In-capillary enrichment, proteolysis and separation using capillary electrophoresis with discontinuous buffers: application on proteins with moderately acidic and basic isoelectric points
Bibliographic record
Abstract
Advances in mass spectrometry and capillary-format separation continue to improve the sensitivity of protein analysis. Of equal importance is the miniaturization of sample pretreatment such as enrichment and proteolysis. In a previous report (Nesbitt et al., Electrophoresis, 2008, 29, 466-474), nanoliter-volume protein enrichment, tryptic digestion, and partial separation was demonstrated in capillary electrophoresis followed by MALDI mass spectral analysis. A discontinuous buffer system, consisting of ammonium (pH 10) and acetate (pH 4), was used to create a pH junction inside the capillary, trapping a protein with a neutral isoelectric point, myoglobin (pI 7.2). Moreover, co-enrichment of myoglobin with trypsin led to an in-capillary digestion. In this paper, the ability of this discontinuous buffer system to perform similar in-capillary sample pretreatment on proteins with moderately acidic and basic pI was studied and reported. Lentil lectin (pI 8.6) and a multi-phosphorylated protein, beta-casein (pI 5.1), were selected as model proteins. In addition to the previously shown tryptic digestion, proteolysis with endoproteinase Asp-N was also performed. Digestion of these acidic and basic pI proteins produced a few peptides with extreme pI values lying outside the trapping range of the discontinuous buffer. An alteration in the peptide trapping procedure was made to accommodate these analytes. Offline MALDI mass spectral analysis confirmed the presence of the expected peptides. The presented miniaturized sample pretreatment methodology was proven to be applicable on proteins with a moderately wide range of pI. Flexibility in the choice of protease was also evident.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".