ArticleClinical & developmental immunology2013
Selection of conserved epitopes from hepatitis C virus for pan-populational stimulation of T-cell responses.
Article in Clinical & developmental immunology, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers.
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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
39 citing papers in PubMed, 55 citations in OpenAlex.
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- Targets of protective immunity and opportunities in hepatitis C virus vaccine development.Nature reviews. Immunology · 2026Review
- Deep learning in next-generation vaccine development for infectious diseases.Molecular therapy. Nucleic acids · 2025Review
- Characteristics of Norovirus capsid protein-specific CD8Virulence · 2024Article
- Tetanus-diphtheria vaccine can prime SARS-CoV-2 cross-reactive T cells.Frontiers in immunology · 2024Article
- Integrating machine learning to advance epitope mapping.Frontiers in immunology · 2024Review
- Multi-epitope vaccines: a promising strategy against viral diseases in swine.Frontiers in cellular and infection microbiology · 2024Review
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- Combining different bacteria in vaccine formulations enhances the chance for antiviral cross-reactive immunity: a detailedFrontiers in immunology · 2023Article
- Peptide-based vaccine for cancer therapies.Frontiers in immunology · 2023Review
- Analysis of LruC lipoprotein and identification of peptides candidates for vaccine development and diagnosis of leptospirosis.PloS one · 2023Article
- To Affinity and Beyond: A Personal Reflection on the Design and Discovery of Drugs.Molecules (Basel, Switzerland) · 2022Review
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- Resilience of Spike-Specific Immunity Induced by COVID-19 Vaccines against SARS-CoV-2 Variants.Biomedicines · 2022Article
- Development of a Novel Multi-Epitope Vaccine Candidate againstArchives of Razi Institute · 2022Article
- Machine Learning Techniques for the Prediction of B-Cell and T-Cell Epitopes as Potential Vaccine Targets with a Specific Focus on SARS-CoV-2 Pathogen: A Review.Pathogens (Basel, Switzerland) · 2022Review
- BCEPS: A Web Server to Predict Linear B Cell Epitopes with Enhanced Immunogenicity and Cross-Reactivity.Cells · 2021Article
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- Article
- Human rhinovirus-specific CD8 T cell responses target conserved and unusual epitopes.FASEB journal : official publication of the Federation of American Societies for Experimental Biology · 2021Article
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The hepatitis C virus (HCV) is able to persist as a chronic infection, which can lead to cirrhosis and liver cancer. There is evidence that clearance of HCV is linked to strong responses by CD8 cytotoxic T lymphocytes (CTLs), suggesting that eliciting CTL responses against HCV through an epitope-based vaccine could prove an effective means of immunization. However, HCV genomic plasticity as well as the polymorphisms of HLA I molecules restricting CD8 T-cell responses challenges the selection of epitopes for a widely protective vaccine. Here, we devised an approach to overcome these limitations. From available databases, we first collected a set of 245 HCV-specific CD8 T-cell epitopes, all known to be targeted in the course of a natural infection in humans. After a sequence variability analysis, we next identified 17 highly invariant epitopes. Subsequently, we predicted the epitope HLA I binding profiles that determine their potential presentation and recognition. Finally, using the relevant HLA I-genetic frequencies, we identified various epitope subsets encompassing 6 conserved HCV-specific CTL epitopes each predicted to elicit an effective T-cell response in any individual regardless of their HLA I background. We implemented this epitope selection approach for free public use at the EPISOPT web server.
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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.