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Record W3037495288 · doi:10.1093/nar/gkaa548

The methyltransferase TrmA facilitates tRNA folding through interaction with its RNA-binding domain

2020· article· en· W3037495288 on OpenAlexafffund
Laura Carole Keffer-Wilkes, Emily F Soon, Ute Kothe

Bibliographic record

VenueNucleic Acids Research · 2020
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRNA modifications and cancer
Canadian institutionsUniversity of Lethbridge
FundersNatural Sciences and Engineering Research Council of CanadaAlberta InnovatesUniversity of Lethbridge
KeywordsBiologyTransfer RNARNAMethyltransferaseFolding (DSP implementation)GeneticsProtein foldingComputational biologyEvolutionary biologyCell biologyDNAGene

Abstract

fetched live from OpenAlex

tRNAs are the most highly modified RNAs in all cells, and formation of 5-methyluridine (m5U) at position 54 in the T arm is a common RNA modification found in all tRNAs. The m5U modification is generated by the methyltransferase TrmA. Here, we test and prove the hypothesis that Escherichia coli TrmA has dual functions, acting both as a methyltransferase and as a tRNA chaperone. We identify two conserved residues, F106 and H125, in the RNA-binding domain of TrmA, which interact with the tRNA elbow and are critical for tRNA binding. Co-culture competition assays reveal that the catalytic activity of TrmA is important for cellular fitness, and that substitutions of F106 or H125 impair cellular fitness. We directly show that TrmA enhances tRNA folding in vitro independent of its catalytic activity. In conclusion, our study suggests that F106 and H125 in the RNA-binding domain of TrmA act as a wedge disrupting tertiary interactions between tRNA's D arm and T arm; this tRNA unfolding is the mechanistic basis for TrmA's tRNA chaperone activity. TrmA is the second tRNA modifying enzyme next to the pseudouridine synthase TruB shown to act as a tRNA chaperone supporting a functional link between RNA modification and folding.

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)0.001
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.286
Threshold uncertainty score0.465

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.080
GPT teacher head0.364
Teacher spread0.284 · 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

Citations43
Published2020
Admission routes2
Has abstractyes

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