An Atlas of the base inter-RNA stacks involved in bacterial translation
Bibliographic record
Abstract
Nucleobase-specific noncovalent interactions (i.e., inter-RNA base pairing and stacking) play a crucial role in the RNA-driven biochemical process of translation. Although the structural details of translation are well studied, especially base-pairing, the role of nucleobase-specific inter-RNA stacking interactions is still not fully appreciated. Herein, we provide a comprehensive analysis of the stacking interactions between different RNA components in the available crystal structures of the bacterial ribosome caught at different stages of translation. Analysis of tRNA||rRNA stacking interactions reveals distinct stacking behaviour; both the A-and E-site tRNAs exhibit unique stacking patterns with 23S rRNA bases, while P-site tRNAs stack with 16S rRNA bases. Furthermore, E-site stacks exhibit diverse face orientations and ring topologies―rare for inter-chain RNA interactions―with higher average interaction energies than found in either A or P-site rRNA||tRNA stacks. This suggests that stacking may be essential for stabilizing tRNA progression to and through the E-site. Additionally, examination of mRNA||rRNA stacking interactions reveals other stacking geometries, which depend on the site of tRNA binding; A-site mRNA||rRNA stacks exhibit more frequent interactions with high stability, suggesting they play an essential role in mRNA positioning within the translational complex. Similarly, analysis of 16S rRNA||23S rRNA stacks highlights the importance of specific bases in maintaining the integrity of the translational complex by linking the two rRNAs. Furthermore, tRNA||mRNA stacking interactions exhibit distinct geometries and energetics at the E-site, indicating their significance during tRNA translocation and elimination. Overall, the analysis demonstrates that both A and E-sites display a broader and more diverse distribution of inter-RNA stacking interactions compared to the P-site. Notably, P-site stacking interactions are the least stable, suggesting their diminished role due to the need for interactions to be dominated by codon:anticodon base pairing to avoid misreading the mRNA. Stacking interactions in the active ribosome are not simply accidental byproducts of biochemistry but are invoked to compensate and support the integrity and dynamics of 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".