Discovery and design of novel inhibitors of botulinus neurotoxin A: targeted ‘hinge’ peptide libraries
Bibliographic record
Abstract
Intoxication by the zinc protease botulinus neurotoxin A (BoNT-A) results from cleavage of a single Q-R bond in the neuronal protein SNAP-25, which disables the docking mechanism required for neurotransmitter release. In the present study, potential inhibitors of BoNT-A were assessed from their effects on the BoNT-A cleavage of a synthetic 17-mer peptide (SNAP-25, residues 187-203) spanning the Q-R cleavage site. Compounds that inhibited BoNT-A included thiols (zinc chelators) such as dithiothreitol, dimercaptopropanesulfonic acid, mercaptosuccinic acid and captopril. In addition, compounds containing multiple acidic functions, such as the SNARE motif V2 (ELDDRADALQ), the tripeptide Glu-Glu-Glu and the steroid glycoside glycyrrhizic acid, were effective inhibitors. 'Hinge' peptide mini-libraries (PMLs) having the structure acetyl-X(1)-X(2)-linker-X(3)-X(4)-NH(2) or X(1)-X(2)-linker-X(3), where X(1)-X(4) were mixtures of selected amino acids and the flexible linker was 4-aminobutyric acid, also provided effective inhibition. Targeted PMLs containing the acidic amino acids Asp and Glu, the scissile-bond amino acids Gln and Arg and the zinc chelators His and Cys produced pronounced inhibition of BoNT-A. Deconvolution of these libraries will provide novel ligands with improved inhibitory potency as leads in the design of peptide mimetics to treat BoNT poisoning.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".