Lectin Interactions on Surface-Grafted Glycostructures: Influence of the Spatial Distribution of Carbohydrates on the Binding Kinetics and Rupture Forces
Bibliographic record
Abstract
We performed quantitative analysis of the binding kinetics and affinity of carbohydrate-lectin binding and correlated them directly with the molecular and structural features of ligands presented at the nanoscale within the glycocalyx mimicking layers on surfaces. The surface plasmon resonance analysis identified that the mode of binding changed from multivalent to monovalent, which resulted in a near 1000-fold change in the equilibrium association constant, by varying the spatial distribution of carbohydrate ligands within the surface-grafted polymer layer. We identified, for the first time, that the manner in which the ligands presented on the surface has great influence on the binding at the first stage of bivalent chelating, not on the binding at the second stage. The rupture forces measured by atomic force microscope force spectroscopy also indicated that the mode of binding between lectin and ligands changed from multiple to single with variation in the ligand presentation. The dependence of lectin binding on the glycopolymer composition and grafting density is directly correlated with the nanoscale presentation of ligands on a surface, which is a determining factor in controlling the clustering and statistical effects contributing to the enhanced binding.
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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 | 0.001 |
| 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) | 0.001 | 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 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".