First demonstration of differential inhibition of lectin binding by synthetic tri- and tetravalent glycoclusters from cross-coupling of rigidified 2-propynyl lactoside
Bibliographic record
Abstract
The interplay of mammalian lectins such as galectins with cellular glycoconjugates is intimately involved in crucial reaction pathways including tumor cell adhesion, migration or growth regulation. These clinically relevant functions explain the interest in designing glycoclusters with potent activity to interfere with lectin binding. In view of the perspective for medical applications the following objective arises: to correlate topological factors of ligand display most favorably to reactivity against endogenous lectins. To date, plant agglutinins have commonly been used as models. Properly addressing this issue we first prepared di- to tetravalent clusters from 2-propynyl lactoside under mild oxidative homocoupling conditions and using the Sonogashira palladium-catalyzed cross-coupling reaction with triiodobenzene or pentaerythritol cores. These products were tested for bioactivity in a competitive solid-phase assay using different labeled sugar receptors as probes, i,e. the beta-trefoil mistletoe lectin, the natural lactoside-binding immunoglobulin G fraction from human serum and three mammalian galectins from two subgroups. The lactose headgroups in the derivatives retained ligand properties. Differences in inhibitory capacity were marked between the galectins. In contrast to homodimeric proto-type galectins-1 and -7 significant inhibition of galectin-3 binding with a 7-fold increase in relative potency was observed for the trivalent compound. In comparison, the binding of the beta-trefoil mistletoe agglutinin was reduced best by tetravalent substances The result for galectin-3 was independently confirmed by haemagglutination and cytofluorometric cell binding assays. These data underline the feasibility of galectin-type target selectivity by compound design despite using an identical headgroup (lactose) in synthesis.
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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) | 0.000 | 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".