Structural and Functional Correlates of Enhanced Antiviral Immunity Generated by Heteroclitic CD8 T Cell Epitopes
Bibliographic record
Abstract
Peptides that bind poorly to MHC class I molecules often elicit low-functional avidity T cell responses. Peptide modification by altering the anchor residue facilitates increased binding affinity and may elicit T cells with increased functional avidity toward the native epitope ("heteroclitic"). This augmented MHC binding is likely to increase the half-life and surface density of the heteroclitic complex, but precisely how this enhanced T cell response occurs in vivo is not known. Furthermore, the ideal heteroclitic epitope will elicit T cell responses that completely cross-react with the native epitope, maximizing protection and minimizing undesirable off-target effects. Such epitopes have been difficult to identify. In this study, using mice infected with a murine coronavirus that encodes epitopes that elicit high (S510, CSLWNGPHL)- and low (S598, RCQIFANI)-functional avidity responses, we show that increased expression of peptide S598 but not S510 generated T cells with enhanced functional avidity. Thus, immune responses can be augmented toward T cell epitopes with low functional avidity by increasing Ag density. We also identified a heteroclitic epitope (RCVIFANI) that elicited a T cell response with nearly complete cross-reactivity with native epitope and demonstrated increased MHC/peptide abundance compared with native S598. Structural and thermal melt analyses indicated that the Q600V substitution enhanced stability of the peptide/MHC complex without greatly altering the antigenic surface, resulting in highly cross-reactive T cell responses. Our data highlight that increased peptide/MHC complex display contributes to heteroclitic epitope efficacy and describe parameters for maximizing immune responses that cross-react with the native epitope.
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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".