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Updated: Jul 5, 2025

A Hydrogen-Deuterium Exchange Mass Spectrometry HDX-MS Platform for Investigating Peptide Biosynthetic Enzymes
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Deep Learning Enables Automatic Correction of Experimental HDX-MS Data with Applications in Protein Modeling.

Ramin E Salmas1, Antoni J Borysik1

  • 1Department of Chemistry, King's College London, Britannia House, London SE1 1DB, U.K.

Journal of the American Society for Mass Spectrometry
|January 23, 2024
PubMed
Summary

Deep neural networks can automatically correct mass shifts in hydrogen-deuterium exchange mass spectrometry (HDX-MS) data. This AI-driven approach improves accuracy for advanced analysis and protein structure modeling.

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Area of Science:

  • Biochemistry
  • Analytical Chemistry
  • Computational Biology

Background:

  • Hydrogen-deuterium exchange mass spectrometry (HDX-MS) measures protein dynamics.
  • Mass shifts in HDX-MS can deviate from expected values due to deuterium back and forward exchange.
  • Accurate mass shift correction is crucial for advanced HDX-MS data processing and interpretation.

Purpose of the Study:

  • To demonstrate the potential of deep neural networks for automatic correction of HDX-MS data.
  • To evaluate the performance of an AI model in improving data fidelity across various analytical levels.
  • To assess the utility of AI-corrected HDX-MS data for protein structure modeling.

Main Methods:

  • Development and application of a multilayer perceptron (MLP) model.
  • Training the MLP to learn the mapping between uncorrected and corrected HDX-MS mass shifts.
  • Rigorous testing of the model at peptide, residue, and protein fold levels.

Main Results:

  • The AI model successfully learned to correct mass shifts caused by deuterium exchange.
  • Corrected data improved accuracy in calculating protection factors and identifying protein folds.
  • The approach showed significant potential for enhancing HDX-MS data fidelity.

Conclusions:

  • Deep neural networks offer a powerful tool for automated HDX-MS data correction.
  • AI-driven correction enhances the reliability of HDX-MS for advanced applications, including protein modeling.
  • Future online tools could predict corrected mass shifts, improving workflow efficiency and enabling retrospective data analysis.