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Record W4405652424 · doi:10.1111/febs.17361

Transfer <scp>RNA</scp> and small molecule therapeutics for aminoacyl‐<scp>tRNA</scp> synthetase diseases

2024· review· en· W4405652424 on OpenAlexafffund
Tristan N. Samuels, Fanqi Wu, Maziah Mahmood, Wael Abuzaid, Angélica Moresco, Victoria Mok Siu, Patrick O’Donoghue, Ilka U. Heinemann

Bibliographic record

VenueFEBS Journal · 2024
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRNA and protein synthesis mechanisms
Canadian institutionsChildren’s Health Research InstituteWestern University
FundersOntario Ministry of Research and InnovationNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health Research
KeywordsAminoacyl tRNA synthetaseTransfer RNAGenetic codeAmino acidBiologyTranslation (biology)Nucleic acidRNABiochemistryAmino Acyl-tRNA SynthetasesProtein biosynthesisAminoacylationSmall moleculeGeneMessenger RNA

Abstract

fetched live from OpenAlex

Aminoacyl-tRNA synthetases catalyze the ligation of a specific amino acid to its cognate tRNA. The resulting aminoacyl-tRNAs are indispensable intermediates in protein biosynthesis, facilitating the precise decoding of the genetic code. Pathogenic alleles in the aminoacyl-tRNA synthetases can lead to several dominant and recessive disorders. To date, disease-specific treatments for these conditions are largely unavailable. We review pathogenic human synthetase alleles, the molecular and cellular mechanisms of tRNA synthetase diseases, and emerging approaches to allele-specific treatments, including small molecules and nucleic acid-based therapeutics. Current treatment approaches to rescue defective or dysfunctional tRNA synthetase mutants include supplementation with cognate amino acids and delivery of cognate tRNAs to alleviate bottlenecks in translation. Complementary approaches use inhibitors to target the integrated stress response, which can be dysregulated in tRNA synthetase diseases.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.964
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
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 head0.042
GPT teacher head0.297
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.

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

Citations6
Published2024
Admission routes2
Has abstractyes

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