Design of Cytotoxic T Cell Epitopes by Machine Learning of Human Degrons

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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.

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Truex, N. L., Mohapatra, S., Melo, M., Rodriguez, J., Li, N., Abraham, W., … Pentelute, B. L. (2023). Design of Cytotoxic T Cell Epitopes by Machine Learning of Human Degrons. ACS Central Science. https://doi.org/10.1021/acscentsci.3c01544

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