Discovery of functional factorless internal ribosome entry site-like structures through virome mining
Bibliographic record
Abstract
All viruses must co-opt the host translational machinery for viral protein synthesis. The dicistrovirus intergenic region internal ribosome entry site (IGR-IRES) utilizes the most streamlined translation mechanism by adopting a triple pseudoknot structure that directly recruits and binds within the intersubunit space of the ribosome and initiates translation from a non-AUG codon. The origin of this unprecedented mechanism is not known. Using a bioinformatics pipeline to examine the diversity and function of IRESs across RNA viromes, we searched for IRES-like RNA structures using RNA covariance models for multiple IRES sub-types, and tested functional IRES by using a dual-fluorescent lentiviral library reporter screen. We identified over >4,700 dicistro-like genomes with ~32% containing putative IRES structures, including novel viral genome arrangements with multiple IRESs and IRESs embedded within open-reading frames (ORFs). Predicted IRESs bound directly to purified ribosomes and supported internal ribosome entry activity in vitro and in vivo. Moreover, internal IRESs embedded within an ORF of monocistronic genomes were functional and operated simultaneously to produce the downstream ORF. We also identified IRES-like structures within non-dicistrovirus viral genomes, including in the families Tombusviridae and Narnaviridae that bound to ribosomes directly and a subset can direct internal ribosome entry. This study provides a framework to map the origin of factorless IRES mechanisms and study the diverse viral strategies utilizing RNA-based mechanisms.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".