Proteasome isoforms exhibit only quantitative differences in cleavage and epitope generation
Bibliographic record
Abstract
Immunoproteasomes are considered to be optimised to process Ags and to alter the peptide repertoire by generating a qualitatively different set of MHC class I epitopes. Whether the immunoproteasome at the biochemical level, influence the quality rather than the quantity of the immuno-genic peptide pool is still unclear. Here, we quantified the cleavage-site usage by human standard- and immunoproteasomes, and proteasomes from immuno-subunit-deficient mice, as well as the peptides generated from model polypeptides. We show in this study that the different proteasome isoforms can exert significant quantitative differences in the cleavage-site usage and MHC class I restricted epitope production. However, independent of the proteasome isoform and substrates studied, no evidence was obtained for the abolishment of the specific cleavage-site usage, or for differences in the quality of the peptides generated. Thus, we conclude that the observed differences in MHC class I restricted Ag presentation between standard- and immunoproteasomes are due to quantitative differences in the proteasome-generated antigenic peptides.
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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 | 0.001 | 0.001 |
| 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 | 0.001 | 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".