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Record W2068970210 · doi:10.1093/nar/gkm578

The three transfer RNAs occupying the A, P and E sites on the ribosome are involved in viral programmed -1 ribosomal frameshift

2007· article· en· W2068970210 on OpenAlexafffund
M.C. Leger, Diane Dulude, Sergey V. Steinberg, Léa Brakier‐Gingras

Bibliographic record

VenueNucleic Acids Research · 2007
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRNA and protein synthesis mechanisms
Canadian institutionsUniversité de Montréal
FundersCanadian Institutes of Health Research
KeywordsBiologyRibosomeRibosomal RNATransfer RNAP-siteGeneticsShine-Dalgarno sequenceFrameshift mutationRibosomal binding siteStop codonA-siteMutagenesisEukaryotic RibosomeMolecular biologyRNAMutationBinding siteGene

Abstract

fetched live from OpenAlex

The -1 programmed ribosomal frameshifts (PRF), which are used by many viruses, occur at a heptanucleotide slippery sequence and are currently thought to involve the tRNAs interacting with the ribosomal P- and A-site codons. We investigated here whether the tRNA occupying the ribosomal E site that precedes a slippery site influences -1 PRF. Using the human immunodeficiency virus type 1 (HIV-1) frameshift region, we found that mutating the E-site codon altered the -1 PRF efficiency. When the HIV-1 slippery sequence was replaced with other viral slippery sequences, mutating the E-site codon also altered the -1 PRF efficiency. Because HIV-1 -1 PRF can be recapitulated in bacteria, we used a bacterial ribosome system to select, by random mutagenesis, 16S ribosomal RNA (rRNA) mutations that modify the expression of a reporter requiring HIV-1 -1 PRF. Three mutants were isolated, which are located in helices 21 and 22 of 16S rRNA, a region involved in translocation and E-site tRNA binding. We propose a novel model where -1 PRF is triggered by an incomplete translocation and depends not only on the tRNAs interacting with the P- and A-site codons, but also on the tRNA occupying the E site.

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)0.005
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.056
Threshold uncertainty score0.673

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.050
GPT teacher head0.317
Teacher spread0.267 · 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 designBench or experimental
Domainnot available
GenreEmpirical

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

Citations75
Published2007
Admission routes2
Has abstractyes

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