Detection of prokaryotic-like ribosome exit tunnels within eukaryotic kingdoms
Bibliographic record
Abstract
Abstract The ribosome exit tunnel is a critical sub-compartment that actively regulates the folding and dynamics of nascent polypeptide chains during protein translation. In this study, we systematically examined tunnel structures of 725 ribosome models obtained through cryo-EM and X-ray crystallography, to quantify structural variations across different species and biological domains. Hierarchical clustering revealed significant geometric differences between prokaryotic and eukaryotic ribosomes, with a surprising discovery: six eukaryotic protist species display tunnel structures remarkably similar to those of archaea and bacteria. By analyzing the sequences and structures of ribosomal components forming the tunnel walls, we identified four specific sequence modifications in ribosomal proteins and ribosomal RNAs (rRNA) responsible for these unique geometric variations, and detected these modifications in additional protist species lacking existing 3D structural data. Overall, our findings highlights some complex evolutionary mechanisms governing ribosomal protein and large subunit rRNA, providing novel insights into the tunnel’s regulatory role in protein translation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".