Optimization of Ketobenzothiazole‐Based Type II Transmembrane Serine Protease Inhibitors to Block H1N1 Influenza Virus Replication
Bibliographic record
Abstract
Human influenza viruses cause acute respiratory symptoms that can lead to death. Due to the emergence of antiviral drug-resistant strains, there is an urgent requirement for novel antiviral agents and innovative therapeutic strategies. Using the peptidomimetic ketobenzothiazole protease inhibitor RQAR-Kbt (IN-1, aka N-0100) as a starting point, we report how substituting P2 and P4 positions with natural and unnatural amino acids can modulate the inhibition potency toward matriptase, a prototypical type II transmembrane serine protease (TTSP) that acts as a priming protease for influenza viruses. We also introduced modifications of the peptidomimetics N-terminal groups, leading to significant improvements (from μM to nM, 60 times more potent than IN-1) in their ability to inhibit the replication of influenza H1N1 virus in the Calu-3 cell line derived from human lungs. The selectivity towards other proteases has been evaluated and explained using molecular modeling with a crystal structure recently obtained by our group. By targeting host cell TTSPs as a therapeutic approach, it may be possible to overcome the high mutational rate of influenza viruses and consequently prevent potential drug resistance.
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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".