De Novo Discovery of Bicyclic Competitive Inhibitors of Zika Virus Protease from Peptide–Bismuth Phage Display Screening
Bibliographic record
Abstract
No specific vaccines or therapeutics are currently available for the prevention or treatment of Zika virus infections. The viral protease NS2B-NS3 is essential for the replication of Zika and other orthoflaviviruses, making it a target for antiviral drug development. Traditional discovery of competitive inhibitors has relied on substrate recognition sequences, typically yielding multi-basic peptides. Here, we present a de novo strategy for identifying competitive inhibitors using peptide phage display in combination with Bi(III)-mediated in situ formation of bicyclic peptides. In an initial screening, phages displaying a library of randomized peptide-bismuth bicycles were eluted by interrupting the phage-target interactions at low pH. This approach yielded a small number of peptides biased toward the active site, characterized by dibasic motifs, but only one low-ranking sequence showed modest inhibitory activity. To enhance specificity, a second screening campaign employed competitive phage elution using the dibasic boronate inhibitor CN-714 that covalently binds to the catalytically active serine residue S135 of NS2B-NS3. This strategy enriched a larger pool of competitive inhibitors sharing the characteristic dibasic substrate recognition motif. The most potent peptide-bismuth bicycle identified featured a completely novel sequence, exhibited an inhibition constant of 3.9 µM and displayed remarkable proteolytic stability over 24 hours.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".