“One-Pot” Methylation in Glycomics Application: Esterification of Sialic Acids and Permanent Charge Construction
Bibliographic record
Abstract
A simple and rapid "one-pot" methylation method to esterify sialic acids and construct a permanent charge was developed for N-linked glycan analysis, which combined complete nonspecific proteolytic digestion and methylation. A mixture of Asn-glycans prepared from Pronase E digestion of the glycoprotein was passed through a cation-exchange column to convert carboxylic acids to the Na+ form before being methylated with methyl iodide. Derivatives could be easily purified with a hydrophilic affinity chromatography cartridge. Mass spectrometry analysis was performed by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF) and MALDI-TOF/TOF. The mass spectrometric data indicated that carboxylic acids were methylated in addition to the formation of a quaternary ammonium in the amino group of asparagine residues. Three model glycoproteins, including ribonuclease B, ovalbumin, and transferrin, were employed to demonstrate the merits of this technique. Results showed that the stabilization of sialic acid was achieved in addition to the formation of a permanent charge. Compared to the analysis of underivatized N-glycans, detection sensitivity improved approximately 10-fold. The new technique was further evaluated with glycan profiling of serum transferrin and proved to be a sensitive method for the characterizing protein glycosylation.
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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 | 0.000 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".