Understanding ribosome assembly: the structure of in vivo assembled immature 30S subunits revealed by cryo-electron microscopy
Bibliographic record
Abstract
Four decades after early in vitro assembly studies demonstrated that ribosome assembly is a controlled process, our understanding of ribosome assembly is still incomplete. Just as structure determination has been so important to understanding ribosome function, so too will it be critical to sorting out the assembly process. Here, we used a viable deletion in the yjeQ gene, a recognized ribosome assembly factor, to isolate and structurally characterize immature 30S subunits assembled in vivo. These small ribosome subunits contained unprocessed 17S rRNA and lacked some late ribosomal proteins. Cryo-electron microscopy reconstructions revealed that the presence of precursor sequences in the rRNA induces a severe distortion in the 3' minor domain of the subunit involved in the decoding of mRNA and interaction with the large ribosome subunit. These findings suggest that rRNA processing events induce key local conformational changes directing the structure toward the mature assembly. We concluded that rRNA processing, folding, and the entry of tertiary r-proteins are interdependent events in the late stages of 30S subunit assembly. In addition, we demonstrate how studies of emerging assembly factors in ribosome biogenesis can help to elucidate the path of subunit assembly in vivo.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".