Impact of the Nature and Size of the Polymeric Backbone on the Ability of Heterobifunctional Ligands to Mediate Shiga Toxin and Serum Amyloid P Component Ternary Complex Formation
Bibliographic record
Abstract
Inhibition of AB(5)-type bacterial toxins can be achieved by heterobifunctional ligands (BAITs) that mediate assembly of supramolecular complexes involving the toxin's pentameric cell membrane-binding subunit and an endogenous protein, serum amyloid P component, of the innate immune system. Effective in vivo protection from Shiga toxin Type 1 (Stx1) is achieved by polymer-bound, heterobifunctional inhibitors-adaptors (PolyBAITs), which exhibit prolonged half-life in circulation and by mediating formation of face-to-face SAP-AB(5) complexes, block receptor recognition sites and redirect toxins to the spleen and liver for degradation. Direct correlation between solid-phase activity and protective dose of PolyBAITs both in the cytotoxicity assay and in vivo indicate that the mechanism of protection from intoxication is inhibition of toxin binding to the host cell membrane. The polymeric scaffold influences the activity not only by clustering active binding fragments but also by sterically interfering with the supramolecular complex assembly. Thus, inhibitors based on N-(2-hydroxypropyl) methacrylamide (HPMA) show significantly lower activity than polyacrylamide-based analogs. The detrimental steric effect can partially be alleviated by extending the length of the spacer, which separates pendant ligand from the backbone, as well as extending the spacer, which spans the distance between binding moieties within each heterobifunctional ligand. Herein we report that polymer size and payload of the active ligand had moderate effects on the inhibitor's activity.
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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) | 0.002 | 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".