Improvement of Phosphoproteome Analyses Using FAIMS and Decision Tree Fragmentation. Application to the Insulin Signaling Pathway in <i>Drosophila melanogaster</i> S2 Cells
Bibliographic record
Abstract
This report examines the analytical benefits of high-field asymmetric waveform ion mobility spectrometry (FAIMS) coupled to liquid chromatography mass spectrometry (LC-MS) for phosphoproteomics analyses. The ability of FAIMS to separate multiply charged peptide ions from chemical interferences confers a unique advantage in phosphoproteomics by enhancing the detection of low abundance phosphopeptides. LC-FAIMS-MS experiments performed on TiO(2)-enriched tryptic digests from Drosophila melanogaster provided a 50% increase in phosphopeptide identification compared to conventional LC-MS analysis. Also, FAIMS can be used to select different population of multiply charged phosphopeptide ions prior to their activation with either collision activated dissociation (CAD) or electron transfer dissociation (ETD). Importantly, FAIMS enabled the resolution of coeluting phosphoisomers of different abundances to facilitate their unambiguous identification using conventional database search engines. The benefits of FAIMS in large-scale phosphoproteomics of D. melanogaster are further investigated using label-free quantitation to identify differentially regulated phosphoproteins in response to insulin stimulation.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".