Inhibition of Zika Virus Protease by Modulating NS2B-NS3 Interactions
Bibliographic record
Abstract
Zika virus infections in humans were identified in several African countries in the 1950s and spread globally in the mid-2010s. Cases began to surge in South America around 2016, coinciding with a rise in severe developmental disorders in babies born to infected mothers. Gaining a deeper understanding of ways to treat infection is crucial. To this end, we sought to investigate the dynamics of Zika Virus Protease NS2B-NS3, a drug target that will enable effective treatment of infection and may be invaluable in treating more lethal variants that may eventually emerge. Specifically, we employed hydrogen-deuterium exchange mass spectrometry on NS2B-NS3 to observe which regions of the protein are quick or slow to exchange, and how these exchange patterns change in the presence of an allosteric inhibitor. From these studies, we observe that Zika Virus Protease populates the open conformation when it is unbound or bound to the allosteric inhibitor MH1. We further identified a single substitution in NS3, A125C, that directly blocks allosteric inhibition. The observed deuterium uptake patterns provide a detailed view of Zika Virus Protease dynamics in unbound and inhibitor-bound states, allowing us to visualize how allosteric binding at NS3 prevents closure of NS2B and propagates structural perturbations that together result in protease inhibition. Importantly, our studies predict that interactions between the NS2B cofactor and the NS3 core determine the potency of this class of inhibitors across the flaviviral proteases. Pan-flaviviral inhibitors would provide invaluable antiviral modalities, and insights from these studies should aid in their development.
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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".