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Specific attributes of the VL domain influence both the structure and structural variability of CDR-H3 through steric effects

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:
10.3389/fimmu.2023.1223802

Authors

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Institution:
University of Oxford
Role:
Author
ORCID:
0000-0002-4180-4940
More by this author
Institution:
University of Oxford
Role:
Author
ORCID:
0000-0003-1388-2252


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Funder identifier:
10.13039/100010269


Publisher:
Frontiers Media
Journal:
Frontiers in Immunology More from this journal
Volume:
14
Pages:
1223802-1223802
Article number:
1223802
Publication date:
2023-07-26
DOI:
EISSN:
1664-3224
ISSN:
1664-3224


Language:
English
Keywords:
Pubs id:
1510162
Local pid:
pubs:1510162
Source identifiers:
W4385272068
Deposit date:
2026-05-12
ARK identifier:
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