Abstract
Background: Computational methods for T cell-based vaccine target discovery focus on selection of highly conserved peptides identified across pathogen variants, followed by prediction of their binding of human leukocyte antigen molecules. However, experimental studies have shown that T cells often target diverse regions in highly variable viral pathogens and this diversity may need to be addressed through redefinition of suitable peptide targets. Methods: We have developed a method for antigen assessment and target selection for polyvalent vaccines, with which we identified immune epitopes from variable regions, where all variants bind HLA. These regions, although variable, can thus be considered stable in terms of HLA binding and represent valuable vaccine targets. Results: We applied this method to predict CD8+ T-cell targets in influenza A H7N9 hemagglutinin and significantly increased the number of potential vaccine targets compared to the number of targets discovered using the traditional approach where low-frequency peptides are excluded. Conclusions: We developed a webserver with an intuitive visualization scheme for summarizing the T cell-based antigenic potential of any given protein or proteome using human leukocyte antigen binding predictions and made a web-accessible software implementation freely available at http://met-hilab.cbs.dtu.dk/blockcons/.
Original language | English |
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Journal | BMC Medical Genomics |
Volume | 8 |
Issue number | Suppl. 4 |
Pages (from-to) | 566 |
Number of pages | 1 |
ISSN | 1755-8794 |
DOIs | |
Publication status | Published - 2015 |
Event | Joint 26th Genome Informatics Workshop and 14th International Conference on Bioinformatics: Medical Genomics - Tokyo, Japan Duration: 9 Sept 2015 → 11 Sept 2015 Conference number: 26 |
Conference
Conference | Joint 26th Genome Informatics Workshop and 14th International Conference on Bioinformatics: Medical Genomics |
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Number | 26 |
Country/Territory | Japan |
City | Tokyo |
Period | 09/09/2015 → 11/09/2015 |
Bibliographical note
© 2015 Olsen et al. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http:// creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.Keywords
- Bioinformatics
- Conservation analysis
- Cross-reactivity
- Epitope prediction
- T cell immunity