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 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 | 0.001 |
| 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".