ArticleNature communications2024
Machine learning-enhanced immunopeptidomics applied to T-cell epitope discovery for COVID-19 vaccines.
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
9 citing papers in PubMed.
- Long-term immune response to mRNA anti-SARS-CoV-2 vaccination in patients with cancer.Frontiers in immunology · 2026Trial
- Therapeutic Vaccines for Chronic Viral Infections: From Immune Modulation to Clinical Translation.Vaccines · 2026Review
- Algorithm guided personalized T cell therapy: machine learning unlocks next generation TCR engineered immunotherapy.Pharmacological reports : PR · 2026Review
- The race between viral immune evasion and the MHC class I antigen processing pathway.FEMS microbiology reviews · 2026Review
- Could artificial intelligence gradually replace classical adjuvants?Frontiers in immunology · 2026Review
- Engineering Anti-Tumor Immunity: An Immunological Framework for mRNA Cancer Vaccines.Vaccines · 2025Review
- AI-driven epitope prediction: a system review, comparative analysis, and practical guide for vaccine development.NPJ vaccines · 2025Review
- Predicting pathogen evolution and immune evasion in the age of artificial intelligence.Computational and structural biotechnology journal · 2025Review
- Machine learning-enhanced immunopeptidomics applied to T-cell epitope discovery for COVID-19 vaccines.Nature communications · 2024Article
Corrections and comments
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Authors and funding
29 authors.
Funding
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.