Comparing kinetic proofreading and kinetic segregation for T cell receptor activation
Bibliographic record
Abstract
Abstract The T cell receptor (TCR) is a key component of the adaptive immune system, recognizing foreign antigens and triggering an immune response. Competing models exist to explain the high sensitivity and selectivity of the TCR in discriminating ‘self’ from ‘non-self’ antigens, particularly models using kinetic proofreading (KP), kinetic segregation (KS), and combinations of the two. In this paper, we consider the role and importance of KS in TCR activation, using two models: classic KP (cKP), without KS, where antigen-TCR binding is required for activation, and a combination of KP and KS (KS-KP), where only residence within a close contact is required for activation. Building on previous work, our computational model is the first to permit a head-to-head comparison of these models in silico . 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 favour the KS-KP model over cKP. Our results may be used to motivate and guide future experiments to determine highly accurate computational models for the TCR. Author summary The T cell receptor (TCR) is a master of reliable sensing: it detects faint ‘signals’ (rare ligands derived from foreign proteins) over high ‘noise’ (abundant ligands derived from the body’s own proteins) to set T cells on a course to exterminate pathogens and tumours, a process that is central to our immune response. Despite decades of studying TCR signalling, we still do not know how the TCR can be so exceptionally sensitive and accurate. It is widely believed that kinetic proofreading (KP), in which the TCR binds to an antigen and triggers a series of phosphorylation steps prior to activation, plays an important role. However, recent results suggest that kinetic segregation (KS), in which binding is not required, is also important. These models are mutually exclusive, and yet both appear to explain various aspects of T cell activation. Our work directly addresses this puzzle. We develop a computational modeling framework which can simulate TCR activation by both KP-based and KS-based models, making it possible to compare them in silico for the first time. Using this framework, we find conditions under which the two models provide different responses, and we show that the limited experimental evidence to date is consistent with KS, which should motivate further investigation.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".