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Peptide Identification Using Tandem Mass Spectrometry

Peptide Identification Using Tandem Mass Spectrometry

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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Related Experiment Video

Updated: Jun 29, 2025

Immunopeptidomics: Isolation of Mouse and Human MHC Class I- and II-Associated Peptides for Mass Spectrometry Analysis

Immunopeptidomics: Isolation of Mouse and Human MHC Class I- and II-Associated Peptides for Mass Spectrometry Analysis

Published on: October 15, 2021

Deep Learning-Assisted Analysis of Immunopeptidomics Data.

Wassim Gabriel¹, Mario Picciani¹, Matthew The²

¹Computational Mass Spectrometry, TUM School of Life Sciences, Technical University of Munich, Freising, Germany.

Methods in Molecular Biology (Clifton, N.J.)

|March 29, 2024

Summary

This summary is machine-generated.

Deep learning models enhance mass spectrometry analysis for human leukocyte antigen (HLA) peptides, improving identification accuracy. This approach aids in discovering disease-specific peptides and neo-epitopes, overcoming computational challenges in immunopeptidomics.

Keywords:

Deep learning Immunopeptidomics Mass spectrometry Peptide identification Prosit Rescoring Visualizations

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Last Updated: Jun 29, 2025

Immunopeptidomics: Isolation of Mouse and Human MHC Class I- and II-Associated Peptides for Mass Spectrometry Analysis

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Published on: October 15, 2021

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Published on: March 25, 2014

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Published on: October 19, 2021

Area of Science:

Mass Spectrometry
Immunopeptidomics
Computational Biology

Background:

Liquid chromatography-coupled mass spectrometry (LC-MS/MS) is crucial for identifying human leukocyte antigen (HLA) peptides.
Analyzing HLA peptides presents unique computational and statistical challenges compared to standard proteomics.
Fragment ion intensity-based scores significantly improve peptide identification, especially for non-tryptic peptides.

Purpose of the Study:

To detail procedures for applying deep learning models in mass spectrometry-based immunopeptidomics.
To demonstrate how to analyze and validate spectral data using state-of-the-art deep learning tools.
To showcase the benefits of deep learning for HLA peptide identification and neo-epitope discovery.

Main Methods:

Utilizing deep learning frameworks like Prosit for fragment ion intensity and retention time prediction.
Applying tools such as Universal Spectrum Explorer (USE) and Oktoberfest (online/offline) for spectral analysis.
Leveraging intensity-based scoring for enhanced peptide matching in mass spectrometry data.

Main Results:

Deep learning-assisted analysis increases the number of confidently identified HLA peptides.
Facilitates the discovery of confidently identified neo-epitopes.
Assists in the assessment of cryptic peptides, including spliced peptides.

Conclusions:

Deep learning models offer powerful solutions to computational challenges in HLA peptide analysis.
These methods enhance the accuracy and scope of immunopeptidomics studies.
The described procedures provide a framework for advancing personalized medicine through improved peptide identification.