Henderson, James;
Nagano, Yuta;
Milighetti, Martina;
Tiffeau-Mayer, Andreas;
(2024)
Limits on inferring T cell specificity from partial information.
Proceedings of the National Academy of Sciences of the United States of America
, 121
(42)
, Article e2408696121. 10.1073/pnas.2408696121.
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Abstract
A key challenge in molecular biology is to decipher the mapping of protein sequence to function. To perform this mapping requires the identification of sequence features most informative about function. Here, we quantify the amount of information (in bits) that T cell receptor (TCR) sequence features provide about antigen specificity. We identify informative features by their degree of conservation among antigen-specific receptors relative to null expectations. We find that TCR specificity synergistically depends on the hypervariable regions of both receptor chains, with a degree of synergy that strongly depends on the ligand. Using a coincidence-based approach to measuring information enables us to directly bound the accuracy with which TCR specificity can be predicted from partial matches to reference sequences. We anticipate that our statistical framework will be of use for developing machine learning models for TCR specificity prediction and for optimizing TCRs for cell therapies. The proposed coincidence-based information measures might find further applications in bounding the performance of pairwise classifiers in other fields.
| Type: | Article |
|---|---|
| Title: | Limits on inferring T cell specificity from partial information |
| Location: | United States |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1073/pnas.2408696121 |
| Publisher version: | https://doi.org/10.1073/pnas.2408696121 |
| Language: | English |
| Additional information: | Copyright © 2024 the Author(s). Published by PNAS. This open access article is distributed under Creative Commons Attribution License 4.0 (CC BY) (https://creativecommons.org/licenses/by/4.0/). |
| Keywords: | TCR, immune repertoire, information theory, Renyi information, receptor-ligand interaction |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences > Div of Infection and Immunity |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10205430 |
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