Engineering Pyrrolysyl-tRNA Synthetase for the Incorporation of Non-Canonical Amino Acids with Smaller Side Chains
Bibliographic record
Abstract
Site-specific incorporation of non-canonical amino acids (ncAAs) into proteins has emerged as a universal tool for systems bioengineering at the interface of chemistry, biology, and technology. The diversification of the repertoire of the genetic code has been achieved for amino acids with long and/or bulky side chains equipped with various bioorthogonal tags and useful spectral probes. Although ncAAs with relatively small side chains and similar properties are of great interest to biophysics, cell biology, and biomaterial science, they can rarely be incorporated into proteins. To address this gap, we report the engineering of PylRS variants capable of incorporating an entire library of aliphatic “small-tag” ncAAs. In particular, we performed mutational studies of a specific PylRS, designed to incorporate the shortest non-bulky ncAA (S-allyl-l-cysteine) possible to date and based on this knowledge incorporated aliphatic ncAA derivatives. In this way, we have not only increased the number of translationally active “small-tag” ncAAs, but also determined key residues responsible for maintaining orthogonality, while engineering the PylRS for these interesting substrates. Based on the known plasticity of PylRS toward different substrates, our approach further expands the reassignment capacities of this enzyme toward aliphatic amino acids with smaller side chains endowed with valuable functionalities.
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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 | 0.000 | 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".