Improving MHC‐I Ligand Identifications from LC‐MS/MS Data by Incorporating Allelic Peptide Motifs
Bibliographic record
Abstract
MHC class I (MHC-I)-bound ligands play a pivotal role in CD8 T cell immunity and are hence of major interest in understanding and designing immunotherapies. One of the most commonly utilized approaches for detecting MHC ligands is LC-MS/MS. Unfortunately, the effectiveness of current algorithms to identify MHC ligands from LC-MS/MS data is limited because the search algorithms used were originally developed for proteomics approaches detecting tryptic peptides. Consequently, the analysis often results in inflated false discovery rate (FDR) statistics and an overall decrease in the number of peptides that pass FDR filters. Andreatta et al. describe a new scoring tool (MS-rescue) for peptides from MHC-I immunopeptidome datasets. MS-rescue incorporates the existence of MHC-I peptide motifs to rescore peptides from ligandome data. The tool is demonstrated here using peptides assigned from LC-MS/MS data with PEAKs software but can be deployed on data from any search algorithm. This new approach increased the number of peptides identified by up to 20-30% and promises to aid the discovery of novel MHC-I ligands with immunotherapeutic potential.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".