The co-chaperone DNAJA2 buffers proteasomal degradation of cytosolic proteins with missense mutations
Bibliographic record
Abstract
Abstract Mutations can result in the loss of a protein’s native function due to protein misfolding, which is generally handled by an intricate protein quality control network. To better understand the triaging mechanisms of misfolded cytosolic proteins, we screened a human mutation library to identify a panel of unstable mutations. The degradation of these mutated cytosolic proteins is largely dependent on the ubiquitin proteasome system. Using BioID proximity labelling, we found that the co-chaperones DNAJA1 and DNAJA2 are key interactors of one of the mutated proteins. Notably, the absence of DNAJA2 increases the turnover of the mutant protein but not of the wild-type protein. Our work indicates that missense mutations in cytosolic proteins can promote interactions with molecular chaperones that normally do not occur. Assessment of the broader panel of cytosolic mutant proteins shows that the co-chaperone DNAJA2 exhibits three distinct behaviours: acting to stabilize solely the mutant, both the wild-type and mutant proteins, or being dispensable. Our work illustrates how distinct elements of the protein homeostasis network are utilized in the presence of a cytosolic misfolded protein. Summary Statement We identified a panel of cytosolic mutant proteins degraded by the proteasome. DNAJA2 is often required to prevent mutant protein turnover, even if it is sometimes dispensable for the wild-type protein.
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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 | 0.001 |
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".