Position‐Scanning Peptide Libraries as Particle Immunogens for Improving CD8<sup>+</sup> T‐Cell Responses
Bibliographic record
Abstract
Abstract Short peptides reflecting major histocompatibility complex (MHC) class I (MHC‐I) epitopes frequently lack sufficient immunogenicity to induce robust antigen (Ag)‐specific CD8+ T cell responses. In the current work, it is demonstrated that position‐scanning peptide libraries themselves can serve as improved immunogens, inducing Ag‐specific CD8+ T cells with greater frequency and function than the wild‐type epitope. The approach involves displaying the entire position‐scanning library onto immunogenic nanoliposomes. Each library contains the MHC‐I epitope with a single randomized position. When a recently identified MHC‐I epitope in the glycoprotein gp70 envelope protein of murine leukemia virus (MuLV) is assessed, only one of the eight positional libraries tested, randomized at amino acid position 5 (Pos5), shows enhanced induction of Ag‐specific CD8+ T cells. A second MHC‐I epitope from gp70 is assessed in the same manner and shows, in contrast, multiple positional libraries (Pos1, Pos3, Pos5, and Pos8) as well as the library mixture give rise to enhanced CD8+ T cell responses. The library mixture Pos1‐3‐5‐8 induces a more diverse epitope‐specific T‐cell repertoire with superior antitumor efficacy compared to an established single mutation mimotope (AH1‐A5). These data show that positional peptide libraries can serve as immunogens for improving CD8+ T‐cell responses against endogenously expressed MHC‐I epitopes.
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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 | 0.001 |
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".