Structural determinants of an internal ribosome entry site that direct translational reading frame selection
Bibliographic record
Abstract
The dicistrovirus intergenic internal ribosome entry site (IGR IRES) directly recruits the ribosome and initiates translation using a non-AUG codon. A subset of IGR IRESs initiates translation in either of two overlapping open reading frames (ORFs), resulting in expression of the 0 frame viral structural polyprotein and an overlapping +1 frame ORFx. A U-G base pair adjacent to the anticodon-like pseudoknot of the IRES directs +1 frame translation. Here, we show that the U-G base pair is not absolutely required for +1 frame translation. Extensive mutagenesis demonstrates that 0 and +1 frame translation can be uncoupled. Ribonucleic acid (RNA) structural probing analyses reveal that the mutant IRESs adopt distinct conformations. Toeprinting analysis suggests that the reading frame is selected at a step downstream of ribosome assembly. We propose a model whereby the IRES adopts conformations to occlude the 0 frame aminoacyl-tRNA thereby allowing delivery of the +1 frame aminoacyl-tRNA to the A site to initiate translation of ORFx. This study provides a new paradigm for programmed recoding mechanisms that increase the coding capacity of a viral genome.
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) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".