Huib32: A Potent and Selective USP32 Inhibitor Modulating Endosomal Processes and Advancing Cell-Permeable USP32 Probes
Bibliographic record
Abstract
ABSTRACT Deubiquitinating enzymes (DUBs) are pivotal regulators of ubiquitination, a vital post-translational modification essential for cellular processes. Dysregulated DUB activity disrupts cellular homeostasis, driving diseases like cancer and neurodegeneration. Ubiquitin-specific protease 32 (USP32) has emerged as a promising therapeutic target due to its role in endosomal and autophagosomal dynamics and its association with breast, ovarian, and lung cancers. Here, we describe Huib32 ( H uman de U biquitinase Inhi b itor 32) as a USP32 inhibitor. Cyanimide-containing Huib32 potently and selectively inhibits USP32 by covalently binding to the active site Cys743 in vitro and in cells, enhancing substrate ubiquitination, altering endosomal morphology, and mimicking USP32 depletion. Additionally, we present two activity-based probes (ABPs), Huib32*1 and Huib32*2 , which enable precise detection of USP32 activity and confirm probe selectivity via mass spectrometry. Together, Huib32 and its probes represent a unique approach for targeting USP32, offering new research tools and potential therapeutic avenues for cancer and disorders involving endocytic trafficking.
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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".