Limits on inferring T cell specificity from partial information.

Autor:	Henderson J; Division of Infection and Immunity, University College London, London WC1E 6BT, United Kingdom.; Institute for the Physics of Living Systems, University College London, London WC1E 6BT, United Kingdom., Nagano Y; Division of Infection and Immunity, University College London, London WC1E 6BT, United Kingdom.; Division of Medicine, University College London, London WC1E 6BT, United Kingdom., Milighetti M; Division of Infection and Immunity, University College London, London WC1E 6BT, United Kingdom.; Cancer Institute, University College London, London WC1E 6DD, United Kingdom., Tiffeau-Mayer A; Division of Infection and Immunity, University College London, London WC1E 6BT, United Kingdom.; Institute for the Physics of Living Systems, University College London, London WC1E 6BT, United Kingdom.
Jazyk:	angličtina
Zdroj:	Proceedings of the National Academy of Sciences of the United States of America [Proc Natl Acad Sci U S A] 2024 Oct 15; Vol. 121 (42), pp. e2408696121. Date of Electronic Publication: 2024 Oct 07.
DOI:	10.1073/pnas.2408696121
Abstrakt:	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. Competing Interests: Competing interests statement:The authors declare no competing interest.
Databáze:	MEDLINE
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