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Drost, F. ; Dorigatti, E. ; Straub, A.* ; Hilgendorf, P.* ; Wagner, K.I.* ; Heyer, K.* ; López Montes, M.* ; Bischl, B.* ; Busch, D.H.* ; Schober, K.* ; Schubert, B.

Predicting T cell receptor functionality against mutant epitopes.

Cell Genom. 4:100634 (2024)
Publ. Version/Full Text DOI PMC
Open Access Gold
Creative Commons Lizenzvertrag
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.
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Publication type Article: Journal article
Document type Scientific Article
Keywords T Cell Receptor ; Tcr-epitope Prediction ; Active Learning ; Cross-reactivity ; Deep Mutational Scan ; Epitope ; Machine Learning ; Mutation; Peptide; Deconvolution; Accuracy; Complex
Language english
Publication Year 2024
HGF-reported in Year 2024
ISSN (print) / ISBN 2666-979X
e-ISSN 2666-979X
Journal Cell Genomics
Quellenangaben Volume: 4, Issue: 9, Pages: , Article Number: 100634 Supplement: ,
Publisher Elsevier
Publishing Place Radarweg 29, 1043 Nx Amsterdam, Netherlands
Reviewing status Peer reviewed
Institute(s) Institute of Computational Biology (ICB)
POF-Topic(s) 30205 - Bioengineering and Digital Health
Research field(s) Enabling and Novel Technologies
PSP Element(s) G-503800-001
Grants Else Kroner-Stiftung
Deutsche Forschungsgemeinschaft (DFG)
BMBF
Joachim Herz Stiftung
Helmholtz Association
DOI 10.1016/j.xgen.2024.100634
WOS ID 001315206700001
Scopus ID 85203131023
PubMed ID 39151427
Erfassungsdatum 2024-10-01
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