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hxsparse: A Feature Extraction Algorithm for Hydrogen Exchange-Mass Spectrometry Based on a Linear Deuteration Model

Yuqi Shi1, Jarod Hart2, David D Weis1

  • 1Department of Chemistry, The University of Kansas, Lawerence, Kansas 66045, United States.

Analytical Chemistry
|October 2, 2025
PubMed
Summary

A new algorithm, hxsparse, improves deuterium extraction from hydrogen exchange-mass spectrometry (HX-MS) data. This method outperforms commercial software and expert analysis for deuterated peptide mass determination.

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Area of Science:

  • Analytical Chemistry
  • Biochemistry
  • Computational Biology

Background:

  • Accurate mass extraction of deuterated peptides from LC-MS data in hydrogen exchange-mass spectrometry (HX-MS) is crucial but challenging.
  • Traditional LC-MS feature extraction algorithms are not optimized for the complexities of HX-MS data.
  • Existing commercial software lacks dedicated, literature-described algorithms for HX-MS specific feature extraction.

Purpose of the Study:

  • To develop and describe a novel algorithm, hxsparse, for automated feature extraction in HX-MS data.
  • To model peptide deuteration using discrete states and LASSO regularization.
  • To evaluate the performance of hxsparse against commercial software and expert curation.

Main Methods:

  • Developed hxsparse, an algorithm utilizing an exact model for peptide deuteration states.
  • Applied least absolute shrinkage and selection operator (LASSO) regularization to introduce sparsity.
  • Compared hxsparse performance with commercial software and manual expert curation on LC-MS data.

Main Results:

  • hxsparse successfully extracts average masses of deuterated peptides from HX-MS data.
  • Automated feature extraction using hxsparse demonstrated superior performance compared to commercial software.
  • hxsparse results closely approximated those achieved by expert manual curation.

Conclusions:

  • hxsparse provides an effective and accurate automated solution for feature extraction in HX-MS data analysis.
  • The algorithm's performance suggests it is a valuable tool for researchers working with deuterium labeling experiments.
  • This work addresses a significant gap in the literature for specialized HX-MS data analysis tools.