Machine learning-enhanced immunopeptidomics applied to T-cell epitope discovery for COVID-19 vaccines
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 computati...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2024-11-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-024-54734-9 |
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| author | 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 Etienne Caron |
| author_facet | 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 Etienne Caron |
| author_sort | Kevin A. Kovalchik |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-9c66e9cf31c342f29664ca716c9b1d6c |
| institution | Kabale University |
| issn | 2041-1723 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj-art-9c66e9cf31c342f29664ca716c9b1d6c2024-12-01T12:36:22ZengNature PortfolioNature Communications2041-17232024-11-0115112210.1038/s41467-024-54734-9Machine learning-enhanced immunopeptidomics applied to T-cell epitope discovery for COVID-19 vaccinesKevin A. Kovalchik0David J. Hamelin1Peter Kubiniok2Benoîte Bourdin3Fatima Mostefai4Raphaël Poujol5Bastien Paré6Shawn M. Simpson7John Sidney8Éric Bonneil9Mathieu Courcelles10Sunil Kumar Saini11Mohammad Shahbazy12Saketh Kapoor13Vigneshwar Rajesh14Maya Weitzen15Jean-Christophe Grenier16Bayrem Gharsallaoui17Loïze Maréchal18Zhaoguan Wu19Christopher Savoie20Alessandro Sette21Pierre Thibault22Isabelle Sirois23Martin A. Smith24Hélène Decaluwe25Julie G. Hussin26Mathieu Lavallée-Adam27Etienne Caron28CHU Sainte-Justine Research Center, Université de MontréalCHU Sainte-Justine Research Center, Université de MontréalCHU Sainte-Justine Research Center, Université de MontréalCHU Sainte-Justine Research Center, Université de MontréalMontreal Heart Institute, Université de MontréalMontreal Heart Institute, Université de MontréalCHU Sainte-Justine Research Center, Université de MontréalCHU Sainte-Justine Research Center, Université de MontréalCenter for Infectious Disease and Vaccine Research, La Jolla Institute for ImmunologyInstitute of Research in Immunology and CancerInstitute of Research in Immunology and CancerDepartment of Health Technology, Section of Experimental and Translational Immunology, Technical University of DenmarkDepartment of Biochemistry and Molecular Biology and Infection and Immunity Program, Biomedicine Discovery Institute, Monash UniversityDepartment of Immunobiology, Yale School of MedicineDepartment of Immunobiology, Yale School of MedicineDepartment of Immunobiology, Yale School of MedicineMontreal Heart Institute, Université de MontréalCHU Sainte-Justine Research Center, Université de MontréalCHU Sainte-Justine Research Center, Université de MontréalCHU Sainte-Justine Research Center, Université de MontréalCHU Sainte-Justine Research Center, Université de MontréalCenter for Infectious Disease and Vaccine Research, La Jolla Institute for ImmunologyInstitute of Research in Immunology and CancerCHU Sainte-Justine Research Center, Université de MontréalCHU Sainte-Justine Research Center, Université de MontréalCHU Sainte-Justine Research Center, Université de MontréalMontreal Heart Institute, Université de MontréalDepartment of Biochemistry, Microbiology and Immunology, Faculty of Medicine, University of OttawaCHU Sainte-Justine Research Center, Université de MontréalAbstract 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.https://doi.org/10.1038/s41467-024-54734-9 |
| spellingShingle | 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 Etienne Caron Machine learning-enhanced immunopeptidomics applied to T-cell epitope discovery for COVID-19 vaccines Nature Communications |
| title | Machine learning-enhanced immunopeptidomics applied to T-cell epitope discovery for COVID-19 vaccines |
| title_full | Machine learning-enhanced immunopeptidomics applied to T-cell epitope discovery for COVID-19 vaccines |
| title_fullStr | Machine learning-enhanced immunopeptidomics applied to T-cell epitope discovery for COVID-19 vaccines |
| title_full_unstemmed | Machine learning-enhanced immunopeptidomics applied to T-cell epitope discovery for COVID-19 vaccines |
| title_short | Machine learning-enhanced immunopeptidomics applied to T-cell epitope discovery for COVID-19 vaccines |
| title_sort | machine learning enhanced immunopeptidomics applied to t cell epitope discovery for covid 19 vaccines |
| url | https://doi.org/10.1038/s41467-024-54734-9 |
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