The impact of recent improvements in cryo-electron microscopy technology on the understanding of bacterial ribosome assembly
Bibliographic record
Abstract
Cryo-electron microscopy (cryo-EM) had played a central role in the study of ribosome structure and the process of translation in bacteria since the development of this technique in the mid 1980s. Until recently cryo-EM structures were limited to ∼10 Å in the best cases. However, the recent advent of direct electron detectors has greatly improved the resolution of cryo-EM structures to the point where atomic resolution is now achievable. This improved resolution will allow cryo-EM to make groundbreaking contributions in essential aspects of ribosome biology, including the assembly process. In this review, we summarize important insights that cryo-EM, in combination with chemical and genetic approaches, has already brought to our current understanding of the ribosomal assembly process in bacteria using previous detector technology. More importantly, we discuss how the higher resolution structures now attainable with direct electron detectors can be leveraged to propose precise testable models regarding this process. These structures will provide an effective platform to develop new antibiotics that target this fundamental cellular process.
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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 | 0.001 | 0.001 |
| 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".