Computational Aminoacyl-tRNA Synthetase Library Design for Photocaged Tyrosine
Bibliographic record
Abstract
Engineering aminoacyl-tRNA synthetases (aaRSs) provides access to the ribosomal incorporation of noncanonical amino acids via genetic code expansion. Conventional targeted mutagenesis libraries with 5–7 positions randomized cover only marginal fractions of the vast sequence space formed by up to 30 active site residues. This frequently results in selection of weakly active enzymes. To overcome this limitation, we use computational enzyme design to generate a focused library of aaRS variants. For aaRS enzyme redesign, photocaged ortho-nitrobenzyl tyrosine (ONBY) was chosen as substrate due to commercial availability and its diverse applications. Diversifying 17 first- and second-shell sites and performing conventional aaRS positive and negative selection resulted in a high-activity aaRS. This MjTyrRS variant carries ten mutations and outperforms previously reported ONBY-specific aaRS variants isolated from traditional libraries. In response to a single in-frame amber stop codon, it mediates the in vivo incorporation of ONBY with an efficiency matching that of the wild type MjTyrRS enzyme acylating cognate tyrosine. These results exemplify an improved general strategy for aaRS library design and engineering.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Bench or experimental | low |
| gpt | no category Domain: not available · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Simulation or modeling | high |
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".