N‐terminal lysines are essential for protein translocation via a modified <scp>ERAD</scp> system in complex plastids
Bibliographic record
Abstract
Nuclear-encoded pre-proteins being imported into complex plastids of red algal origin have to cross up to five membranes. Thereby, transport across the second outermost or periplastidal membrane (PPM) is facilitated by SELMA (symbiont-specific ERAD-like machinery), an endoplasmic reticulum-associated degradation (ERAD)-derived machinery. Core components of SELMA are enzymes involved in ubiquitination (E1-E3), a Cdc48 ATPase complex and Derlin proteins. These components are present in all investigated organisms with four membrane-bound complex plastids of red algal origin, suggesting a ubiquitin-dependent translocation process of substrates mechanistically similar to the process of retro-translocation in ERAD. Even if, according to the current model, translocation via SELMA does not end up in the classical poly-ubiquitination, transient mono-/oligo-ubiquitination of pre-proteins might be required for the mechanism of translocation. We investigated the import mechanism of SELMA and were able to show that protein transport across the PPM depends on lysines in the N-terminal but not in the C-terminal part of pre-proteins. These lysines are predicted to be targets of ubiquitination during the translocation process. As proteins lacking the N-terminal lysines get stuck in the PPM, a 'frozen intermediate' of the translocation process could be envisioned and initially characterized.
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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".