Analysis of Sialic Acid Linkage in N-Linked Glycopeptides Using Liquid Chromatography–Electron-Activated Dissociation Time-of-Flight Mass Spectrometry
Bibliographic record
Abstract
Herein, we report a novel liquid chromatography coupled with tandem mass spectrometry method to characterize N -acetylneuraminic acid (Neu5Ac, Sa) linkage in N -linked glycans in glycopeptides with no sialic acid derivatization. First, we established a separation in reversed-phase high-performance liquid chromatography (HPLC) using a higher formic acid concentration in the mobile phases, which separated the N -glycopeptides depending on the Sa linkage. We also demonstrated a novel characterization method of Sa linkages in N -glycopeptides using electron-activated dissociation. We found that hot electron capture dissociation using an electron beam energy higher than 5 eV cleaved glycosidic bonds in glycopeptides, resulting in each glycosidic bond in the antennas being broken on both sides of the oxygen atom. Such glycosidic bond cleavage at the reducing end (C-type ion) showed the difference in Sa linkages between Sa-Gal, Gal-GlcNAc, and GlcNAc-Man. We proposed a rule to characterize the Sa linkages using the Sa-Gal products. This method was applied to N -glycopeptides in tryptic fetuin digest separated by an optimized reversed-phase HPLC. We successfully identified a number of isomeric glycoforms in the glycopeptides with different Sa links, whose peptide backbones were also simultaneously sequenced by hot ECD.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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".