Oligomeric scaffolding for curvature generation by ER tubule-forming proteins
Bibliographic record
Abstract
The reticulons and receptor expression-enhancing proteins (REEPs) in the endoplasmic reticulum (ER) are necessary and sufficient for generating ER tubules. However, the mechanism of curvature generation remains elusive. Here, we systematically analyze components of the REEP family based on AI-predicted structures. In yeast REEP Yop1p, TM1/2 and TM3/4 form hairpins and TM2-4 exist as a bundle. Site-directed cross-linking reveals that TM2 and TM4 individually mediate homotypic dimerization, allowing further assembly into a curved shape. Truncated Yop1p lacking TM1 (equivalent to REEP1) retains the curvature-generating capability, undermining the role of the intrinsic wedge. Unexpectedly, both REEP1 and REEP5 fail to replace Yop1p in the maintenance of ER morphology, mostly due to a subtle difference in oligomerization tendency, which involves not only the TM domains, but also the TM-connecting cytosolic loop and previously neglected C-terminal helix. Several hereditary spastic paraplegia-causing mutations in REEP1 appear at the oligomeric interfaces identified here, suggesting compromised self-association of REEP as a pathogenic mechanism. These results indicate that membrane curvature stabilization by integral membrane proteins is dominantly achieved by curved, oligomeric scaffolding.
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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) | 0.000 | 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".