Design of Cytotoxic T Cell Epitopes by Machine Learning of Human Degrons.
| Authors | |
| Abstract | Antigen processing is critical for therapeutic vaccines to generate epitopes for priming cytotoxic T cell responses against cancer and pathogens, but insufficient processing often limits the quantity of epitopes released. We address this challenge using machine learning to ascribe a proteasomal degradation score to epitope sequences. Epitopes with varying scores were translocated into cells using nontoxic anthrax proteins. Epitopes with a low score show pronounced immunogenicity due to antigen processing, but epitopes with a high score show limited immunogenicity. This work sheds light on the sequence-activity relationships between proteasomal degradation and epitope immunogenicity. We anticipate that future efforts to incorporate proteasomal degradation signals into vaccine designs will lead to enhanced cytotoxic T cell priming by these vaccines in clinical settings. |
| Year of Publication | 2024
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| Journal | ACS central science
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| Volume | 10
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| Issue | 4
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| Pages | 793-802
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| Date Published | 04/2024
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| ISSN | 2374-7943
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| DOI | 10.1021/acscentsci.3c01544
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| PubMed ID | 38680558
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