Abstract
One strategy to generate T-cell responses to tumors is to alter subdominant epitopes through substitution of amino acids that are optimal anchors for specific MHC molecules, termed heteroclitic epitopes. This approach is manually error-prone and time-consuming. In here, we describe a computer-based algorithm (EpitOptimizer) for the streamlined design of heteroclitic epitopes. Analysis of two cancer-related proteins showed that EpitOptimizer-generated peptides have enhanced MHC-I binding compared with their wild-type counterparts; and were able to induce stronger CD8+ T-cell responses against their native epitope. These data demonstrate that this approach can serve as the basis of epitope-engineering against cancer and intracellular pathogens.
Publication types
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Research Support, N.I.H., Extramural
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Research Support, Non-U.S. Gov't
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Validation Study
MeSH terms
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Algorithms
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Amino Acid Sequence
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Animals
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CD8-Positive T-Lymphocytes / immunology
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Cancer Vaccines / genetics
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Cancer Vaccines / immunology*
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Cancer Vaccines / metabolism
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Computational Biology / methods*
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Cross Reactions
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Epitopes / chemistry
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Epitopes / genetics
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Epitopes / immunology*
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Epitopes / metabolism
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Female
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Histocompatibility Antigens Class I / metabolism
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Mice
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Mice, Inbred C57BL
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Mice, Transgenic
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Models, Animal
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Molecular Sequence Data
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Mutagenesis, Site-Directed
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Neoplasms
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Peptides / chemistry
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Peptides / genetics
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Peptides / immunology*
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Peptides / metabolism
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Protein Engineering
Substances
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Cancer Vaccines
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Epitopes
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Histocompatibility Antigens Class I
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Peptides