Comparison of the methods for profiling glycoprotein glycans—HUPO Human Disease Glycomics/Proteome Initiative multi-institutional study
Bibliographic record
Abstract
Mass spectrometry (MS) of glycoproteins is an emerging field in proteomics, poised to meet the technical demand for elucidation of the structural complexity and functions of the oligosaccharide components of molecules. Considering the divergence of the mass spectrometric methods employed for oligosaccharide analysis in recent publications, it is necessary to establish technical standards and demonstrate capabilities. In the present study of the Human Proteome Organisation (HUPO) Human Disease Glycomics/Proteome Initiative (HGPI), the same samples of transferrin and immunoglobulin-G were analyzed for N-linked oligosaccharides and their relative abundances in 20 laboratories, and the chromatographic and mass spectrometric analysis results were evaluated. In general, matrix-assisted laser desorption/ionization (MALDI) time-of-flight MS of permethylated oligosaccharide mixtures carried out in six laboratories yielded good quantitation, and the results can be correlated to those of chromatography of reductive amination derivatives. For underivatized oligosaccharide alditols, graphitized carbon-liquid chromatography (LC)/electrospray ionization (ESI) MS detecting deprotonated molecules in the negative ion mode provided acceptable quantitation. The variance of the results among these three methods was small. Detailed analyses of tryptic glycopeptides employing either nano LC/ESI MS/MS or MALDI MS demonstrated excellent capability to determine site-specific or subclass-specific glycan profiles in these samples. Taking into account the variety of MS technologies and options for distinct protocols used in this study, the results of this multi-institutional study indicate that MS-based analysis appears as the efficient method for identification and quantitation of oligosaccharides in glycomic studies and endorse the power of MS for glycopeptide characterization with high sensitivity in proteomic programs.
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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 | 0.008 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".