Binding site plasticity in viral PPxY Late domain recognition by the third WW domain of human NEDD4
Bibliographic record
Abstract
The recognition of PPxY viral Late domains by the third WW domain of the HECT-E3 ubiquitin ligase NEDD4 (hNEDD4-WW3) is essential for the completion of the budding process of numerous enveloped viruses, including Ebola, Marburg, HTLV1 or Rabies. hNEDD4-WW3 has been validated as a promising target for the development of novel host-oriented broad spectrum antivirals. Nonetheless, finding inhibitors with good properties as therapeutic agents remains a challenge since the key determinants of binding affinity and specificity are still poorly understood. We present here a detailed structural and thermodynamic study of the interactions of hNEDD4-WW3 with viral Late domains combining isothermal titration calorimetry, NMR structural determination and molecular dynamics simulations. Structural and energetic differences in Late domain recognition reveal a highly plastic hNEDD4-WW3 binding site that can accommodate PPxY-containing ligands with varying orientations. These orientations are mostly determined by specific conformations adopted by residues I859 and T866. Our results suggest a conformational selection mechanism, extensive to other WW domains, and highlight the functional relevance of hNEDD4-WW3 domain conformational flexibility at the binding interface, which emerges as a key element to consider in the search for potent and selective inhibitors of therapeutic interest.
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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".