2<scp>D TCR</scp>–p<scp>MHC</scp>–<scp>CD</scp>8 kinetics determines <scp>T</scp>‐cell responses in a self‐antigen‐specific <scp>TCR</scp> system
Bibliographic record
Abstract
Two-dimensional (2D) kinetic analysis directly measures molecular interactions at cell-cell junctions, thereby incorporating inherent cellular effects. By comparison, three-dimensional (3D) analysis probes the intrinsic physical chemistry of interacting molecules isolated from the cell. To understand how T-cell tumor reactivity relates to 2D and 3D binding parameters and to directly compare them, we performed kinetic analyses of a panel of human T-cell receptors (TCRs) interacting with a melanoma self-antigen peptide (gp100209 -217 ) bound to peptide-major histocompatibility complex in the absence and presence of co-receptor CD8. We found that while 3D parameters are inadequate to predict T-cell function, 2D parameters (that do not correlate with their 3D counterparts) show a far broader dynamic range and significantly improved correlation with T-cell function. Thus, our data support the general notion that 2D parameters of TCR-peptide-major histocompatibility complex-CD8 interactions determine T-cell responsiveness and suggest a potential 2D-based strategy to screen TCRs for tumor immunotherapy.
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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) | 0.002 | 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".