Inferring residue level hydrogen deuterium exchange with ReX

Hydrogen-Deuterium Exchange Mass-Spectrometry (HDX-MS) has emerged as a powerful technique to explore the conformational dynamics of proteins and protein complexes in solution. The bottom-up approach to MS uses peptides to represent an average of residues, leading to reduced resolution of deuterium exchange and complicates the interpretation of the data. Here, we introduce ReX, a method to infer residue-level uptake patterns leveraging the overlap in peptides, the temporal component of the data and the correlation along the sequence dimension. This approach infers statistical significance for individual residues by treating HDX-MS as a multiple change-point problem. By fitting our model in a Bayesian non-parametric framework, we perform parameter number inference, differential HDX confidence assessments, and uncertainty estimation for temporal kinetics. Benchmarking against existing methods using a three-way proteolytic digestion experiment shows our method's superior performance at predicting unseen HDX data. Moreover, it aligns HDX-MS with the reporting standards of other structural methods by providing global and local resolution metrics. Using ReX, we analyze the differential flexibility of BRD4's two Bromodomains in the presence of I-BET151 and quantify the conformational variations induced by a panel of seventeen small molecules on LXRα. Our analysis reveals distinct residue-level HDX signatures for ligands with varied functional outcomes, highlighting the potential of this characterisation to inform mode of action analysis.

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Journal
Communications Chemistry
Published
2025-11-10
DOI
https://doi.org/10.1038/s42004-025-01719-4
Primary Topic
Mass Spectrometry Techniques and Applications
Type
article
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article

Inferring residue level hydrogen deuterium exchange with ReX

Charlotte M. Deane, Oliver M. Crook, Chun‐wa Chung, Nathan Gittens
Communications Chemistry
Mass Spectrometry Techniques and Applications
article

Inferring residue level hydrogen deuterium exchange with ReX

Charlotte M. Deane, Oliver M. Crook, Chun‐wa Chung, Nathan Gittens
article en

Abstract

Hydrogen-Deuterium Exchange Mass-Spectrometry (HDX-MS) has emerged as a powerful technique to explore the conformational dynamics of proteins and protein complexes in solution. The bottom-up approach to MS uses peptides to represent an average of residues, leading to reduced resolution of deuterium exchange and complicates the interpretation of the data. Here, we introduce ReX, a method to infer residue-level uptake patterns leveraging the overlap in peptides, the temporal component of the data and the correlation along the sequence dimension. This approach infers statistical significance for individual residues by treating HDX-MS as a multiple change-point problem. By fitting our model in a Bayesian non-parametric framework, we perform parameter number inference, differential HDX confidence assessments, and uncertainty estimation for temporal kinetics. Benchmarking against existing methods using a three-way proteolytic digestion experiment shows our method's superior performance at predicting unseen HDX data. Moreover, it aligns HDX-MS with the reporting standards of other structural methods by providing global and local resolution metrics. Using ReX, we analyze the differential flexibility of BRD4's two Bromodomains in the presence of I-BET151 and quantify the conformational variations induced by a panel of seventeen small molecules on LXRα. Our analysis reveals distinct residue-level HDX signatures for ligands with varied functional outcomes, highlighting the potential of this characterisation to inform mode of action analysis.

Communications ChemistryVol. 8(1)
GlaxoSmithKline (United Kingdom) (GB), University of Oxford (GB), CRUK/MRC Oxford Institute for Radiation Oncology (GB)
Openalex Percentile: Top 69%
Mass Spectrometry Techniques and Applications
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Inferring residue level hydrogen deuterium exchange with ReX — Charlotte M. Deane, Oliver M. Crook, et al. · Communications Chemistry (2025) | TGRS Research Map | TGRS