First‐in‐class Deubiquitylase Inhibitors Reveal New Enzyme Conformations
Bibliographic record
Abstract
Cells maintain protein homeostasis by adding a small protein, ubiquitin, to regulate a variety of cellular processes, dictating protein activity, localisation or degradation. The addition of ubiquitin, known as ubiquitylation, is a reversible process making it a versatile post‐translational modification aptly suited for cell signalling. Removal of ubiquitin is catalysed by deubiquitylating enzymes, commonly referred to as DUBs. BRCC36 isopeptidase complex (BRISC) is a multi‐protein DUB complex which hydrolyses lysine‐63‐linked ubiquitin chains on Type I interferon receptors (IFNAR1/2), thus regulating interferon‐dependent signalling. Therefore, BRISC‐mediated inflammatory signalling amplification is a promising target for autoimmune disease drug development. We performed a high‐throughput screen to identify small molecules which inhibit BRISC enzymatic activity. Employing an integrative structural biology approach (cryo‐electron microscopy, native mass spectrometry, hydrogen‐deuterium exchange mass spectrometry), complemented with biochemical assays, we have uncovered new enzyme conformations, revealing a remarkable mode of action for BRISC inhibitors. Exploring these key mechanisms will expand current knowledge of inflammatory signalling pathways and establish the use of DUB inhibitors as therapeutics to combat autoimmune disease and hyperactive cytokine signalling.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".