The presence of prolines in the flanking region of an immunodominant HIV‐2 gag epitope influences the quality and quantity of the epitope generated
Bibliographic record
Abstract
Both the recognition of HIV-infected cells and the immunogenicity of candidate CTL vaccines depend on the presentation of a peptide epitope at the cell surface, which in turn depends on intracellular antigen processing. Differential antigen processing maybe responsible for the differences in both the quality and the quantity of epitopes produced, influencing the immunodominance hierarchy of viral epitopes. Previously, we showed that the magnitude of the HIV-2 gag-specific T-cell response is inversely correlated with plasma viral load, particularly when responses are directed against an epitope, 165 DRFYKSLRA173 , within the highly conserved Major Homology Region of gag-p26. We also showed that the presence of three proline residues, at positions 119, 159 and 178 of gag-p26, was significantly correlated with low viral load. Since this proline motif was also associated with stronger gag-specific CTL responses, we investigated the impact of these prolines on proteasomal processing of the protective 165 DRFYKSLRA173 epitope. Our data demonstrate that the 165 DRFYKSLRA173 epitope is most efficiently processed from precursors that contain two flanking proline residues, found naturally in low viral-load patients. Superior antigen processing and enhanced presentation may account for the link between infection with HIV-2 encoding the "PPP-gag" sequence and both strong gag-specific CTL responses as well as lower viral load.
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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".