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Record W2510217100 · doi:10.1080/15476286.2016.1219833

tRNA-mimicry in IRES-mediated translation and recoding

2016· review· en· W2510217100 on OpenAlexafffund
Samuel E. Butcher, Eric Jan

Bibliographic record

VenueRNA Biology · 2016
Typereview
Languageen
FieldAgricultural and Biological Sciences
TopicPlant Virus Research Studies
Canadian institutionsUniversity of British Columbia
FundersNational Institute of General Medical SciencesCanadian Institutes of Health ResearchNational Institutes of Health
KeywordsBiologyInternal ribosome entry siteORFSOpen reading frameRibosomeGeneticsTranslation (biology)Reading frameTransfer RNAGenomeRibosome profilingRNATranslational frameshiftGeneComputational biologyMessenger RNAPeptide sequence

Abstract

fetched live from OpenAlex

Viruses maintain compact genomes that must be packaged within capsids typically less than 200 nanometers in diameter. Therefore, instead of coding for a full set of genes needed for replication, viruses have evolved remarkable strategies for co-opting the host cellular machinery. Additionally, viruses often increase the coding capacity of their own genomes by employing overlapping open reading frames (ORFs). Some overlapping viral ORFs involve recoding events that are programmed by the viral RNA. During these programmed recoding events, the ribosome is directed to translate in an alternative reading frame. Here we describe how the Dicistroviridae family of viruses utilize an internal ribosome entry site (IRES) in order to recruit ribosomes to initiate translation at a non-AUG codon. The IRES accomplishes this in part by mimicking the structure of a tRNA. Recently, we showed that the Israeli Acute Paralysis Virus (IAPV) member of the Dicistroviridae family utilizes its IRES to initiate translation in 2 different reading frames. Thus, IAPV has evolved an apparently novel recoding mechanism that reveals important insights into translation. Finally, we compare the IAPV structure to other systems that utilize tRNA mimicry in 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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.998
Threshold uncertainty score0.257

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.183
GPT teacher head0.356
Teacher spread0.173 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations19
Published2016
Admission routes2
Has abstractyes

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