Ribosome-associated quality control of membrane proteins at the endoplasmic reticulum
Bibliographic record
Abstract
Protein synthesis is an energetically costly, complex and risky process. Aberrant protein biogenesis can result in cellular toxicity and disease, with membrane-embedded proteins being particularly challenging for the cell. In order to protect the cell from consequences of defects in membrane proteins, quality control systems act to maintain protein homeostasis. The majority of these pathways act post-translationally; however, recent evidence reveals that membrane proteins are also subject to co-translational quality control during their synthesis in the endoplasmic reticulum (ER). This newly identified quality control pathway employs components of the cytosolic ribosome-associated quality control (RQC) machinery but differs from canonical RQC in that it responds to biogenesis state of the substrate rather than mRNA aberrations. This ER-associated RQC (ER-RQC) is sensitive to membrane protein misfolding and malfunctions in the ER insertion machinery. In this Review, we discuss the advantages of co-translational quality control of membrane proteins, as well as potential mechanisms of substrate recognition and degradation. Finally, we discuss some outstanding questions concerning future studies of ER-RQC of membrane proteins.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".