Targeted degradation of USP7 in solid cancer cells reveals disparate effects of deubiquitinase inhibition vs. acute protein depletion
Bibliographic record
Abstract
Abstract Proteolysis-targeting chimeras (PROTACs) co-op the ubiquitin system for targeted protein degradation, creating opportunities to interrogate cellular functions of proteins through “chemical knockdown”. However, matched pairs of protein degraders and inhibitors, that possess high specificity and chemical complementarity, for individual components of the ubiquitin system have remained scarce. This includes reagents to modulate activity and abundance of deubiquitinases (DUBs), which critically regulate ubiquitin-mediated signaling. Here, using an integrated chemical biology approach, we explored the cellular function of the DUB USP7 as a case study comparing inhibition and degradation of this DUB in melanoma and pancreatic cancer cells. Through the synthesis of a degrader library, we identified potent USP7 PROTACs for each cancer type, established BRET-based ternary complex formation and quantified degradation efficiency. USP7 degraders and their cognate inhibitor were subsequently employed to characterize treatment-induced phenotypic alterations. Proteomic and cellular analyses revealed that highly specific degradation of USP7 modulated both shared and distinct protein sets across cancer cell types, without impacting cell growth. Notably, cellular responses to USP7 degradation differed markedly from those to USP7 inhibition. Moreover, our data uncovered broad proteomic and metabolic changes induced by prolonged USP7 inhibitor treatment. Collectively, our work provides a chemical toolbox of comprehensively characterized reagents to distinguish on-target phenotypes which will aid the understanding of the role of USP7 in malignant diseases. More broadly, our data emphasize the importance of increased specificity via PROTAC-mediated degradation and the potential of this modality to distinguish catalytic from non-catalytic as well as cell-line specific functions of DUBs.
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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".