Predicting T cell receptor functionality against mutant epitopes.

Autor:	Drost F; Institute of Computational Biology, Helmholtz Center Munich, 85764 Neuherberg, Germany; School of Life Sciences Weihenstephan, Technical University of Munich, 85354 Freising, Germany., Dorigatti E; Institute of Computational Biology, Helmholtz Center Munich, 85764 Neuherberg, Germany; Department of Statistics, Ludwig Maximilian Universität, 80539 Munich, Germany; Munich Center for Machine Learning (MCML), Ludwig Maximilian Universität, 80538 Munich, Germany., Straub A; Institute for Medical Microbiology, Immunology, and Hygiene, Technical University of Munich, 81675 Munich, Germany., Hilgendorf P; Institute for Medical Microbiology, Immunology, and Hygiene, Technical University of Munich, 81675 Munich, Germany; Mikrobiologisches Institut-Klinische Mikrobiologie, Immunologie, und Hygiene, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, 91054 Erlangen, Germany., Wagner KI; Institute for Medical Microbiology, Immunology, and Hygiene, Technical University of Munich, 81675 Munich, Germany., Heyer K; Institute for Medical Microbiology, Immunology, and Hygiene, Technical University of Munich, 81675 Munich, Germany., López Montes M; Institute for Medical Microbiology, Immunology, and Hygiene, Technical University of Munich, 81675 Munich, Germany., Bischl B; Department of Statistics, Ludwig Maximilian Universität, 80539 Munich, Germany; Munich Center for Machine Learning (MCML), Ludwig Maximilian Universität, 80538 Munich, Germany., Busch DH; Institute for Medical Microbiology, Immunology, and Hygiene, Technical University of Munich, 81675 Munich, Germany; German Center for Infection Research, Deutschen Zentrum für Infektionsforschung (DZIF), Partner Site Munich, 81675 Munich, Germany., Schober K; Institute for Medical Microbiology, Immunology, and Hygiene, Technical University of Munich, 81675 Munich, Germany; Mikrobiologisches Institut-Klinische Mikrobiologie, Immunologie, und Hygiene, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, 91054 Erlangen, Germany; Medical Immunology Campus Erlangen, Friedrich-Alexander-Universität (FAU) Erlangen-Nürnberg, 91054 Erlangen, Germany., Schubert B; Institute of Computational Biology, Helmholtz Center Munich, 85764 Neuherberg, Germany; School of Computation, Information, and Technology, Technical University of Munich, 85748 Garching bei München, Germany. Electronic address: benjamin.schubert@helmholtz-munich.de.
Jazyk:	angličtina
Zdroj:	Cell genomics [Cell Genom] 2024 Sep 11; Vol. 4 (9), pp. 100634. Date of Electronic Publication: 2024 Aug 15.
DOI:	10.1016/j.xgen.2024.100634
Abstrakt:	Cancer cells and pathogens can evade T cell receptors (TCRs) via mutations in immunogenic epitopes. TCR cross-reactivity (i.e., recognition of multiple epitopes with sequence similarities) can counteract such escape but may cause severe side effects in cell-based immunotherapies through targeting self-antigens. To predict the effect of epitope point mutations on T cell functionality, we here present the random forest-based model Predicting T Cell Epitope-Specific Activation against Mutant Versions (P-TEAM). P-TEAM was trained and tested on three datasets with TCR responses to single-amino-acid mutations of the model epitope SIINFEKL, the tumor neo-epitope VPSVWRSSL, and the human cytomegalovirus antigen NLVPMVATV, totaling 9,690 unique TCR-epitope interactions. P-TEAM was able to accurately classify T cell reactivities and quantitatively predict T cell functionalities for unobserved single-point mutations and unseen TCRs. Overall, P-TEAM provides an effective computational tool to study T cell responses against mutated epitopes. Competing Interests: Declaration of interests The authors declare no competing interests. (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)
Databáze:	MEDLINE
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