Modification of the HLA-A*24:02 Peptide Binding Pocket Enhances Cognate Peptide-Binding Capacity and Antigen-Specific T Cell Activation
Bibliographic record
Abstract
The immunogenicity of a T cell Ag is correlated with the ability of its antigenic epitope to bind HLA and be stably presented to T cells. This presents a challenge for the development of effective cancer immunotherapies, as many self-derived tumor-associated epitopes elicit weak T cell responses, in part due to weak binding affinity to HLA. Traditional methods to increase peptide-HLA binding affinity involve modifying the peptide to reflect HLA allele binding preferences. Using a different approach, we sought to analyze whether the immunogenicity of wild-type peptides could be altered through modification of the HLA binding pocket. After analyzing HLA class I peptide binding pocket alignments, we identified an alanine 81 to leucine (A81L) modification within the F binding pocket of HLA-A*24:02 that was found to heighten the ability of artificial APCs to retain and present HLA-A*24:02-restricted peptides, resulting in increased T cell responses while retaining Ag specificity. This modification led to increased peptide exchange efficiencies for enhanced detection of low-avidity T cells and, when expressed on artificial APCs, resulted in greater expansion of Ag-specific T cells from melanoma-derived tumor-infiltrating lymphocytes. Our study provides an example of how modifications to the HLA binding pocket can enhance wild-type cognate peptide presentation to heighten T cell activation.
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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".