Glycoprotein Analysis by Capillary Zone Electrophoresis-Electrospray Mass Spectrometry
Bibliographic record
Abstract
The study of glycoproteins as intact molecules or as smaller fragments generated by chemical or enzymatic digestion is well suited to the technique of capillary zone electrophoresis-electrospray mass spectrometry (CZE-ESMS). Separation of glycoform populations of intact glycoproteins or protein digests is possible using buffers compatible with mass spectrometric detection ( 1 – 8 ). CZE-ESMS analysis of intact proteins can provide semiquantitative information about the degree of heterogeneity of the glycoprotein ( 1 , 6 , 7 ). Enzymatic or chemical digestion of the glycoprotein, followed by CZE-ESMS, gives a direct measure of individual sites of heterogeneity ( 1 – 5 ). Combined CZE-collision induced dissociation (CID) mass spectrometric experiments (CZE-MS-MS) of digests enables the characterization of oligosaccharide structures ( 1 – 5 ). In particular, CID of glycopeptides is characterized by fragment ions corresponding to cleavage at each glycosidic bond. The occurrence of specific carbohydrate residues such as hexose (Man, Glc, Gal), N -acetylhexosamine (GlcNAc, GalNAc), or N-acetylneuraminic acid (NeuNAc) can be monitored by the observation of characteristic oxonium ions at m/z 163, 204, and 292, respectively. More recently, CZE-front-end CID-MS-MS has been shown to be a powerful tool for peptide sequencing of glycopeptides using subpicomole quantities of injected glycoprotein ( 3 – 5 ). These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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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 | 0.002 | 0.002 |
| 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".