Application of the StrOligo algorithm for the automated structure assignment of complex N‐linked glycans from glycoproteins using tandem mass spectrometry
Bibliographic record
Abstract
Oligosaccharides associated with proteins are known to give these molecules specific conformations and functions. Analysis of proteins would not be complete without studying the glycans. However, high-throughput techniques in proteomics will soon overwhelm the current capacity of methods if no automation is incorporated into glycomics. New capabilities of the StrOligo algorithm introduced for this purpose (Ethier et al., Rapid Commun. Mass Spectrom., 2002; 16: 1743) will be discussed here. Experimental tandem mass spectra were acquired to test the algorithm using a hybrid quadrupole-time-of-flight (QqTOF) instrument with a matrix-assisted laser desorption/ionization (MALDI) source. The samples were N-linked oligosaccharides from monoclonal antibody IgG, beta interferon and fetuin, detached by enzymatic deglycosylation and labeled at the reducing end. Improvements to the program were made in order to reduce the need for user intervention. StrOligo strips the spectra down to monoisotopic peaks only. The algorithm first builds a relationship tree, accounting for each observed loss of a monosaccharide moiety, and then analyzes the tree and proposes possible structures from combinations of adducts and fragment ion types. A score, which reflects agreement with experimental results, is then given to each proposed structure. The program then decides which combination is the best one and labels relevant peaks in the experimental mass spectrum using a modified nomenclature. The usefulness of the algorithm has been demonstrated by assigning structures to several glycans released from glycoproteins. The analysis was completed in less than 2 minutes for any glycan, which is a substantial improvement over manual interpretation.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 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) | 0.004 | 0.002 |
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".