Taking a Step Back from Back-Translocation: an Integrative View of LepA/EF4's Cellular Function
Bibliographic record
Abstract
Protein synthesis, the translation of mRNA into a polypeptide facilitated by the ribosome, is assisted by a variety of protein factors, some of which are GTPases. In addition to four highly conserved and well-understood GTPases with known function, there are also a number of noncanonical GTPases that are implicated in translation but whose functions are not fully understood. LepA/EF4 is one of these noncanonical GTPases. It is highly conserved and present in bacteria, mitochondria, and chloroplasts, but its functional role in the cell remains unknown. LepA's sequence and domain arrangement are very similar to those of other translational GTPases, but it contains a unique C-terminal domain (CTD) that is likely essential to its specific function in the cell. Three main hypotheses about the function of LepA have been brought forward to date: (i) LepA is a back-translocase, (ii) LepA relieves ribosome stalling or facilitates sequestration, and (iii) LepA is involved in ribosome biogenesis. This review examines the structural and biochemical information available on bacterial LepA and discusses it on the background of the available in vivo information from higher organisms in order to broaden the view regarding LepA's functional role in the cell and how the structure of its unique CTD might be involved in facilitating this role.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".