Characterization of Proteasome-Generated Spliced Peptides Detected by Mass Spectrometry
Bibliographic record
Abstract
Abstract CD8+ T cells recognize peptides displayed by HLA class I molecules and monitor intracellular peptide pools. It is known that the proteasome splices two short peptide fragments. Recent studies using mass spectrometry (MS) and bioinformatics analysis have suggested that proteasome-generated spliced peptides (PSPs) may account for a substantial proportion of HLA class I ligands. However, the authenticity of the PSPs identified using bioinformatics approaches remain ambiguous. In this study, we employed MS-based de novo sequencing to directly capture cryptic HLA ligands that were not templated in the genome. We identified two PSPs originating from the same protein in a human colorectal cancer line with microsatellite instability. Healthy donor-derived CD8+ T cells readily responded to the two PSPs, showing their natural HLA presentation and antigenicity. Experiments using minigene constructs demonstrated proteasome-dependent processing of two PSPs generated by standard and reverse cis splicing, respectively. Our results suggest a broader diversity of HLA class I Ag repertoires generated by proteasomal splicing, supporting the advantage of MS-based approaches for the comprehensive identification of PSPs.
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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 | 0.001 | 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".