Combining Glycomimetic and Multivalent Strategies toward Designing Potent Bacterial Lectin Inhibitors
Bibliographic record
Abstract
As part of ongoing activities toward the design of potent and selective ligands against galactoside-binding proteins from animal, bacterial, and plant lectins, a systematic investigation involving the synthesis and binding evaluations of a series of original β-C-galactopyranoside mimetics is described. The multivalent presentation of partly optimized candidates on various dendritic scaffolds through Cu(I)-catalyzed azide-alkyne cycloaddition (CuAAc) has also been achieved. Biophysical investigations based on isothermal titration calorimetry (ITC) have indicated a dissociation constant in the low micromolar range for the best optimized monovalent conjugate (K(d)=37 μM). The results thus confirmed that stable C-galactosides could represent efficient synthetic glycomimetics of natural α-linked oligosaccharidic inhibitors of PA-IL lectin (Lec A) from the pathogenic Pseudomonas aeruginosa. Striking enhancements in the avidity of the glycoconjugates were also observed for tri-, hexa-, and nonavalent derivatives, among which the most potent exhibited dissociation constants below 500 nM, corresponding to a 400-fold increase in affinity compared with the β-D-Gal-O-Me used as reference. To deepen our understanding of the binding mode of the best glycomimetics involved in the recognition process, molecular modeling studies, docking calculations, and NMR diffusion measurements have been performed. Although favorable complementary interactions induced by the addition of the hydrophobic aglycon might explain the affinity enhancement, experimental determination of the size and the topology of the multivalent conjugates further supported the formation of aggregative complexes as a major multivalent binding mode. This work represents a systematic and comprehensive study towards a thorough understanding of the protein-carbohydrate interactions involved in Pseudomonas aeruginosa infection, and as such should prove useful for the development of stable and optimized anti-adhesive agents.
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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 | 0.000 | 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 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".