Identification and Quantification of Glycoproteins Using Ion-Pairing Normal-phase Liquid Chromatography and Mass Spectrometry
Bibliographic record
Abstract
Glycoprotein structure determination and quantification by MS requires efficient isolation of glycopeptides from a proteolytic digest of complex protein mixtures. Here we describe that the use of acids as ion-pairing reagents in normal-phase chromatography (IP-NPLC) considerably increases the hydrophobicity differences between non-glycopeptides and glycopeptides, thereby resulting in the reproducible isolation of N-linked high mannose type and sialylated glycopeptides from the tryptic digest of a ribonuclease B and fetuin mixture. The elution order of non-glycopeptides relative to glycopeptides in IP-NPLC is predictable by their hydrophobicity values calculated using the Wimley-White water/octanol hydrophobicity scale. O-linked glycopeptides can be efficiently isolated from fetuin tryptic digests using IP-NPLC when N-glycans are first removed with PNGase. IP-NPLC recovers close to 100% of bacterial N-linked glycopeptides modified with non-sialylated heptasaccharides from tryptic digests of periplasmic protein extracts from Campylobacter jejuni 11168 and its pglD mutant. Label-free nano-flow reversed-phase LC-MS is used for quantification of differentially expressed glycopeptides from the C. jejuni wild-type and pglD mutant followed by identification of these glycoproteins using multiple stage tandem MS. This method further confirms the acetyltransferase activity of PglD and demonstrates for the first time that heptasaccharides containing monoacetylated bacillosamine are transferred to proteins in both the wild-type and mutant strains. We believe that IP-NPLC will be a useful tool for quantitative glycoproteomics.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".