Blending Protein Separation and Peptide Analysis through Real-Time Proteolytic Digestion
Bibliographic record
Abstract
Typical liquid- or gel-based protein separations require enzymatic digestion as an important first step in generating protein identifications. Traditional protocols involve long-term proteolytic digestion of the separated protein, often leading to sample loss and reduced sensitivity. Previously, we presented a rapid method of proteolytic digestion that showed excellent digestion of resistant and low concentrations of protein without requiring reduction and alkylation. Here, we demonstrate on-line, real-time tryptic digestion in conjunction with reversed-phase protein separation. The studies were aimed at optimizing pH and ionic strength and the size of the digestion element, to produce maximal protein digestion with minimal effects on chromatographic integrity. Upon establishing optimal conditions, the digestion element was attached downstream from a capillary C4 reversed-phase column. A four-protein mixture was processed through the combined system, and the resulting peptides were analyzed on-line by electrospray mass spectrometry. Extracted ion chromatograms for protein chromatography based on peptide elution were generated. These were shown to emulate ion chromatograms produced in a subsequent run without the digestion element, based on protein elution. The methodology will enable rapid and sensitive analysis of liquid-based protein separations using the power of bottom-up proteomics methodologies.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".