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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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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

Contemplating immunopeptidomes to better predict them.

David Gfeller¹, Yan Liu¹, Julien Racle¹

¹Department of Oncology, Ludwig Institute for Cancer Research Lausanne, University of Lausanne, Switzerland; Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland; Agora Cancer Research Centre, 1011 Lausanne, Switzerland; Swiss Cancer Center Leman (SCCL), 1011 Lausanne, Switzerland.

Seminars in Immunology

|January 9, 2023

Summary

This summary is machine-generated.

Identifying T-cell epitopes is crucial for understanding immunity in diseases and cancer. Advances in mass spectrometry and computational analysis improve the prediction of these key immune targets, aiding vaccine and therapy development.

Keywords:

Antigen presentation Epitope predictions Immunopeptidomics Machine learning

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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

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Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions

Published on: January 26, 2024

Area of Science:

Immunology
Computational Biology
Proteomics

Background:

T-cell epitopes are vital for immune recognition in infectious diseases, autoimmunity, and cancer.
They serve as targets for personalized vaccines and T-cell therapies, particularly in cancer immunotherapy.
T-cell epitopes are short peptides presented by Major Histocompatibility Complex (MHC) molecules.

Purpose of the Study:

To review computational developments for analyzing experimentally determined immunopeptidomes.
To enhance understanding of antigen presentation and MHC binding specificities.
To improve the prediction of MHC ligands and T-cell epitopes.

Main Methods:

Analysis of mass spectrometry (MS)-based immunopeptidome profiling data.
Review of computational algorithms for MHC ligand prediction.
Discussion of methods for predicting T-cell receptor (TCR) recognition and immunogenicity.

Main Results:

Mass spectrometry techniques have identified vast numbers of MHC-presented peptides, including T-cell recognized epitopes.
Experimental data have revealed fundamental properties of antigen presentation pathways.
Computational approaches have significantly improved the prediction of naturally presented MHC ligands and T-cell epitopes.

Conclusions:

Computational analysis of immunopeptidomes is essential for advancing our understanding of antigen presentation.
Improved prediction of MHC ligands and T-cell epitopes has implications for therapeutic strategies.
Future research should focus on predicting TCR recognition and immunogenicity beyond antigen presentation.