An Engineered <i>Escherichia coli</i> Strain with Synthetic Metabolism for in‐Cell Production of Translationally Active Methionine Derivatives
Bibliographic record
Abstract
In the last decades, it has become clear that the canonical amino acid repertoire codified by the universal genetic code is not up to the needs of emerging biotechnologies. For this reason, extensive genetic code re-engineering is essential to expand the scope of ribosomal protein translation, leading to reprogrammed microbial cells equipped with an alternative biochemical alphabet to be exploited as potential factories for biotechnological purposes. The prerequisite for this to happen is a continuous intracellular supply of noncanonical amino acids through synthetic metabolism from simple and cheap precursors. We have engineered an Escherichia coli bacterial system that fulfills these requirements through reconfiguration of the methionine biosynthetic pathway and the introduction of an exogenous direct trans-sulfuration pathway. Our metabolic scheme operates in vivo, rescuing intermediates from core cell metabolism and combining them with small bio-orthogonal compounds. Our reprogrammed E. coli strain is capable of the in-cell production of l-azidohomoalanine, which is directly incorporated into proteins in response to methionine codons. We thereby constructed a prototype suitable for economic, versatile, green sustainable chemistry, pushing towards enzyme chemistry and biotechnology-based production.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".