A molecular basis underpinning TRBV28+ T-cell receptor recognition of MR1–antigen
Bibliographic record
Abstract
Mucosal-associated invariant T (MAIT) cells express a TRAV1-2 + T cell receptor (TCR) that recognises microbial vitamin B2-derivatives presented by the MHC class I-related molecule, MR1. Most MAIT TCRs incorporate a biased TCR-β repertoire, predominantly TRBV20-1 and TRBV6, but some utilise other TRBV genes, including TRBV28. A second conserved, albeit less frequent TRAV36 + TRBV28 + T cell population exhibits MAIT-like phenotypic features but use a markedly distinct mode of MR1-antigen-recognition compared to MAIT TCR-MR1 binding. Nevertheless, our understanding of how differing TCR gene usage results in altered MR1 binding modes remains incomplete. Here, binding studies demonstrated differential affinities and antigen-specificities between TRBV6 + and TRBV28 + MR1-restricted TCRs. Alanine-scanning mutagenesis on the TRAV36-TRBV28 TCR, revealed a strong dependence on germline-encoded residues within the highly selected CDR3α loop, similar to TRAV1-2- TRBV6 TCRs, and further alanine-scanning mutagenesis experiments demonstrate differential energetic footprints by these TCRs atop MR1. We determined the crystal structure of a MAIT TRAV1-2-TRBV28 + TCR-MR1-5-OP-RU ternary complex. This structure revealed a docking mode conserved amongst other TRAV1-2 + MAIT TCRs, with the TRBV28 -encoded TCR-β chain adopting highly distinct docking modes between the TRAV1-2 + and TRAV36 + TCRs. This indicates that the TCR-α chain dictates the positioning and role of the TCR-β chain. Taken together, these findings provide new molecular insights into MR1-Ag driven selection of paired TCR-α and TCR-β chains.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".