Discovery of Nedd4 auto-ubiquitination inhibitors
Bibliographic record
Abstract
Abstract E3 ubiquitin ligases are critical to the protein degradation pathway by catalyzing the final step in protein ubiquitination by mediating ubiquitin transfer from E2 enzymes to target proteins. Nedd4 is a HECT domain-containing E3 ubiquitin ligase with a wide range of protein targets, the dysregulation of which has been implicated in myriad pathologies, including cancer and Parkinson's disease. Towards the discovery of compounds disrupting the auto-ubiquitination activity of Nedd4, we developed and optimized a TR-FRET assay for high-throughput screening. Through selective screening of a library of potentially covalent compounds, compounds25and81demonstrated apparent IC50values of 52 µM and 31 µM, respectively. Tandem mass spectrometry (MS/MS) analysis confirmed that25and81were covalently bound to Nedd4 cysteine residues (Cys182 and Cys867). In addition,81also adducted to Cys627. Auto-ubiquitination assays of Nedd4 mutants featuring alanine substitutions for each of these cysteines suggested that the mode of inhibition of these compounds occurs through blocking the catalytic Cys867. The discovery of these inhibitors could enable the development of therapeutics for various diseases caused by Nedd4 E3 ligase dysregulation.
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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".