Glycoproteomic Reactor for Human Plasma
Bibliographic record
Abstract
We describe the development of a glycoproteomic reactor that combines multiple biochemical and chemical protein processing into a single device for the study of N-glycosylated proteins. The glycoproteins are first enriched by concanavalin A affinity chromatography and then transferred onto and efficiently processed in the glycoproteomic reactor. This glycoproteomic reactor combines protein concentration and purification, disulfide bond reduction, peptide-N-glycosidase-mediated (18)O-labeling and deglycosylation, alkylation, tryptic digestion and pH based fractionation in a device that has an interstitial volume (reaction volume) of approximately 1 microL. We demonstrated the potential of the glycoproteomic reactor using human plasma. Under stringent criteria, 82 unique glycopeptides representing 41 unique glycoproteins were identified from as little as 5 microL of human plasma. Our glycoproteomic reactor reduces the sample processing time to less than 1.5 h, reduces the reagent consumption while providing over 1000-fold concentration of the sample, provides efficient removal of high concentration of glycan buffer, and, finally, allows both glycopeptides and nonglycosylated tryptic peptides to be analyzed by the mass spectrometer which provides much greater protein coverage and more reliable identifications.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".