ArticleBMC bioinformatics2013
PREDIVAC: CD4+ T-cell epitope prediction for vaccine design that covers 95% of HLA class II DR protein diversity.
Article in BMC bioinformatics, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 41 papers.
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41 citing papers in PubMed, 74 citations in OpenAlex.
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- Protective Immunity of a Novel Multi-Epitope Vaccine Encoding OMP31, TF, BLS, SOD, BP26, and L9 Against Brucella spp. InfectionIranian biomedical journal · 2025Article
- Plasmodium vivax antigen candidate prediction improves with the addition of Plasmodium falciparum data.NPJ systems biology and applications · 2024Article
- Positive-unlabeled learning identifies vaccine candidate antigens in the malaria parasite Plasmodium falciparum.NPJ systems biology and applications · 2024Article
- AbImmPred: An immunogenicity prediction method for therapeutic antibodies using AntiBERTy-based sequence features.PloS one · 2024Article
- Personalized cancer vaccine design using AI-powered technologies.Frontiers in immunology · 2024Review
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- Multi-epitope vaccines: a promising strategy against viral diseases in swine.Frontiers in cellular and infection microbiology · 2024Review
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- Moving beyond Titers.Vaccines · 2022Review
- Identification of promising CD8 and CD4 T cell epitopes for peptide vaccine formulation against SARS-CoV-2.Archives of microbiology · 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
- Refining the DC-targeting vaccination for preventing emerging infectious diseases.Frontiers in immunology · 2022Review
- In Silico Identification of Chikungunya Virus B- and T-Cell Epitopes with High Antigenic Potential for Vaccine Development.Viruses · 2021Article
- Allergic Diseases: A Comprehensive Review on Risk Factors, Immunological Mechanisms, Link with COVID-19, Potential Treatments, and Role of Allergen Bioinformatics.International journal of environmental research and public health · 2021Review
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundCD4+ T-cell epitopes play a crucial role in eliciting vigorous protective immune responses during peptide (epitope)-based vaccination. The prediction of these epitopes focuses on the peptide binding process by MHC class II proteins. The ability to account for MHC class II polymorphism is critical for epitope-based vaccine design tools, as different allelic variants can have different peptide repertoires. In addition, the specificity of CD4+ T-cells is often directed to a very limited set of immunodominant peptides in pathogen proteins. The ability to predict what epitopes are most likely to dominate an immune response remains a challenge.
resultsWe developed the computational tool Predivac to predict CD4+ T-cell epitopes. Predivac can make predictions for 95% of all MHC class II protein variants (allotypes), a substantial advance over other available methods. Predivac bases its prediction on the concept of specificity-determining residues. The performance of the method was assessed both for high-affinity HLA class II peptide binding and CD4+ T-cell epitope prediction. In terms of epitope prediction, Predivac outperformed three available pan-specific approaches (delivering the highest specificity). A central finding was the high accuracy delivered by the method in the identification of immunodominant and promiscuous CD4+ T-cell epitopes, which play an essential role in epitope-based vaccine design.
conclusionsThe comprehensive HLA class II allele coverage along with the high specificity in identifying immunodominant CD4+ T-cell epitopes makes Predivac a valuable tool to aid epitope-based vaccine design in the context of a genetically heterogeneous human population.The tool is available at: http://predivac.biosci.uq.edu.au/.
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