Two Residues in the Anticodon Recognition Domain of the Aspartyl-tRNA Synthetase from <i>Pseudomonas aeruginosa</i> Are Individually Implicated in the Recognition of tRNA <sup>Asn</sup>
Bibliographic record
Abstract
In many organisms, the formation of asparaginyl-tRNA is not done by direct aminoacylation of tRNA(Asn) but by specific tRNA-dependent transamidation of aspartyl-tRNA(Asn). This transamidation pathway involves a nondiscriminating aspartyl-tRNA synthetase (AspRS) that charges both tRNA(Asp) and tRNA(Asn) with aspartic acid. Recently, it has been shown for the first time in an organism (Pseudomonas aeruginosa PAO1) that the transamidation pathway is the only route of synthesis of Asn-tRNA(Asn) but does not participate in Gln-tRNA(Gln) formation. P. aeruginosa PAO1 has a nondiscriminating AspRS. We report here the identification of two residues in the anticodon recognition domain (H31 and G83) which are implicated in the recognition of tRNA(Asn). Sequence comparisons of putative discriminating and nondiscriminating AspRSs (based on the presence or absence of the AdT operon and of AsnRS) revealed that bacterial nondiscriminating AspRSs possess a histidine at position 31 and usually a glycine at position 83, whereas discriminating AspRSs possess a leucine at position 31 and a residue other than a glycine at position 83. Mutagenesis of these residues of P. aeruginosa AspRS from histidine to leucine and from glycine to lysine increased the specificity of tRNA(Asp) charging over that of tRNA(Asn) by 3.5-fold and 4.2-fold, respectively. Thus, we show these residues to be determinants of the relaxed specificity of this nondiscriminating AspRS. Using available crystallographic data, we found that the H31 residue could interact with the central bases of the anticodons of the tRNA(Asp) and tRNA(Asn). Therefore, these two determinants of specificity of P. aeruginosa AspRS could be important for all bacterial AspRSs.
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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 | 0.000 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".