Bottom‐up proteomics of envelope proteins extracted from spinach chloroplast via high organic content CE‐MS
Bibliographic record
Abstract
A high organic content CE-MS/MS (HOCE-MS/MS) method was developed for the proteomic analysis of envelope proteins extracted from spinach leaves. Separation was performed in a 1-m long hydroxypropyl cellulose coated capillary, using 8% (v/v) formic acid in 70% (v/v) methanol and 22% water as the BGE. A flow-through microvial interface was used to couple the CE system with an Orbitrap Fusion Lumos mass spectrometer, and field-amplified sample stacking was used to improve the concentration sensitivity. Using this optimized method, 3579 peptides and 1141 proteins were identified using the Proteome Discoverer software with a 1% false discovery rate at the protein level. Relative to conventional aqueous CE, HOCE-MS did a better job of discovering hydrophobic peptides and provided more peptide and protein identifications. Relative to nano-LC-MS, it achieved comparable peptide and protein identification performance and detected peptides not identified by LC-MS: of the full set of peptides identified using the two techniques, 19% were identified only using HOCE-MS. It also outperformed nano-LC-MS with respect to the detection of low molecular weight peptides.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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".