Expanding the Scope of Orthogonal Translation with Pyrrolysyl-tRNA Synthetases Dedicated to Aromatic Amino Acids
Bibliographic record
Abstract
In protein engineering and synthetic biology, Methanosarcina mazei pyrrolysyl-tRNA synthetase (MmPylRS), with its cognate tRNAPyl, is one of the most popular tools for site-specific incorporation of non-canonical amino acids (ncAAs). Numerous orthogonal pairs based on engineered MmPylRS variants have been developed during the last decade, enabling a substantial genetic code expansion, mainly with aliphatic pyrrolysine analogs. However, comparatively less progress has been made to expand the substrate range of MmPylRS towards aromatic amino acid residues. Therefore, we set to further expand the substrate scope of orthogonal translation by a semi-rational approach; redesigning the MmPylRS efficiency. Based on the randomization of residues from the binding pocket and tRNA binding domain, we identify three positions (V401, W417 and S193) crucial for ncAA specificity and enzyme activity. Their systematic mutagenesis enabled us to generate MmPylRS variants dedicated to tryptophan (such as β-(1-Azulenyl)-l-alanine or 1-methyl-l-tryptophan) and tyrosine (mainly halogenated) analogs. Moreover, our strategy also significantly improves the orthogonal translation efficiency with the previously activated analog 3-benzothienyl-l-alanine. Our study revealed the engineering of both first shell and distant residues to modify substrate specificity as an important strategy to further expand our ability to discover and recruit new ncAAs for orthogonal translation
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".