Calculations show that T cell antigen discrimination requires multiple localized binding events (35.3)
Bibliographic record
Abstract
Abstract T cells of the adaptive immune system rapidly scan the surface of antigen-presenting-cells (APC) for foreign material, in the form of peptide-MHC, using their T cell receptors (TCR). T cell activation via TCR-pMHC binding is extremely sensitive (a single presented pMHC can elicit a transient response), specific (a single amino acid substitution on a presented peptide can dramatically alter the response), and the decision to respond (e.g. by forming an immune synapse) occurs rapidly. Mathematical modeling based on statistical distributions reveals that pMHC must be repeatedly sampled (by binding TCR) in order for the T cell to make the decision to respond (or not) reliably. We find that TCR clustering and coreceptors act to immobilize pMHC which ensures that multiple binding events arise from a given pMHC. Functional assays of 1G4 T cells binding to a panel of 17 altered-peptide-ligands (NY-ESO) are used to support predictions from the model. We propose that sensitive, specific, and rapid antigen discrimination that is reliable can take place between a single TCR/pMHC.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.005 |
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".