<i>N</i>-Glycosylation Analysis Using the StrOligo Algorithm
Bibliographic record
Abstract
N-glycosylation of proteins is the predominant glycosylation in mammals and confers specific conformations, localization, and functions to proteins. High-throughput proteomics techniques have focused on the identification of proteins through amino acid sequence determination, with little attention paid to their post-translational modification, in particular, glycosylation. High-throughput mass spectrometric data often contain information about glycosylation, but this is systematically discarded by proteomic search engines. We have developed an algorithm, StrOligo (for STRucture of OLIGOsaccharides), capable of automated analysis of oligosaccharide composition and possible structures by mass spectrometry. The algorithm analyzes tandem mass spectrometry (MS/MS) data in an automated three-step process and provides possible structures and a discrimination score. In the first step, the algorithm constructs a relationship tree of the monosaccharide moiety losses observed in the MS/MS spectrum. In the second step, the algorithm uses the tree to propose possible compositions and structures from combinations of adduct and fragment ions as well as a discrimination score, which reflects the fit with the experimental results. Finally, an interface is available to visualize the proposed structures and their scores. As well, the MS/MS spectrum is displayed with relevant peaks labeled for the proposed structure with the highest discrimination score, using a modified nomenclature.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.020 | 0.012 |
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".