A search for structurally similar cellular internal ribosome entry sites
Bibliographic record
Abstract
Internal ribosome entry sites (IRES) allow ribosomes to be recruited to mRNA in a cap-independent manner. Some viruses that impair cap-dependent translation initiation utilize IRES to ensure that the viral RNA will efficiently compete for the translation machinery. IRES are also employed for the translation of a subset of cellular messages during conditions that inhibit cap-dependent translation initiation. IRES from viruses like Hepatitis C and Classical Swine Fever virus share a similar structure/function without sharing primary sequence similarity. Of the cellular IRES structures derived so far, none were shown to share an overall structural similarity. Therefore, we undertook a genome-wide search of human 5'UTRs (untranslated regions) with an empirically derived structure of the IRES from the key inhibitor of apoptosis, X-linked inhibitor of apoptosis protein (XIAP), to identify novel IRES that share structure/function similarity. Three of the top matches identified by this search that exhibit IRES activity are the 5'UTRs of Aquaporin 4, ELG1 and NF-kappaB repressing factor (NRF). The structures of AQP4 and ELG1 IRES have limited similarity to the XIAP IRES; however, they share trans-acting factors that bind the XIAP IRES. We therefore propose that cellular IRES are not defined by overall structure, as viral IRES, but are instead dependent upon short motifs and trans-acting factors for their function.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".