Cooperative ectodomain interaction among TCRαβ, CD3δɛ and CD3γɛ
Bibliographic record
Abstract
Abstract The T-cell receptor (TCR) complex comprises of the ligand-binding subunit TCRαβ, and the signaling subunits CD3δɛ, CD3γɛ and CD3ζζ, with the Cα/Cβ in proximity to both CD3δɛ and CD3γɛ extracellularly. Direct measurements of the ectodomain interactions had not been successful, although they are believed to be important for TCR stability and functionality. Mechanical force has been shown to modulate TCR–ligand interactions. The TCR mechanosensor hypothesis predicts that force-encoded information may transmit from pMHC to CD3 via TCR-CD3 interaction. Evaluating ectodomain interactions among TCRαβ, CD3δɛ and CD3γɛ can help elucidate the TCR triggering mechanism and further guide the design of TCR-based immunotherapy. Using two mechanical based assays, we were able to measure the weak two-dimensional (2D) affinities among ectodomains of human TCRαβ (2B4-LC13), and human CD3δɛ or CD3γɛ and showed catch bond formation where lifetimes of TCRαβ–CD3δɛ and TCRαβ–CD3γɛ bonds are prolonged by forces <15 pN. Remarkably, CD3δɛ and CD3γɛ bind TCRαβ cooperatively, forming more bonds that last longer when both CD3s interact with TCRαβ as a whole than the sum of either CD3 interacting with TCRαβ individually. Interestingly, these measurements are comparable to 2D affinity and force-dependent bond lifetime of the 2B4 TCR interaction with its cognate ligand K5:I-Ek, supporting their relevance to TCR function. Using molecular dynamics simulations based on a published Cryo-EM structure, we identified the formation of long-lasting CD3δɛ–TCRαβ–CD3γɛ trimolecular bonds as the structural mechanisms of the cooperativity. Our work helps explain TCR function under force and suggests strategies for engineering of TCR for immunotherapy applications.
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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) | 0.001 | 0.000 |
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".