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T-cell receptor structures and predictive models reveal comparable alpha and beta chain structural diversity despite differing genetic complexity

Abstract:
T-cell receptor (TCR) structures are currently under-utilised in early-stage drug discovery and repertoire-scale informatics. Here, we leverage a large dataset of solved TCR structures from Immunocore to evaluate the current state-of-the-art for TCR structure prediction, and identify which regions of the TCR remain challenging to model. Through clustering analyses and the training of a TCR-specific model capable of large-scale structure prediction, we find that the alpha chain VJ-recombined loop (CDR3α) is as structurally diverse and correspondingly difficult to predict as the beta chain VDJ-recombined loop (CDR3β). This differentiates TCR variable domain loops from the genetically analogous antibody loops and supports the conjecture that both TCR alpha and beta chains are deterministic of antigen specificity. We hypothesise that the larger number of alpha chain joining genes compared to beta chain joining genes compensates for the lack of a diversity gene segment. We also provide over 1.5M predicted TCR structures to enable repertoire structural analysis and elucidate strategies towards improving the accuracy of future TCR structure predictors. Our observations reinforce the importance of paired TCR sequence information and capture the current state-of-the-art for TCR structure prediction, while our model and 1.5M structure predictions enable the use of structural TCR information at an unprecedented scale.
Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1038/s42003-025-07708-6

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Institution:
University of Oxford
Division:
MPLS
Department:
Statistics
Oxford college:
Linacre College
Role:
Author
ORCID:
0009-0002-7460-8572
More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Statistics
Role:
Author
ORCID:
0000-0001-8712-533X
More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Statistics
Role:
Author
ORCID:
0000-0002-4180-4940
More by this author
Role:
Author
ORCID:
0000-0002-9645-7958
More by this author
Role:
Author
ORCID:
0000-0002-6029-8668


Publisher:
Springer Nature
Journal:
Communications Biology More from this journal
Volume:
8
Issue:
1
Article number:
362
Publication date:
2025-03-04
Acceptance date:
2025-02-09
DOI:
EISSN:
2399-3642
ISSN:
2399-3642


Language:
English
Pubs id:
2093481
Local pid:
pubs:2093481
Deposit date:
2025-03-05
ARK identifier:

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