Quantifying Protein–Glycan Interactions Using Native Mass Spectrometry
Bibliographic record
Abstract
Interactions between glycan-binding proteins (GBPs) and carbohydrates (glycans) are essential to many biological processes relevant to human health and disease. For most GBPs, however, their glycan interactome-the repertoire of glycans recognized and their specificities-is poorly defined. The structural diversity of biologically relevant glycans and their limited availability in purified form, as well as their varied presentation, often as glycoconjugates, and weak affinities are key challenges hindering comprehensive glycan interaction mapping. Native mass spectrometry (nMS), a versatile, sensitive and label-free tool for the discovery of GBP-glycan interactions and quantifying their stoichiometry and thermodynamic parameters, is poised to play a leading role in defining the glycan interactome of GBPs. Here, we review established nMS methodologies, as well as important experimental and instrumental considerations, for detecting GBP-glycan interactions in vitro, and reliably measuring their stoichiometry and affinity. Recent advances in nMS methods for high-throughput library screening, including shotgun glycomics, and quantifying GBP interactions with glycoproteins and glycosphingolipids, are also described.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.004 |
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".