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Inferring residue level hydrogen deuterium exchange with ReX

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.
Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1038/s42004-025-01719-4

Authors

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Institution:
University of Oxford
Division:
SSD
Department:
Divisional Administration
Sub department:
Kavli Institute for Nanoscience Discovery
Role:
Author
ORCID:
0000-0001-5669-8506
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Role:
Author
ORCID:
0000-0002-2480-3110
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Institution:
University of Oxford
Division:
MPLS
Department:
Statistics
Sub department:
Statistics
Role:
Author
ORCID:
0000-0003-1388-2252


Publisher:
Nature Research
Journal:
Communications Chemistry More from this journal
Volume:
8
Issue:
1
Article number:
343
Publication date:
2025-11-10
Acceptance date:
2025-09-18
DOI:
EISSN:
2399-3669
ISSN:
2399-3669


Language:
English
Pubs id:
2325632
UUID:
uuid_b024add6-b575-46c9-a9d7-f28f761dbac9
Local pid:
pubs:2325632
Source identifiers:
3458334
Deposit date:
2025-11-10
ARK identifier:
This ORA record was generated from metadata provided by an external service. It has not been edited by the ORA Team.

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