Efficient Unnatural Protein Production by Pyrrolysyl-tRNA Synthetase With Genetically Fused Solubility Tags
Bibliographic record
Abstract
Introducing non-canonical amino acids (ncAAs) by engineered orthogonal pairs of aminoacyl-tRNA synthetases and tRNAs has proven to be a highly useful tool for the expansion of the genetic code. Pyrrolysyl-tRNA synthetase (PylRS) from methanogenic archaeal and bacterial species is particularly attractive due to its natural orthogonal reactivity in bacterial and eukaryotic cells. However, the scope of such a reprogrammed translation is often limited, due to low yields of chemically modified target protein. This can be the result of substrate specificity engineering, which decreases the aminoacyl-tRNA synthetase stability and reduces the abundance of active enzyme. We show that the solubility and folding of these engineered enzymes can become a bottleneck for the production of ncAA-containing proteins in vivo. Solubility tags derived from various species provide a strategy to remedy this issue. We find the N terminal fusion of the small metal binding protein from Nitrosomonas europaea to the PylRS sequence to improve enzyme solubility and to boost orthogonal translation efficiency. Our strategy enhances the production of site-specifically labelled proteins with a variety of engineered PylRS variants by 200-540 %, and further allows triple labeling. Even the wild-type enzyme gains up to 245 % efficiency for established ncAA substrates.
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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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".