Using the 3D structures of the viral proteinases of porcine epidemic diarrhoea virus (PEDV) to design anti-PEDV drugs
Bibliographic record
Abstract
The Porcine epidemic diarrhea virus (PEDV) is a coronavirus that causes severe diarrhoea and a high mortality rate in suckling piglets. Traditional antiviral measures such as vaccinations have not shown sufficient effectiveness in protecting piglets against PEDV infection. Given its important role in viral polyprotein processing, the PEDV 3C-like proteinase (3CL pro ) may be a potential target for the development of antiviral drugs adopting a structure-guided approach. To facilitate these efforts, we have expressed, purified, and successfully crystallized a recombinant PEDV 3CL pro that presents the cognate N-and C-termini of the viral protein. In addition, we have developed an in vitro enzymatic activity assay for screening anti-PEDV 3CL pro inhibitors using FRET-based peptide substrates. We are currently working to solve the 3-dimenstional structures of PEDV 3CL pro in complex with various substrate analogues and inhibitors. The structural information garnered through these studies will help shed light on the mechanisms of catalysis and inhibition as well as possible chemical modifications of the inhibitors that may improve their specificity and potency.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".