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Record W4399865664 · doi:10.1016/j.jbc.2024.107488

RNA modifying enzymes shape tRNA biogenesis and function

2024· review· en· W4399865664 on OpenAlexafffund
Sarah K. Schultz, Ute Kothe

Bibliographic record

VenueJournal of Biological Chemistry · 2024
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRNA modifications and cancer
Canadian institutionsUniversity of Lethbridge
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsTransfer RNABiogenesisRNAEnzymeFunction (biology)BiochemistryBiologyChemistryComputational biologyCell biologyGene

Abstract

fetched live from OpenAlex

Transfer RNAs (tRNAs) are the most highly modified cellular RNAs, both with respect to the proportion of nucleotides that are modified within the tRNA sequence and with respect to the extraordinary diversity in tRNA modification chemistry. However, the functions of many different tRNA modifications are only beginning to emerge. tRNAs have two general clusters of modifications. The first cluster is within the anticodon stem-loop including several modifications essential for protein translation. The second cluster of modifications is within the tRNA elbow, and roles for these modifications are less clear. In general, tRNA elbow modifications are typically not essential for cell growth, but nonetheless several tRNA elbow modifications have been highly conserved throughout all domains of life. In addition to forming modifications, many tRNA modifying enzymes have been demonstrated or hypothesized to also play an important role in folding tRNA acting as tRNA chaperones. In this review, we summarize the known functions of tRNA modifying enzymes throughout the lifecycle of a tRNA molecule, from transcription to degradation. Thereby, we describe how tRNA modification and folding by tRNA modifying enzymes enhance tRNA maturation, tRNA aminoacylation, and tRNA function during protein synthesis, ultimately impacting cellular phenotypes and disease.

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 score0.988
Threshold uncertainty score0.811

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
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 head0.068
GPT teacher head0.323
Teacher spread0.255 · 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

Citations36
Published2024
Admission routes2
Has abstractyes

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