Deep Sequencing of Complex Proteoglycans: A Novel Strategy for High Coverage and Site-specific Identification of Glycosaminoglycan-linked Peptides
Bibliographic record
Abstract
Proteoglycans are distributed in all animal tissues and play critical, multifaceted, physiological roles. Expressed in a spatially and temporally regulated manner, these molecules regulate interactions among growth factors and cell surface receptors and play key roles in basement membranes and other extracellular matrices. Because of the high degree of glycosylation by glycosaminoglycan (GAG), N-glycan and mucin-type O-glycan classes, the peptide sequence coverage of complex proteoglycans is revealed poorly by standard mass spectrometry-based proteomics methods. As a result, there is little information concerning how proteoglycan site specific glycosylation changes during normal and pathological processes. Here, we developed a workflow to improve sequence coverage and identification of glycosylated peptides in proteoglycans. We applied this workflow to the small leucine-rich proteoglycan decorin and three hyalectan proteoglycans: neurocan, brevican, and aggrecan.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".