On the Study of Deubiquitinases: Using the Right Tools for the Job
Bibliographic record
Abstract
Deubiquitinases (DUBs) have been the subject of intense scrutiny in recent years. Many of their diverse enzymatic mechanisms are well characterized in vitro; however, our understanding of these enzymes at the cellular level lags due to the lack of quality tool reagents. DUBs play a role in seemingly every biological process and are central to many human pathologies, thus rendering them very desirable and challenging therapeutic targets. This review aims to provide researchers entering the field of ubiquitination with knowledge of the pharmacological modulators and tool molecules available to study DUBs. A focus is placed on small molecule inhibitors, ubiquitin variants (UbVs), and activity-based probes (ABPs). Leveraging these tools to uncover DUB biology at the cellular level is of particular importance and may lead to significant breakthroughs. Despite significant drug discovery efforts, only approximately 15 chemical probe-quality small molecule inhibitors have been reported, hitting just 6 of about 100 DUB targets. UbV technology is a promising approach to rapidly expand the library of known DUB inhibitors and may be used as a combinatorial platform for structure-guided drug design.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".