynamics for Improved Ligand Binding Predictions

Ping Wu, Weiru Wang, James R. Kiefer, Benjamin T. Walters, Alexander W. Patapoff
article en
3 citations

Abstract

We introduce hydrogen-exchange experimental structure prediction (HX-ESP), a method that integrates hydrogen exchange (HX) data with molecular dynamics (MD) simulations to accurately predict ligand binding modes, even for targets requiring significant conformational changes. Benchmarking HX-ESP by fitting two ligands to PAK1 and four ligands to MAP4K1 (HPK1) and comparing the results to X-ray crystallography structures, demonstrates that HX-ESP can identify binding modes across a range of affinities significantly outperforming flexible docking for ligands necessitating large conformational adjustments. By objectively guiding simulations with experimental HX data, HX-ESP overcomes the long time scales required for binding predictions using traditional MD. This advancement enhances the accuracy of computational modeling in drug discovery and thus will accelerate the development of effective therapeutics.

Journal of Chemical Information and ModelingVol. 65(12)
Roche
Openalex Percentile: Top 5%
Computational Drug Discovery Methods
4.51
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Integrating Hydrogen Exchange with Molecular Dynamics for Improved Ligand Binding Predictions — Ping Wu, Weiru Wang, et al. · Journal of Chemical Information and Modeling (2025) | TGRS Research Map | TGRS