A framework for unbiased, robust and system-wide characterization of MHC-bound peptides and epitopes (APP5P.111)
Bibliographic record
Abstract
Abstract Vaccines are the most inexpensive medicine in the long run, yet developing them remains a slow process. A major challenge is to identify proteins efficiently processed by the host and presented on major histocompatibility complexes (MHCs). Furthermore, the extreme polymorphism of the MHCs means that candidate antigens must be tested across many genotypes. Immunopeptidomics, the study of all peptides presented by the host’s immune system, holds promise but is limited by informatics:standard approaches result in high error rates for these samples. We describe here an experimentally validated informatic treatment of mass spectrometric data from peptides eluted from the surface of antigen-presenting cells that can identify epitopes bound by specific MHCs, predict their core binding regions, and reveal consensus binding motifs of MHC alleles. Our approach avoids the biases inherent with MHC immunopurification or prior consensus motifs and enables rational vaccine design by allowing rapid and sensitive screening of individual immunopeptidomes.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".