Influence of labeling on the glycan affinities and specificities of glycan-binding proteins. A case study involving a C-terminal fragment of human galectin-3
Bibliographic record
Abstract
Glycan interactions with glycan-binding proteins (GBPs) play essential roles in a wide variety of cellular processes. Currently, the glycan specificities of GBPs are most often inferred from binding data generated using glycan arrays, wherein the GBP is incubated with oligosaccharides immobilized on a glass surface. Detection of glycan-GBP binding is typically fluorescence-based, involving the labeling of the GBP with a fluorophore or with biotin, which binds to fluorophore-labeled streptavidin, or using a fluorophore-labeled antibody that recognizes the GBP. While it is known that covalent labeling of a GBP may influence its binding properties, these effects have not been well studied and are usually overlooked when analyzing glycan array data. In the present study, electrospray ionization mass spectrometry (ESI-MS) was used to quantitatively evaluate the impact of GBP labeling on oligosaccharide affinities and specificities. The influence of three common labeling approaches, biotinylation, labeling with a fluorescent dye and introducing an iodination reagent, on the affinities of a series of human milk and blood group oligosaccharides for a C-terminal fragment of human galectin-3 was evaluated. In all cases labeling resulted in a measurable decrease in oligosaccharide affinity, by as much as 90%, and the magnitude of the change was sensitive to the nature of the ligand. These findings demonstrate that GBP labeling may affect both the absolute and relative affinities and, thereby, obscure the true glycan binding properties. These results also serve to illustrate the utility of the direct ESI-MS assay for quantitatively evaluating the effects of protein labeling on ligand binding.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".