Altered Peptide Ligands Revisited: Vaccine Design through Chemically Modified HLA-A2–Restricted T Cell Epitopes
Bibliographic record
Abstract
Virus or tumor Ag-derived peptides that are displayed by MHC class I molecules are attractive starting points for vaccine development because they induce strong protective and therapeutic cytotoxic T cell responses. In thus study, we show that the MHC binding and consequent T cell reactivity against several HLA-A*02 restricted epitopes can be further improved through the incorporation of nonproteogenic amino acids at primary and secondary anchor positions. We screened more than 90 nonproteogenic, synthetic amino acids through a range of epitopes and tested more than 3000 chemically enhanced altered peptide ligands (CPLs) for binding affinity to HLA-A*0201. With this approach, we designed CPLs of viral epitopes, of melanoma-associated Ags, and of the minor histocompatibility Ag UTA2-1, which is currently being evaluated for its antileukemic activity in clinical dendritic cell vaccination trials. The crystal structure of one of the CPLs in complex with HLA-A*0201 revealed the molecular interactions likely responsible for improved binding. The best CPLs displayed enhanced affinity for MHC, increasing MHC stability and prolonging recognition by Ag-specific T cells and, most importantly, they induced accelerated expansion of antitumor T cell frequencies in vitro and in vivo as compared with the native epitope. Eventually, we were able to construct a toolbox of preferred nonproteogenic residues with which practically any given HLA-A*02 restricted epitope can be readily optimized. These CPLs could improve the therapeutic outcome of vaccination strategies or can be used for ex vivo enrichment and faster expansion of Ag-specific T cells for transfer into patients.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".