Selective Enrichment of Glycopeptides from Glycoprotein Digests Using Ion-Pairing Normal-Phase Liquid Chromatography
Bibliographic record
Abstract
Detailed structural analysis of glycoproteins requires methods capable of isolating glycopeptides from tryptic digests of purified glycoproteins and complex protein mixtures. Here, we describe the selective and reproducible isolation of glycopeptides from a peptide mixture using ion-pairing normal-phase chromatography (IP-NPLC). The addition of inorganic monovalent ions in normal-phase chromatography appears to increase the hydrophobicity difference between peptides and glycopeptides, allowing for more efficient separation. Our data show that IP-NPLC effectively enriches glycopeptides from a tryptic digest of ribonuclease B, bovine fetuin, and a complex mixture of glycoproteins, when compared with normal-phase chromatography alone. The results of the IP-NPLC experiments can be explained using the Wimley-White water/octanol free energy scale to illustrate the hydrophobicity difference of nonglycosylated peptides with and without ion-pairing. We believe that IP-NPLC will be an important tool in glycoprotein characterization and glycoproteomic studies.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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".