Matrix‐assisted laser desorption/ionization on‐target method for the investigation of oligosaccharides and glycosylation sites in glycopeptides and glycoproteins
Bibliographic record
Abstract
The significance of glycoproteins in living systems instigates the ceaseless expansion of new techniques and procedures for the analysis of biological samples. Many of these applications are focused on improving the detection limit of analyzed material. In a previous study, we described a procedure for the detection of oligosaccharides cleaved from tryptic glycopeptides. Treatment of deglycosylated fractions with phenylhydrazine gave rise to peaks consistent with labeled glycans, and both types of compounds--deglycosylated peptides and oligosaccharides--were recorded from one spot and observed in one matrix-assisted laser desorption/ionization (MALDI) mass spectrum for the first time. Here, we added an additional step to this simple procedure of deglycosylating glycopeptides directly from the target spot of the first analyzed glycosylated peptides. For the purpose of this new study, a mixture of 2-aza-2-thiothymine and phenylhydrazine hydrochloride showed to be an excellent matrix for glycopeptides, oligosaccharides, deglycosylated peptides and moreover it allowed PNGaseF to be active enough to cleave oligosaccharides from peptides. The efficiency of this procedure is demonstrated on a series of intact glycoproteins and on the analysis of tryptic peptides obtained from IgG and total mouse serum. This one-step on-target deglycosylation method with subsequent derivatization on the same spot makes MALDI-MS analyses of glycopeptides fast, simple and accessible for biological samples, where classical procedures cannot produce useful results.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".