Targeted modulation of E3 ligases using engineered ubiquitin variants
Bibliographic record
Abstract
Ubiquitination plays an essential role in signal transduction to regulate most if not all cellular processes. Among the enzymes that are involved in the ubiquitin (Ub) signaling cascade, tremendous efforts have been focused on elucidating the roles of E3 Ub ligases as they determine the complexity and specificity of ubiquitination. Not surprisingly, the malfunction of E3 ligases is directly implicated in many human diseases, including cancer. Therefore, there is an urgent need to develop potent and specific molecules to modulate E3 ligase activity as intracellular probes for target validation and as pharmacological agents in preclinical research. Unfortunately, the progress has been hampered by the dynamic regulation mechanisms for different types of E3 ligases. Here, we summarize the progress of using protein engineering to develop Ub variant (UbV) inhibitors for all major families of E3 ligases and UbV activators for homologous with E6-associated protein C terminus E3s and homodimeric RING E3s. We believe that this provides a general strategy and a valuable toolkit for the research community to inhibit or activate E3 ligases and these synthetic molecules have important implications in exploring protein degradation for drug discovery.
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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".