Comparing kinetic proofreading and kinetic segregation for T cell receptor activation
Bibliographic record
Abstract
The T cell receptor (TCR) is a key component of the adaptive immune system, recognizing foreign antigens (ligands) and triggering an immune response. To explain the high sensitivity and selectivity of the TCR in discriminating “self” from “non-self” ligands, most models evoke kinetic proofreading (KP) schemes, however it is unclear how competing models used for TCR triggering, such as the kinetic segregation (KS) model, influence KP performance. In this paper, we consider two different TCR triggering models and their influence on subsequent KP-based ligand discrimination by the TCR: a classic conformational change model (CC-KP), where ligand-TCR binding is strictly required for activation, and the kinetic segregation model (KS-KP), where only residence of the TCR within a close contact devoid of kinases is required for its activation. Building on previous work, our computational model permits a head-to-head comparison of these models . While we find that both models can be used to explain the probability of TCR activation across much of the parameter space, we find biologically important regions in the parameter space where significant differences in performance can be expected. Furthermore, we show that the available experimental evidence may favor the KS-KP model over CC-KP. Our results may be used to motivate and guide future experiments to determine accurate mathematical models of TCR function.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".