Structural basis for self-discrimination by neoantigen-specific TCRs
Bibliographic record
Abstract
Abstract T cell receptors (TCR) are pivotal in mediating tumour cell cytolysis via recognition of mutation-derived tumour neoantigens (neoAgs) presented by major histocompatibility class-I (MHC-I). Understanding the factors governing the emergence of neoAg from somatic mutations is a major focus of current research. However, the structural and cellular determinants controlling TCR recognition of neoAgs remain poorly understood. This study describes the multi-level analysis of a model neoAg from the B16F10 murine melanoma, H2-Db/Hsf2 p.K72N68-76, as well as its cognate TCR 47BE7. Through cellular, molecular and structural studies we demonstrate that the p.K72N mutation enhances H2-Db binding, thereby improving cell surface presentation and stabilizing the TCR 47BE7 epitope. Furthermore, TCR 47BE7 exhibited high functional avidity and selectivity, attributable to a broad, stringent, binding interface enabling recognition of native B16F10 despite low antigen density. Our findings provide insight into the generation of anchor-residue modified neoAg, and emphasize the value of molecular and structural investigations of neoAg in diverse MHC-I contexts for advancing the understanding of neoAg immunogenicity.
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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) | 0.001 | 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".