Chemical composition, sequence context, and base-pairing potential of posttranscriptional modifications at the wobble position of the tRNA anticodon loop
Bibliographic record
Abstract
It is well accepted that RNA is frequently and diversely modified, with the anticodon stem loop of transfer RNA (tRNA) containing the largest number and types of chemical substitutions. Nevertheless, the roles many modifications play in cells remain unclear. The present study consolidates and expands our current knowledge of tRNA modifications at position 34 (wobble position) using a range of bioinformatics and computational techniques. Sequence analysis of 474 tRNAs clarifies the position 34 modifications identified to date at each parent nucleotide across all domains of life. Subsequent analysis of 1291 cryo-EM or X-ray crystal structures of ribosomal complexes led to the curation of a dataset of 468 high-resolution structures of position 34 base-pair interactions with messenger RNA (mRNA). Despite highlighting that structural information is scarce for several canonical base-pairing combinations and nucleotide modifications, the structural data hint that modifications can have differential impact on the base pairing at position 34. Due to limited experimental structural data for position 34 modifications, density functional theory calculations were used to characterize 120 pairs involving canonical and/or modified nucleobases, revealing that some chemical substituents do not impact base-pairing properties of parent nucleotides regardless of modification size, while others slightly alter inherent base pairing or afford completely new base-pairing properties to fine-tune tRNA–mRNA interactions. Overall, consolidation of previous and newly-generated data suggests that position 34 modifications likely regulate translation in several ways and underscores the importance of incorporating computational analyses in the future analysis pipeline as modifications are identified.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".