A computational method for identification of vaccine targets from protein regions of conserved human leukocyte antigen binding
Research output: Contribution to journal › Journal article › Research › peer-review
Standard
A computational method for identification of vaccine targets from protein regions of conserved human leukocyte antigen binding. / Olsen, Lars Rønn; Simon, Christian; Kudahl, Ulrich J.; Bagger, Frederik Otzen; Winther, Ole; Reinherz, Ellis L.; Zhang, Guang L.; Brusic, Vladimir.
In: B M C Medical Genomics, Vol. 8, No. Suppl 4, 5, 2015.Research output: Contribution to journal › Journal article › Research › peer-review
Harvard
APA
Vancouver
Author
Bibtex
}
RIS
TY - JOUR
T1 - A computational method for identification of vaccine targets from protein regions of conserved human leukocyte antigen binding
AU - Olsen, Lars Rønn
AU - Simon, Christian
AU - Kudahl, Ulrich J.
AU - Bagger, Frederik Otzen
AU - Winther, Ole
AU - Reinherz, Ellis L.
AU - Zhang, Guang L.
AU - Brusic, Vladimir
PY - 2015
Y1 - 2015
N2 - 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/.
AB - 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/.
KW - bioinformatics
KW - conservation analysis
KW - cross-reactivity
KW - epitope prediction
KW - T cell immunity
U2 - 10.1186/1755-8794-8-S4-S1
DO - 10.1186/1755-8794-8-S4-S1
M3 - Journal article
C2 - 26679766
AN - SCOPUS:84962361636
VL - 8
JO - BMC Medical Genomics
JF - BMC Medical Genomics
SN - 1755-8794
IS - Suppl 4
M1 - 5
ER -
ID: 161945997