Machine learning-enhanced immunopeptidomics applied to T-cell epitope discovery for COVID-19 vaccines

Next-generation T-cell-directed vaccines for COVID-19 focus on establishing lasting T-cell immunity against current and emerging SARS-CoV-2 variants. Precise identification of conserved T-cell epitopes is critical for designing effective vaccines. Here we introduce a comprehensive computational framework incorporating a machine learning algorithm-MHCvalidator-to enhance mass spectrometry-based immunopeptidomics sensitivity. MHCvalidator identifies unique T-cell epitopes presented by the B7 supertype, including an epitope from a + 1-frameshift in a truncated Spike antigen, supported by ribosome profiling. Analysis of 100,512 COVID-19 patient proteomes shows Spike antigen truncation in 0.85% of cases, revealing frameshifted viral antigens at the population level. Our EpiTrack pipeline tracks global mutations of MHCvalidator-identified CD8 + T-cell epitopes from the BNT162b4 vaccine. While most vaccine epitopes remain globally conserved, an immunodominant A*01-associated epitope mutates in Delta and Omicron variants. This work highlights SARS-CoV-2 antigenic features and emphasizes the importance of continuous adaptation in T-cell vaccine development.

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Publication Details

Journal
Nature Communications
Published
2024-11-28
DOI
https://doi.org/10.1038/s41467-024-54734-9
Citations
9
Primary Topic
vaccines and immunoinformatics approaches
Type
article
Field-Weighted Citation Impact
2.10

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article

Machine learning-enhanced immunopeptidomics applied to T-cell epitope discovery for COVID-19 vaccines

Étienne Caron, Hélène Decaluwe, Sunil Kumar Saini, Zhaoguan Wu et al.
9 citations
Nature Communications
vaccines and immunoinformatics approaches
2.10
article

Machine learning-enhanced immunopeptidomics applied to T-cell epitope discovery for COVID-19 vaccines

Étienne Caron, Hélène Decaluwe, Sunil Kumar Saini, Zhaoguan Wu, Pierre Thibault, Alessandro Sette, David Hamelin, Fatima Mostefai, Julie Hussin, Raphaël Poujol, Loïze Maréchal, Mathieu Courcelles, Vigneshwar Rajesh, John Sidney, Maya Weitzen, Éric Bonneil, Peter Kubiniok, Isabelle Sirois, Jean‐Christophe Grenier, Martin A. Smith, Shawn M. Simpson, Mathieu Lavallée‐Adam, Bastien Paré, Benoîte Bourdin, Kevin A. Kovalchik, Mohammad Shahbazy, Christopher J. Savoie, Bayrem Gharsallaoui, Saketh Kapoor
article en
9 citations

Abstract

Next-generation T-cell-directed vaccines for COVID-19 focus on establishing lasting T-cell immunity against current and emerging SARS-CoV-2 variants. Precise identification of conserved T-cell epitopes is critical for designing effective vaccines. Here we introduce a comprehensive computational framework incorporating a machine learning algorithm-MHCvalidator-to enhance mass spectrometry-based immunopeptidomics sensitivity. MHCvalidator identifies unique T-cell epitopes presented by the B7 supertype, including an epitope from a + 1-frameshift in a truncated Spike antigen, supported by ribosome profiling. Analysis of 100,512 COVID-19 patient proteomes shows Spike antigen truncation in 0.85% of cases, revealing frameshifted viral antigens at the population level. Our EpiTrack pipeline tracks global mutations of MHCvalidator-identified CD8 + T-cell epitopes from the BNT162b4 vaccine. While most vaccine epitopes remain globally conserved, an immunodominant A*01-associated epitope mutates in Delta and Omicron variants. This work highlights SARS-CoV-2 antigenic features and emphasizes the importance of continuous adaptation in T-cell vaccine development.

Nature CommunicationsVol. 15(1)
La Jolla Institute for Immunology (US), University of Ottawa (CA), Montreal Heart Institute (CA), Australian Regenerative Medicine Institute (AU), Centre Hospitalier Universitaire Sainte-Justine (CA), Yale Cancer Center (US), Yale University (US), Institute for Research in Immunology and Cancer (CA), Mila - Quebec Artificial Intelligence Institute (CA), Monash University (AU), Université de Montréal (CA), Ottawa University (US), Technical University of Denmark (DK)
Yale University, Genome Canada, Institut de Valorisation des Données, Alliance de recherche numérique du Canada, National Institutes of Health, Canadian Institutes of Health Research, Natural Sciences and Engineering Research Council of Canada, Fonds de Recherche du Québec - Santé, National Institute of Allergy and Infectious Diseases
Zero hunger
Openalex Percentile: Top 12%
vaccines and immunoinformatics approaches
2.10
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