Use of Synthetic Standard Peptides in Standardized Digests to Evaluate Both Sample and Instrument Suitability in Proteomics
Bibliographic record
Abstract
Whole proteome digests are routinely used to diagnose chromatograph and mass spectrometer outputs to ensure suitability for the analysis of complex matrices, but their usage inherently fails to prove system reliability under varying "load" conditions. There is a need for a reliable, predictive tool that can explain variation in both instrument response and downstream identification results from a whole proteome analysis. We designed an experiment using a hybrid sample of standardized materials to create such an approach, which could then lead to a new system suitability test for bottom-up proteomics. The standard HeLa protein digest was combined with Promega 6 × 5 LC-MS/MS Peptide Reference Mix and diluted to create a range of sample mass loadings and reference peptide concentrations. Data were collected using data-dependent (DDA) and data-independent acquisition methods, and reference peptide peak abundances were correlated to the number of protein identifications (IDs), peptide groups (PGs), and peptide spectrum matches (PSMs) found by Proteome Discoverer. An asymptotic relationship explained decreasing IDs, PGs, and PSMs identified from the HeLa digest with decreasing 6 × 5 Peptide abundances. By linking the mass spectrometer measurement of ion abundance with downstream results obtained from a complex matrix, we successfully used the hybrid standardized sample to mathematically define new system suitability thresholds.
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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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".