Comparison of experimental fine‐mapping to in silico prediction results of HIV‐1 epitopes reveals ongoing need for mapping experiments
Summary Methods for identifying physiologically relevant CD8 T‐cell epitopes are critically important not only for the development of T‐cell‐based vaccines but also for understanding host–pathogen interactions. As experimentally mapping an optimal CD8 T‐cell epitope is a tedious procedure, many bioi...
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Published in | Immunology Vol. 143; no. 2; pp. 193 - 201 |
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Main Authors | , , , , , , |
Format | Journal Article |
Language | English |
Published |
England
Wiley Subscription Services, Inc
01.10.2014
Blackwell Science Inc |
Subjects | |
Online Access | Get full text |
ISSN | 0019-2805 1365-2567 1365-2567 |
DOI | 10.1111/imm.12301 |
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Abstract | Summary
Methods for identifying physiologically relevant CD8 T‐cell epitopes are critically important not only for the development of T‐cell‐based vaccines but also for understanding host–pathogen interactions. As experimentally mapping an optimal CD8 T‐cell epitope is a tedious procedure, many bioinformatic tools have been developed that predict which peptides bind to a given MHC molecule. We assessed the ability of the CD8 T‐cell epitope prediction tools syfpeithi, ctlpred and iedb to foretell nine experimentally mapped optimal HIV‐specific epitopes. Randomly – for any of the subjects' HLA type and with any matching score – the optimal epitope was predicted in seven of nine epitopes using syfpeithi, in three of nine epitopes using ctlpred and in all nine of nine epitopes using iedb. The optimal epitope within the three highest ranks was given in four of nine epitopes applying syfpeithi, in two of nine epitopes applying ctlpred and in seven of nine epitopes applying iedb when screening for all of the subjects' HLA types. Knowing the HLA restriction of the peptide of interest improved the ranking of the optimal epitope within the predicted results. Epitopes restricted by common HLA alleles were more likely to be predicted than those restricted by uncommon HLA alleles. Epitopes with aberrant lengths compared with the usual HLA‐class I nonamers were most likely not predicted. Application of epitope prediction tools together with literature searches for already described optimal epitopes narrows down the possibilities of optimal epitopes within a screening peptide of interest. However, in our opinion, the actual fine‐mapping of a CD8 T‐cell epitope cannot yet be replaced. |
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AbstractList | Summary Methods for identifying physiologically relevant CD8 T-cell epitopes are critically important not only for the development of T-cell-based vaccines but also for understanding host-pathogen interactions. As experimentally mapping an optimal CD8 T-cell epitope is a tedious procedure, many bioinformatic tools have been developed that predict which peptides bind to a given MHC molecule. We assessed the ability of the CD8 T-cell epitope prediction tools syfpeithi,ctlpred and iedb to foretell nine experimentally mapped optimal HIV-specific epitopes. Randomly - for any of the subjects' HLA type and with any matching score - the optimal epitope was predicted in seven of nine epitopes using syfpeithi, in three of nine epitopes using ctlpred and in all nine of nine epitopes using iedb. The optimal epitope within the three highest ranks was given in four of nine epitopes applying syfpeithi, in two of nine epitopes applying ctlpred and in seven of nine epitopes applying iedb when screening for all of the subjects' HLA types. Knowing the HLA restriction of the peptide of interest improved the ranking of the optimal epitope within the predicted results. Epitopes restricted by common HLA alleles were more likely to be predicted than those restricted by uncommon HLA alleles. Epitopes with aberrant lengths compared with the usual HLA-class I nonamers were most likely not predicted. Application of epitope prediction tools together with literature searches for already described optimal epitopes narrows down the possibilities of optimal epitopes within a screening peptide of interest. However, in our opinion, the actual fine-mapping of a CD8 T-cell epitope cannot yet be replaced. [PUBLICATION ABSTRACT] Methods for identifying physiologically relevant CD8 T-cell epitopes are critically important not only for the development of T-cell-based vaccines but also for understanding host-pathogen interactions. As experimentally mapping an optimal CD8 T-cell epitope is a tedious procedure, many bioinformatic tools have been developed that predict which peptides bind to a given MHC molecule. We assessed the ability of the CD8 T-cell epitope prediction tools syfpeithi, ctlpred and iedb to foretell nine experimentally mapped optimal HIV-specific epitopes. Randomly - for any of the subjects' HLA type and with any matching score - the optimal epitope was predicted in seven of nine epitopes using syfpeithi, in three of nine epitopes using ctlpred and in all nine of nine epitopes using iedb. The optimal epitope within the three highest ranks was given in four of nine epitopes applying syfpeithi, in two of nine epitopes applying ctlpred and in seven of nine epitopes applying iedb when screening for all of the subjects' HLA types. Knowing the HLA restriction of the peptide of interest improved the ranking of the optimal epitope within the predicted results. Epitopes restricted by common HLA alleles were more likely to be predicted than those restricted by uncommon HLA alleles. Epitopes with aberrant lengths compared with the usual HLA-class I nonamers were most likely not predicted. Application of epitope prediction tools together with literature searches for already described optimal epitopes narrows down the possibilities of optimal epitopes within a screening peptide of interest. However, in our opinion, the actual fine-mapping of a CD8 T-cell epitope cannot yet be replaced.Methods for identifying physiologically relevant CD8 T-cell epitopes are critically important not only for the development of T-cell-based vaccines but also for understanding host-pathogen interactions. As experimentally mapping an optimal CD8 T-cell epitope is a tedious procedure, many bioinformatic tools have been developed that predict which peptides bind to a given MHC molecule. We assessed the ability of the CD8 T-cell epitope prediction tools syfpeithi, ctlpred and iedb to foretell nine experimentally mapped optimal HIV-specific epitopes. Randomly - for any of the subjects' HLA type and with any matching score - the optimal epitope was predicted in seven of nine epitopes using syfpeithi, in three of nine epitopes using ctlpred and in all nine of nine epitopes using iedb. The optimal epitope within the three highest ranks was given in four of nine epitopes applying syfpeithi, in two of nine epitopes applying ctlpred and in seven of nine epitopes applying iedb when screening for all of the subjects' HLA types. Knowing the HLA restriction of the peptide of interest improved the ranking of the optimal epitope within the predicted results. Epitopes restricted by common HLA alleles were more likely to be predicted than those restricted by uncommon HLA alleles. Epitopes with aberrant lengths compared with the usual HLA-class I nonamers were most likely not predicted. Application of epitope prediction tools together with literature searches for already described optimal epitopes narrows down the possibilities of optimal epitopes within a screening peptide of interest. However, in our opinion, the actual fine-mapping of a CD8 T-cell epitope cannot yet be replaced. Methods for identifying physiologically relevant CD 8 T‐cell epitopes are critically important not only for the development of T‐cell‐based vaccines but also for understanding host–pathogen interactions. As experimentally mapping an optimal CD 8 T‐cell epitope is a tedious procedure, many bioinformatic tools have been developed that predict which peptides bind to a given MHC molecule. We assessed the ability of the CD 8 T‐cell epitope prediction tools syfpeithi , ctlpred and iedb to foretell nine experimentally mapped optimal HIV ‐specific epitopes. Randomly – for any of the subjects' HLA type and with any matching score – the optimal epitope was predicted in seven of nine epitopes using syfpeithi , in three of nine epitopes using ctlpred and in all nine of nine epitopes using iedb . The optimal epitope within the three highest ranks was given in four of nine epitopes applying syfpeithi , in two of nine epitopes applying ctlpred and in seven of nine epitopes applying iedb when screening for all of the subjects' HLA types. Knowing the HLA restriction of the peptide of interest improved the ranking of the optimal epitope within the predicted results. Epitopes restricted by common HLA alleles were more likely to be predicted than those restricted by uncommon HLA alleles. Epitopes with aberrant lengths compared with the usual HLA ‐class I nonamers were most likely not predicted. Application of epitope prediction tools together with literature searches for already described optimal epitopes narrows down the possibilities of optimal epitopes within a screening peptide of interest. However, in our opinion, the actual fine‐mapping of a CD 8 T‐cell epitope cannot yet be replaced. Summary Methods for identifying physiologically relevant CD8 T‐cell epitopes are critically important not only for the development of T‐cell‐based vaccines but also for understanding host–pathogen interactions. As experimentally mapping an optimal CD8 T‐cell epitope is a tedious procedure, many bioinformatic tools have been developed that predict which peptides bind to a given MHC molecule. We assessed the ability of the CD8 T‐cell epitope prediction tools syfpeithi, ctlpred and iedb to foretell nine experimentally mapped optimal HIV‐specific epitopes. Randomly – for any of the subjects' HLA type and with any matching score – the optimal epitope was predicted in seven of nine epitopes using syfpeithi, in three of nine epitopes using ctlpred and in all nine of nine epitopes using iedb. The optimal epitope within the three highest ranks was given in four of nine epitopes applying syfpeithi, in two of nine epitopes applying ctlpred and in seven of nine epitopes applying iedb when screening for all of the subjects' HLA types. Knowing the HLA restriction of the peptide of interest improved the ranking of the optimal epitope within the predicted results. Epitopes restricted by common HLA alleles were more likely to be predicted than those restricted by uncommon HLA alleles. Epitopes with aberrant lengths compared with the usual HLA‐class I nonamers were most likely not predicted. Application of epitope prediction tools together with literature searches for already described optimal epitopes narrows down the possibilities of optimal epitopes within a screening peptide of interest. However, in our opinion, the actual fine‐mapping of a CD8 T‐cell epitope cannot yet be replaced. Methods for identifying physiologically relevant CD8 T-cell epitopes are critically important not only for the development of T-cell-based vaccines but also for understanding host-pathogen interactions. As experimentally mapping an optimal CD8 T-cell epitope is a tedious procedure, many bioinformatic tools have been developed that predict which peptides bind to a given MHC molecule. We assessed the ability of the CD8 T-cell epitope prediction tools syfpeithi, ctlpred and iedb to foretell nine experimentally mapped optimal HIV-specific epitopes. Randomly - for any of the subjects' HLA type and with any matching score - the optimal epitope was predicted in seven of nine epitopes using syfpeithi, in three of nine epitopes using ctlpred and in all nine of nine epitopes using iedb. The optimal epitope within the three highest ranks was given in four of nine epitopes applying syfpeithi, in two of nine epitopes applying ctlpred and in seven of nine epitopes applying iedb when screening for all of the subjects' HLA types. Knowing the HLA restriction of the peptide of interest improved the ranking of the optimal epitope within the predicted results. Epitopes restricted by common HLA alleles were more likely to be predicted than those restricted by uncommon HLA alleles. Epitopes with aberrant lengths compared with the usual HLA-class I nonamers were most likely not predicted. Application of epitope prediction tools together with literature searches for already described optimal epitopes narrows down the possibilities of optimal epitopes within a screening peptide of interest. However, in our opinion, the actual fine-mapping of a CD8 T-cell epitope cannot yet be replaced. Methods for identifying physiologically relevant CD8 T-cell epitopes are critically important not only for the development of T-cell-based vaccines but also for understanding host–pathogen interactions. As experimentally mapping an optimal CD8 T-cell epitope is a tedious procedure, many bioinformatic tools have been developed that predict which peptides bind to a given MHC molecule. We assessed the ability of the CD8 T-cell epitope prediction tools syfpeithi , ctlpred and iedb to foretell nine experimentally mapped optimal HIV-specific epitopes. Randomly – for any of the subjects' HLA type and with any matching score – the optimal epitope was predicted in seven of nine epitopes using syfpeithi , in three of nine epitopes using ctlpred and in all nine of nine epitopes using iedb . The optimal epitope within the three highest ranks was given in four of nine epitopes applying syfpeithi , in two of nine epitopes applying ctlpred and in seven of nine epitopes applying iedb when screening for all of the subjects' HLA types. Knowing the HLA restriction of the peptide of interest improved the ranking of the optimal epitope within the predicted results. Epitopes restricted by common HLA alleles were more likely to be predicted than those restricted by uncommon HLA alleles. Epitopes with aberrant lengths compared with the usual HLA-class I nonamers were most likely not predicted. Application of epitope prediction tools together with literature searches for already described optimal epitopes narrows down the possibilities of optimal epitopes within a screening peptide of interest. However, in our opinion, the actual fine-mapping of a CD8 T-cell epitope cannot yet be replaced. |
Author | Bogner, Johannes R. Stirner, Renate Roider, Julia Vollbrecht, Thomas Meissner, Tim Kraut, Franziska Draenert, Rika |
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BackLink | https://www.ncbi.nlm.nih.gov/pubmed/24724694$$D View this record in MEDLINE/PubMed |
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CitedBy_id | crossref_primary_10_3390_vaccines12080836 crossref_primary_10_1016_j_vaccine_2017_08_070 crossref_primary_10_3390_microorganisms7080226 crossref_primary_10_3390_vaccines8020260 crossref_primary_10_1016_j_gde_2014_12_003 crossref_primary_10_1186_s12879_020_4876_4 crossref_primary_10_1099_vir_0_000156 crossref_primary_10_2174_1871526518666180709121653 crossref_primary_10_1016_j_addr_2021_01_007 crossref_primary_10_1371_journal_pone_0166372 |
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Keywords | CD8 T cell MHC class I epitope prediction optimal epitope epitope mapping |
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Methods for identifying physiologically relevant CD8 T‐cell epitopes are critically important not only for the development of T‐cell‐based vaccines but... Methods for identifying physiologically relevant CD 8 T‐cell epitopes are critically important not only for the development of T‐cell‐based vaccines but also... Methods for identifying physiologically relevant CD8 T-cell epitopes are critically important not only for the development of T-cell-based vaccines but also... Summary Methods for identifying physiologically relevant CD8 T-cell epitopes are critically important not only for the development of T-cell-based vaccines but... |
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SubjectTerms | AIDS Vaccines - immunology CD4-Positive T-Lymphocytes - immunology CD4-Positive T-Lymphocytes - virology CD8 T cell Cell Line Computational Biology Computer Simulation epitope mapping Epitope Mapping - methods epitope prediction Epitopes, T-Lymphocyte Histocompatibility Antigens Class I - immunology HIV-1 - immunology HIV-1 - pathogenicity Humans Immunodominant Epitopes MHC class I optimal epitope Original Peptides Reproducibility of Results Software |
Title | Comparison of experimental fine‐mapping to in silico prediction results of HIV‐1 epitopes reveals ongoing need for mapping experiments |
URI | https://onlinelibrary.wiley.com/doi/abs/10.1111%2Fimm.12301 https://www.ncbi.nlm.nih.gov/pubmed/24724694 https://www.proquest.com/docview/1560548250 https://www.proquest.com/docview/1561035631 https://pubmed.ncbi.nlm.nih.gov/PMC4172136 |
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