Production of methylparaben in <i>Escherichia coli</i>
Bibliographic record
Abstract
Since the 1930s, parabens have been employed widely as preservatives in food, pharmaceutical, and personal care products. These alkyl esters of benzoic acid occur naturally in a broad range of plant species, where they are thought to enhance overall fitness through disease resistance and allelopathy. Current manufacture of parabens relies on chemical synthesis and the processing of 4-hydroxybenzoate as a precursor. A variety of bio-based production platforms have targeted 4-hydroxybenzoate for a greener alternative to chemical manufacturing, but parabens have yet to be made in microbes. Here, we deploy the plant enzyme benzoic acid carboxyl methyltransferase together with four additional recombinant enzymes to produce methylparaben in Escherichia coli. The feasibility of a tyrosine-dependent route to methylparaben is explored, establishing a framework for linking paraben production to emerging high-tyrosine E. coli strains. However, our use of a unique plant enzyme for bio-based methylparaben biosynthesis is potentially applicable to any microbial system engineered for the manufacture of 4-hydroxybenzoate.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".