Ligand-switchable Substrates for a Ubiquitin-Proteasome System
Bibliographic record
Abstract
Cellular maintenance of protein homeostasis is essential for normal cellular function. The ubiquitin-proteasome system (UPS) plays a central role in processing cellular proteins destined for degradation, but little is currently known about how misfolded cytosolic proteins are recognized by protein quality control machinery and targeted to the UPS for degradation in mammalian cells. Destabilizing domains (DDs) are small protein domains that are unstable and degraded in the absence of ligand, but whose stability is rescued by binding to a high affinity cell-permeable ligand. In the work presented here, we investigate the biophysical properties and cellular fates of a panel of FKBP12 mutants displaying a range of stabilities when expressed in mammalian cells. Our findings correlate observed cellular instability to both the propensity of the protein domain to unfold in vitro and the extent of ubiquitination of the protein in the non-permissive (ligand-free) state. We propose a model in which removal of stabilizing ligand causes the DD to unfold and be rapidly ubiquitinated by the UPS for degradation at the proteasome. The conditional nature of DD stability allows a rapid and non-perturbing switch from stable protein to unstable UPS substrate unlike other methods currently used to interrogate protein quality control, providing tunable control of degradation rates.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".