Optimization of Tether Length in Nonglycosidically Linked Bivalent Ligands That Target Sites 2 and 1 of a <i>Shiga</i>-like Toxin
Bibliographic record
Abstract
A series of bivalent ligands for a Shiga-like toxin have been synthesized, their experimentally determined inhibitory activities were compared with a simplified thermodynamic model, and computer simulations were used to predict the optimal tether length in bivalent ligands. The design of the inhibitors exploits the proximity of the C-2' hydroxyl groups of two P(k)-trisaccharides when bound to two different, neighboring carbohydrate recognizing binding sites located on the surface of Shiga-like toxin. NMR studies of the complex between the toxin and bivalent ligands show that site 2 and site 1 of a single B subunit are simultaneously occupied by a tethered P(k)-trisaccharide dimer. A simplified thermodynamic treatment provides the intrinsic affinities and binding energies for the intermolecular and intramolecular association events and permits the deconvolution of the contributions to the relative binding energies for the set of bivalent ligands. Conformational analysis based on MD simulations for bivalent galabioside dimers containing different tethers demonstrated that the calculated local concentrations of the pendant ligand at the second binding site correlate with the experimentally determined relative affinity values of the respective bivalent ligands, thereby providing a predictive method to optimize tether length.
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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".