Prefoldin Promotes Proteasomal Degradation of Cytosolic Proteins with Missense Mutations by Maintaining Substrate Solubility
Bibliographic record
Abstract
Misfolded proteins challenge the ability of cells to maintain protein homeostasis and can accumulate into toxic protein aggregates. As a consequence, cells have adopted a number of protein quality control pathways to prevent protein aggregation, promote protein folding, and target terminally misfolded proteins for degradation. In this study, we employed a thermosensitive allele of the yeast Guk1 guanylate kinase as a model misfolded protein to investigate degradative protein quality control pathways. We performed a flow cytometry based screen to identify factors that promote proteasomal degradation of proteins misfolded as the result of missense mutations. In addition to the E3 ubiquitin ligase Ubr1, we identified the prefoldin chaperone subunit Gim3 as an important quality control factor. Whereas the absence of GIM3 did not impair proteasomal function or the ubiquitination of the model substrate, it led to the accumulation of the poorly soluble model substrate in cellular inclusions that was accompanied by delayed degradation. We found that Gim3 interacted with the Guk1 mutant allele and propose that prefoldin promotes the degradation of the unstable model substrate by maintaining the solubility of the misfolded protein. We also demonstrated that in addition to the Guk1 mutant, prefoldin can stabilize other misfolded cytosolic proteins containing missense mutations.
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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".