Abstract
BackgroundComputational 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.MethodsWe 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.ResultsWe 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.ConclusionsWe 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/.
Highlights
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
Visualizing block conservation analysis results Given the large quantity of outputs from the block conservation analysis and human leukocyte antigen (HLA) binding predictions, we developed visualizations to provide a convenient way of summarizing results
The predicted binding affinities of peptide blocks to the user-specified HLA-I or HLA-II alleles are visualized using a heat map displayed below the conservation graph, where each column in the heat map corresponds to the binding affinity of the given position in the multiple sequence alignment (MSA), and each row corresponds to an HLA allele
Summary
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. The majority of traditional vaccines provide protection through neutralizing antibodies and T cells alone rarely offer protection and prevention of diseases They participate in reduction, control, and clearance of intracellular pathogens and have been linked with protective immunity against a number of viral pathogens [5,6,7,8]. The biggest success of immunological bioinformatics is the development of algorithms for prediction of peptide binding affinity to the human leukocyte antigen (HLA) one of the rate limiting steps in T cell-based immune response [9].
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