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Record W3193899231 · doi:10.1073/pnas.2110797118

Bacterial translation machinery for deliberate mistranslation of the genetic code

2021· article· en· W3193899231 on OpenAlexaff
Oscar Vargas‐Rodriguez, Ahmed H. Badran, Kyle Hoffman, Man‐Yun Chen, Ana Crnković, Yousong Ding, Jonathan R. Krieger, Éric Westhof, Dieter Söll, Sergey Melnikov

Bibliographic record

VenueProceedings of the National Academy of Sciences · 2021
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRNA and protein synthesis mechanisms
Canadian institutionsBioinformatics Solutions (Canada)
FundersNational Institute of General Medical SciencesNational Institutes of HealthRoyal Society
KeywordsGenetic codeTransfer RNAAminoacyl tRNA synthetaseENCODETranslation (biology)Extant taxonGeneBiologyGenomeGeneticsAmino Acyl-tRNA SynthetasesComputational biologyRNAEvolutionary biologyMessenger RNA

Abstract

fetched live from OpenAlex

Significance Aminoacyl-transfer RNA (tRNA) synthetases (aaRSs) are essential enzymes that mediate accurate expression of the genetic code. More than 96% of analyzed species possess duplicated aaRSs genes, which are presumed to encode canonical aaRSs. However, in this study, we show that some of these seemingly canonical aaRSs are in fact repurposed for deliberate mistranslation of the genetic code, enabling organisms to encode two amino acids using a single sense codon. This finding, along with the abundance of duplicated aaRSs genes, suggests that the deliberate mistranslation of sense codons may be more common than previously thought. More broadly, our work illustrates that extant genomes have many exciting functionalities that are currently hidden under a disguise of canonical tRNA synthetases and tRNAs.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.003

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 head0.039
GPT teacher head0.288
Teacher spread0.250 · 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 source (direct Gemma or distilled Codex), 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

Citations18
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueProceedings of the National Academy of Sciences→Same topicRNA and protein synthesis mechanisms→French-language works237,207→