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

Kevin A. Kovalchik, David J. Hamelin, Peter Kubiniok, Benoîte Bourdin, Fatima Mostefai, Raphaël Poujol, Bastien Paré, Shawn M. Simpson, John Sidney, Éric Bonneil, Mathieu Courcelles, Sunil Kumar Saini, Mohammad Shahbazy, Saketh Kapoor, Vigneshwar Rajesh, Maya Weitzen, Jean-Christophe Grenier, Bayrem Gharsallaoui, Loïze Maréchal, Zhaoguan Wu, Christopher Savoie, Alessandro Sette, Pierre Thibault, Isabelle Sirois, Martin A. Smith, Hélène Decaluwe, Julie G. Hussin (), Mathieu Lavallée-Adam () and Etienne Caron ()
Additional contact information
Kevin A. Kovalchik: Université de Montréal
David J. Hamelin: Université de Montréal
Peter Kubiniok: Université de Montréal
Benoîte Bourdin: Université de Montréal
Fatima Mostefai: Université de Montréal
Raphaël Poujol: Université de Montréal
Bastien Paré: Université de Montréal
Shawn M. Simpson: Université de Montréal
John Sidney: La Jolla Institute for Immunology
Éric Bonneil: Institute of Research in Immunology and Cancer
Mathieu Courcelles: Institute of Research in Immunology and Cancer
Sunil Kumar Saini: Technical University of Denmark
Mohammad Shahbazy: Monash University
Saketh Kapoor: Yale School of Medicine
Vigneshwar Rajesh: Yale School of Medicine
Maya Weitzen: Yale School of Medicine
Jean-Christophe Grenier: Université de Montréal
Bayrem Gharsallaoui: Université de Montréal
Loïze Maréchal: Université de Montréal
Zhaoguan Wu: Université de Montréal
Christopher Savoie: Université de Montréal
Alessandro Sette: La Jolla Institute for Immunology
Pierre Thibault: Institute of Research in Immunology and Cancer
Isabelle Sirois: Université de Montréal
Martin A. Smith: Université de Montréal
Hélène Decaluwe: Université de Montréal
Julie G. Hussin: Université de Montréal
Mathieu Lavallée-Adam: University of Ottawa
Etienne Caron: Université de Montréal

Nature Communications, 2024, vol. 15, issue 1, 1-22

Abstract: 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.

Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.nature.com/articles/s41467-024-54734-9 Abstract (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-54734-9

Ordering information: This journal article can be ordered from
https://www.nature.com/ncomms/

DOI: 10.1038/s41467-024-54734-9

Access Statistics for this article

Nature Communications is currently edited by Nathalie Le Bot, Enda Bergin and Fiona Gillespie

More articles in Nature Communications from Nature
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().