TCR Binding Kinetics Measured with MHC Class I Tetramers Reveal a Positive Selecting Peptide with Relatively High Affinity for TCR
Bibliographic record
Abstract
The interaction between TCR and peptide-MHC (pMHC) complexes is crucial for the activation of T cells as well as for positive and negative selection in the thymus. The kinetics and affinity of this interaction and the densities of TCR and pMHC complexes on the cell surface are determining factors for different outcomes during thymic selection. In general, it is thought that agonist pMHC, which cause negative selection, have higher affinities and, in particular, slower off-rates than partial or weak agonists and antagonists, which cause positive selection. In this study, we have used pMHC tetramers to investigate the kinetics of TCR-pMHC interaction for agonist, weak agonist, and antagonist ligands of the anti-lymphocytic choriomeningitis virus P14 TCR. Kinetics determined on the cell surface may be biologically more relevant than methods using soluble proteins. We can distinguish between agonists and weak agonists or antagonists based on the half-life and the avidity of tetramer-TCR interaction. Furthermore, we show that a weak agonist self-peptide that positively selects P14 TCR(+) thymocytes has a tetramer half-life and avidity only slightly weaker than strong agonists. We show that, in fact, it can act as quite a strong agonist, but that its poor ability to stabilize MHC causes it instead to have a weak agonist phenotype.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".