Identification of Tandem Mass Spectra of Mixtures of Isomeric Peptides
Bibliographic record
Abstract
Shotgun proteomics separates peptides by chromatography and precursor mass over charge, yet in almost any large data set of a complex sample, there will be some tandem mass spectra containing more than one peptide. These mixture spectra contain two coeluting peptides with close precursor mass over charge, and sometimes contain exact isomers, often the same peptide with the same modification in two different positions. Isomers present a problem when the position of the modification is of special interest, as in histone modification studies or "oxidative footprinting" studies of protein structure. Here we give algorithms for identifying isomeric mixtures, and present results on two different histones and four oxidative footprinting targets. Five of the six targets contain at least one peptide that appears in isomeric mixtures, but in none of the cases are mixtures so prevalent that they greatly impact the overall identification rate.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".