Construction and optimization of <i>trans</i>‐4‐hydroxy‐L‐proline production recombinant <i>E. coli</i> strain taking the glycerol as carbon source
Bibliographic record
Abstract
Abstract BACKGROUND Trans‐4‐hydroxy‐L‐proline (Hyp) is a value‐added amino acid that is an applicable chiral building block in some industries, such as the pharmaceutical, cosmetic and healthcare industries. Given that glycerol is a good carbon source for microbial fermentation and a large amount of glycerol is produced by the biodiesel industry, this study investigated a value‐added Hyp fermentation from glycerol using an engineered Escherichia coli strain. RESULTS By optimizing the proline‐4‐hydroxylase (P4H) gene codons and vectors, a constructed recombinant Escherichia coli BL21 strain successfully converted L‐proline to Hyp. With a chemo‐physical combination mutagenesis, a high Hyp production strain that can grow with glycerol as a sole carbon source, NA45 was isolated, yielding 1.24 g L−1 from 20 g L−1 glycerol at 12 h in a 30 mL/250 mL conical flask fermentation. With further systemic optimization of nutritional elements, the output was enhanced to 1.62 g L−1. In a 5 L fermentator, the strain achieved a surprisingly high output of 25.4 g L−1 at 48 h in fed batch mode. CONCLUSION A high Hyp production strain growing well on glycerol as carbon source was successfully engineered, which is a promising candidate for the industrial Hyp production. © 2016 Society of Chemical Industry
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".