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Updated: Jun 13, 2025

Deep Proteome Profiling by Isobaric Labeling, Extensive Liquid Chromatography, Mass Spectrometry, and Software-assisted Quantification
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Integrating Alternative Fragmentation Techniques into Standard LC-MS Workflows Using a Single Deep Learning Model

Nikita Levin1,2, Cemil Can Saylan3, Joel Lapin3

  • 1Rosalind Franklin Institute, Harwell Campus, OX11 0QX Didcot, U.K.

Biorxiv : the Preprint Server for Biology
|June 12, 2025
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Summary
This summary is machine-generated.

We developed a versatile mass spectrometer and a deep learning model for enhanced proteomics analysis. This approach significantly increases protein identifications in both data-dependent and data-independent acquisition methods.

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Area of Science:

  • Proteomics
  • Mass Spectrometry
  • Computational Biology

Background:

  • Traditional Collision-Induced Dissociation (CID) is a standard but limited fragmentation technique in mass spectrometry.
  • Exploring alternative fragmentation methods like Ultraviolet Photodissociation (UVPD), Electron-Induced Dissociation (EID), and Electron-Capture Dissociation (ECD) can yield richer spectral data.
  • Integrating diverse fragmentation techniques into automated workflows is crucial for deep proteomics.

Purpose of the Study:

  • To build and characterize a mass spectrometer capable of automated multiple fragmentation techniques (CID, UVPD, EID, ECD) within Liquid Chromatography-Mass Spectrometry (LC-MS).
  • To develop a unified deep learning model (Prosit) for predicting fragment ion intensities across these varied dissociation methods.
  • To evaluate the impact of these advanced fragmentation techniques and the deep learning model on protein identification rates in deep proteomics.

Main Methods:

  • Automated mass spectrometer construction supporting CID (beam and resonant), UVPD, EID, and ECD.
  • Multienzyme deep proteomics experiments for generating large-scale datasets.
  • Development and application of a single Prosit deep learning model for fragment ion intensity prediction.
  • Integration of the model into the FragPipe software (MSBooster module) for spectral rescoring.

Main Results:

  • The automated mass spectrometer successfully generated data using multiple fragmentation techniques.
  • A single Prosit deep learning model accurately predicted fragment ion intensities for all tested dissociation methods.
  • Rescoring with the Prosit model improved protein identifications by over 10% on average for both data-dependent acquisition (DDA) and data-independent acquisition (DIA).
  • UVPD and EID provided richer and more comprehensive spectra compared to CID.

Conclusions:

  • Alternative fragmentation techniques (UVPD, EID, ECD) are now viable for standard data analysis pipelines.
  • The developed deep learning model enhances protein identification efficiency and comprehensiveness in proteomics.
  • This integrated approach offers a powerful tool for deep proteome characterization, rivaling and sometimes exceeding CID's performance.