On‐column digestion of proteins in aqueous‐organic solvents
Bibliographic record
Abstract
Proteolytic digestion is an important step in protein identification by peptide mass mapping and tandem mass spectrometry (MS/MS)-based peptide sequencing. Traditional methods of protein digestion require extended incubation times and have difficulty with proteolytically resistant proteins. Here, we describe a method in which a protein solution was combined with a mixed aqueous-organic solution (methanol, isopropanol, or acetonitrile) and passed through a microcolumn containing immobilized trypsin. Myoglobin sequence coverage was high (>85%) in all three solvents, and differences in spectra were seen among the different solution conditions. Notably, methanol-based digestions produced fewer missed cleavages while acetonitrile-based digestions produced the most peptides and the most intense mass spectra. Flow rates through the column were varied from 0.5 to 15 micro L/min, corresponding to column residence times of 78 and 2.6 s, respectively. All flow rates produced high sequence coverage of myoglobin, although, at higher flow rates, more missed cleavages were observed. No significant increase in undigested myoglobin was observed with flow rates up to 15 micro L/min. The described method was applied to the digestion of human transferrin (hTf), a proteolytically resistant protein. Matrix-assisted laser desorption/ionization time-of-flight mass spectrometric (MALDI-TOFMS) analysis detected 42 peptides covering 46% of the hTf sequence. The traditional aqueous method resulted in 12 peptides (8% sequence coverage) only when high concentrations of trypsin were used. Lastly, digestion of low nanomolar myoglobin was shown to produce detectable peptides and resulted in a correct database hit. Thus, we demonstrate a method that is capable of rapid on-line digestion, thereby lending itself to high-throughput identification of proteins.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
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".