Structure of the complete, membrane-assembled COPII coat reveals a complex interaction network
Bibliographic record
Abstract
Abstract The COPII coat mediates Endoplasmic Reticulum (ER) to Golgi trafficking for thousands of proteins. Five essential coat proteins assemble at the ER into a characteristic two-layer architecture, which recruits cargo proteins whilst sculpting membrane carriers with diverse morphologies. How coat architecture drives membrane curvature whilst ensuring morphological plasticity is largely unknown, yet is central to understanding mechanisms of carrier formation. Here, we use an established reconstitution system to visualise the complete, membrane-assembled COPII coat with unprecedented detail by cryo-electron tomography and subtomogram averaging. We discover a network of interactions within and between coat layers, including multiple interfaces that were previously unknown. We reveal the physiological importance of these interactions using genetic and biochemical approaches. A newly resolved Sec31 C-terminal domain provides order to the coat and is essential to drive membrane curvature in cells. Moreover, a novel outer coat assembly mode provides a basis for coat adaptability to varying membrane curvatures. Furthermore, a newly resolved region of Sec23, which we term the L-loop, imparts coat stability and in part dictates membrane shape. Our results suggest these interactions collectively contribute to coat organisation and membrane curvature, providing a structural framework to understand regulatory mechanisms of COPII trafficking and secretion.
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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".