Publication
Title
Machine learning‐based peptide‐spectrum match rescoring opens up the immunopeptidome
Author
Abstract
Immunopeptidomics is a key technology in the discovery of targets for immunotherapy and vaccine development. However, identifying immunopeptides remains challenging due to their non‐tryptic nature, which results in distinct spectral characteristics. Moreover, the absence of strict digestion rules leads to extensive search spaces, further amplified by the incorporation of somatic mutations, pathogen genomes, unannotated open reading frames, and post‐translational modifications. This inflation in search space leads to an increase in random high‐scoring matches, resulting in fewer identifications at a given false discovery rate. Peptide‐spectrum match rescoring has emerged as a machine learning‐based solution to address challenges in mass spectrometry‐based immunopeptidomics data analysis. It involves post‐processing unfiltered spectrum annotations to better distinguish between correct and incorrect peptide‐spectrum matches. Recently, features based on predicted peptidoform properties, including fragment ion intensities, retention time, and collisional cross section, have been used to improve the accuracy and sensitivity of immunopeptide identification. In this review, we describe the diverse bioinformatics pipelines that are currently available for peptide‐spectrum match rescoring and discuss how they can be used for the analysis of immunopeptidomics data. Finally, we provide insights into current and future machine learning solutions to boost immunopeptide identification.
Language
English
Source (journal)
Proteomics. - Weinheim
Publication
Weinheim : 2023
ISSN
1615-9853
DOI
10.1002/PMIC.202300336
Volume/pages
(2023) , p. 1-11
Article Reference
2300336
ISI
001108809100001
Pubmed ID
38009585
Full text (Publisher's DOI)
Full text (open access)
UAntwerpen
Faculty/Department
Research group
Project info
Beyond the genome: Ethical Aspects of Large Cohort studies.
Publication type
Subject
Affiliation
Publications with a UAntwerp address
External links
Web of Science
Record
Identifier
Creation 28.11.2023
Last edited 16.02.2024
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