Nanoliter‐volume protein enrichment, tryptic digestion, and partial separation based on isoelectric points by CE for MALDI mass spectral analysis
Bibliographic record
Abstract
Sequence-specific proteolysis is an important part of protein identification by MS. Digestion of protein is commonly performed in-solution, in sample vials with volumes ranging from milli- to microliters. When digestion is performed with a sample volume below 1 microL, handling of solution and potential sample loss via adsorption become significant issues. In this report, a proof of concept for the digestion of a small volume protein solution inside a capillary was demonstrated using a discontinuous buffer system previously studied (Nesbitt, C. A., et al. J. Chromatogr. A 2005, 1073, 175-180). Upon voltage application, a pH junction was created by the discontinuous buffer. Using myoglobin as an example, the protein molecules were enriched at the junction with an estimated volume of a few nanoliters. A protease, trypsin, was then introduced to myoglobin at the junction by coenrichment to induce in-capillary digestion. The voltage application was then suspended to provide the necessary time (2 h) for the proteolysis to proceed. When completed, voltage application was resumed, and the discontinuous buffer reconcentrated the peptides formed from digestion. Importantly, the refocused peptides appeared to roughly elute according to their pIs, resulting in a partial separation. Direct sample deposition from capillary was performed to facilitate mass spectral analysis by MALDI. The partial separation, according to pI, offered the potential benefits of MALDI MS signal enhancement and provided supplementary pI information for peptide identity assignment.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".