Identification of carbohydrates binding to lectins by using surface plasmon resonance in combination with HPLC profiling
Bibliographic record
Abstract
A new, powerful method is presented for screening the binding in real time and taking place under dynamic conditions of oligosaccharides to lectins. The approach combines an SPR biosensor and HPLC profiling with fluorescence detection, and is applicable to complex mixtures of oligosaccharides in terms of ligand-fishing. Labeling the oligosaccharides with 2-aminobenzamide ensures a detection level in the fmol range. In an explorative study the binding of RNase B-derived oligomannose-type N-glycans to biosensor-immobilized concanavalin A (Con A) was examined, and an affinity ranking could be established for Man(5)GlcNAc(2) to Man(9)GlcNAc(2), as monitored by HPLC. In subsequent experiments and using well-defined labeled as well as nonlabeled oligosaccharides, it was found that the fluorescent tag does not interfere with the binding and that the optimum epitope for the interaction with Con A comprises the tetramannoside unit Manalpha2Manalpha6(Manalpha3)Man[D(3)B(A)4'], rather than the generally accepted trimannoside Manalpha6 (Manalpha3)Man [B(A)4' or 4(4')3]. In a similar experimental setup, the interaction of various fucosylated human milk oligosaccharides with the fucose-binding lectin from Lotus tetragonolobus purpureaus was studied, and it appeared that oligosaccharides containing blood group H could selectively be retained and eluted from the lectin-coated surface. Finally, using the same lectin and a mixture of O-glycans derived from bovine submaxillary gland mucin, minor constituents but containing fucose could selectively be picked from the analyte solution as demonstrated by HPLC profiling.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".