Microcolumn Capture and Digestion of Proteins Combined with Mass Spectrometry for Protein Identification
Bibliographic record
Abstract
A procedure has been developed for protein identification using mass spectrometry (MS) that incorporates sample cleanup, preconcentration, and protein digestion in a single-stage system. The procedure involves the adsorption of a protein, or protein mixture, from solution onto a hydrophobic resin that is contained within a microcolumn. Sample loading is accomplished by flowing the protein solution through the microcolumn, where the protein adsorbs to the hydrophobic surface. The protein is digested while still bound to the hydrophobic surface by flowing a buffered trypsin solution through the column bed. The peptide fragments are subsequently eluted for detection by MALDI or ESI-MS. The procedure is demonstrated using dilute protein samples containing high concentrations of salt, urea, and modest amount of sodium dodecyl sulfate relative to protein. Peptide fragments are also detected by MS from a 500 nM bacteriorhodopsin solution digested in a microcolumn. In this case, a combined cyanogen bromide/trypsin digestion was performed in-column. The procedure is applied to the MALDI-MS/MS identification of proteins present in an individual fraction collected by ion exchange HPLC separation of E. coli total cell extract. An additional application is illustrated in the analysis of a human plasma fraction. A total of 14 proteins, which were present in the sample at sub-micromolar concentrations, were identified from ESI-MS/MS. The microcolumn digestion procedure represents the next step toward a system for fully automated protein analysis through capture and digestion of the adsorbed protein on hydrophobic surfaces.
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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 | 0.001 | 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) | 0.001 | 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".