Structure-activity mapping of the peptide- and force-dependent landscape of T-cell activation
Bibliographic record
Abstract
SUMMARY Adaptive immunity relies on T lymphocytes that use αβ T-cell receptors (TCRs) to discriminate amongst peptides presented by MHC molecules (pMHCs). An enhanced ability to screen for pMHCs capable of inducing robust T-cell responses could have broad applications in diagnosing and treating immune diseases. T cell activation relies on biomechanical forces to initiate triggering of the TCR. Yet, most in vitro screening technologies for antigenic peptides test potential pMHCs for T cell binding without force and thus are often not predictive of activating peptides. Here, we present a technology that uses biomechanical force to initiate T cell triggering in high throughput. BATTLES ( B iomechanically- A ssisted T -cell T riggering for L arge-scale E xogenous-pMHC S creening) displays candidate pMHCs on spectrally encoded ‘smart beads’ capable of applying physiological loads to T cells, facilitating exploration of the force- and sequence-dependent landscape of T-cell responses. BATTLES can be used to explore basic T-cell mechanobiology and T cell-based immunotherapies.
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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) | 0.003 | 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".