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Related Concept Videos

Peptide Identification Using Tandem Mass Spectrometry01:33

Peptide Identification Using Tandem Mass Spectrometry

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Tandem mass spectrometry, also known as MS/MS or MS2, is an analytical technique that employs two mass analyzers. Essentially it is a series of mass spectrometers that helps isolate a particular biomolecule and then helps study its chemical properties.
This technique helps gather information regarding the protein from which the peptide was obtained and to study the peptides’ amino acid sequence. Identifying peptides from a complex mixture is an important component of the growing field of...
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Updated: Sep 10, 2025

Combining Chemical Cross-linking and Mass Spectrometry of Intact Protein Complexes to Study the Architecture of Multi-subunit Protein Assemblies
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AIRPred: A Deep Learning Model Predictor for Peptide Intensity Ratios in Cross-Linking Mass Spectrometry Improves

Zehong Zhang1,2, Mei Wu1, Max Ruwolt1

  • 1Leibniz-Forschungsinstitut für Molekulare Pharmakologie (FMP), Berlin 13125, Germany.

Analytical Chemistry
|August 27, 2025
PubMed
Summary
This summary is machine-generated.

AIRPred, a deep learning model, improves cross-linked spectrum match identification in structural proteomics by analyzing fragment ion intensity ratios. This method enhances accuracy in cross-linking mass spectrometry (XL-MS) analysis.

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Area of Science:

  • Proteomics
  • Structural Biology
  • Bioinformatics

Background:

  • Cross-linking mass spectrometry (XL-MS) is vital for understanding protein structure and dynamics.
  • Current methods for scoring cross-linked spectrum matches (CSMs) often overlook fragment ion intensity, leading to false positives.

Purpose of the Study:

  • To develop a deep learning model, AIRPred, for enhanced CSM identification in XL-MS.
  • To leverage fragment ion intensity ratios for more accurate distinction between true and false CSMs.

Main Methods:

  • AIRPred utilizes convolutional neural network (CNN) blocks to analyze peptide fragmentation patterns.
  • An attention layer is incorporated to model interactions between cross-linked peptides.
  • The model predicts intensity ratios between cross-linked peptide pairs.

Main Results:

  • Fragment ion intensity ratios are consistent across experiments and effective in differentiating true CSMs from false positives.
  • AIRPred demonstrated high accuracy in predicting intensity ratios during external validation.
  • The model significantly improved the accuracy of peptide identification in XL-MS analysis.

Conclusions:

  • AIRPred offers a novel approach to XL-MS data analysis by incorporating fragment ion intensity information.
  • This deep learning model enhances the reliability and accuracy of CSM identification.
  • AIRPred represents a significant advancement in structural proteomics research.