Inhibition of HECT E3 ligases as potential therapy for COVID-19
Bibliographic record
Abstract
SARS-CoV-2 is responsible for the ongoing world-wide pandemic which has already taken more than two million lives. Effective treatments are urgently needed. The enzymatic activity of the HECT-E3 ligase family members has been implicated in the cell egression phase of deadly RNA viruses such as Ebola through direct interaction of its VP40 Protein. Here we report that HECT-E3 ligase family members such as NEDD4 and WWP1 interact with and ubiquitylate the SARS-CoV-2 Spike protein. Furthermore, we find that HECT family members are overexpressed in primary samples derived from COVID-19 infected patients and COVID-19 mouse models. Importantly, rare germline activating variants in the NEDD4 and WWP1 genes are associated with severe COVID-19 cases. Critically, I3C, a natural NEDD4 and WWP1 inhibitor from Brassicaceae, displays potent antiviral effects and inhibits viral egression. In conclusion, we identify the HECT family members of E3 ligases as likely novel biomarkers for COVID-19, as well as new potential targets of therapeutic strategy easily testable in clinical trials in view of the established well-tolerated nature of the Brassicaceae natural compounds.
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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".