Targeting Host Cell Furin Proprotein Convertases as a Therapeutic Strategy against Bacterial Toxins and Viral Pathogens
Bibliographic record
Abstract
Pathogens or their toxins, including influenza virus, Pseudomonas, and anthrax toxins, require processing by host proprotein convertases (PCs) to enter host cells and to cause disease. Conversely, inhibiting PCs is likely to protect host cells from multiple furin-dependent, but otherwise unrelated, pathogens. To determine if this concept is correct, we designed specific nanomolar inhibitors of PCs modeled from the extended cleavage motif TPQRERRRKKR downward arrowGL of the avian influenza H5N1 hemagglutinin. We then confirmed the efficacy of the inhibitory peptides in vitro against the fluorescent peptide, anthrax protective antigen (PA83), and influenza hemagglutinin substrates and also in mice in vivo against two unrelated toxins, anthrax and Pseudomonas exotoxin. Peptides with Phe/Tyr at P1' were more selective for furin. Peptides with P1' Thr were potent against multiple PCs. Our strategy of basing the peptide sequence on a furin cleavage motif known for an avian flu virus shows the power of starting inhibitor design with a known substrate. Our results confirm that inhibiting furin-like PCs protects the host from the distinct furin-dependent infections and lay a foundation for novel, host cell-focused therapies against acute diseases.
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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".