Molecular mechanism of phosphopeptide neoantigen immunogenicity
Bibliographic record
Abstract
Abstract Altered protein phosphorylation in cancer cells often leads to surface presentation of phosphopeptide neoantigens. However, their role in cancer immunogenicity remains unclear. Here we describe a mechanism by which an HLA-B*0702-specific acute myeloid leukemia phosphoneoantigen pMLL747-755(EPR(pS)PSHSM) is recognized by cognate TCR27, which is a candidate for immunotherapy of AML. We show that the replacement of phosphoserine P4with serine or phosphomimetics does not affect the pMHC conformation or peptide-MHC affinity but abrogates the TCR27-dependent T cell activation and weakens binding between TCR27 and pMHC. We determined the crystal structures for TCR27 and cognate pMHC, mapped the pMHC-TCR interface by TROSY-NMR, generated a ternary pMHC-TCR complex using information-driven protein docking, and identified key polar interactions between phosphate group at P4and TCR27 that are crucial for ternary complex stability and TCR27 specificity. These data will support development of cancer immunotherapy through target expansion and TCR optimization. *The authors would like to note that Yury Patskovsky and Aswin Natarajan contributed equally.
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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 | 0.001 |
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".