Efficient bioproduction of 3-(4-thiazolyl)-l-alanine, an unnatural amino acid of pharmaceutical importance, by an enzymatic relay process
Bibliographic record
Abstract
As an l -histidine analog, 3-(4-thiazolyl)- l -alanine (TAla) has been attracting increasing biotechnological interest because it can serve as a key building block in the development of many novel therapeutic peptides such as rovatirelin. Although several chemical and enzymatic methods have been reported for TAla production, the use of toxic agents and limitation imposed by a maximum theoretical conversion rate of 50% need to be discreetly considered before large-scale production. Here an enzymatic relay system overcoming these potential drawbacks was developed for efficient TAla production. Five aminoacylases (ACYs) were screened to synthesize TAla from racemic N -acetyl-3-(4-thiazolyl)- dl -alanine via l -enantioselective kinetic resolution. Four ACYs displayed the target activity, and the enzyme DdACY1, which is a novel ACY from Dictyostelium discoideum , was found to be promising due to its high activity and solubility. Using the E. coli whole cells expressing DdACY1 as biocatalysts, the optimal temperature and pH were determined to be 60-65°C and 8.5, respectively. The TAla yield reached approximately 45% under the optimized conditions. By coupling the Deinococcus radiodurans N -acyl-amino acid racemase DrNAAAR with DdACY1 in the same system, the maximum theoretical conversion rate of the reaction was overstepped and a 69.3% yield was achieved. Optimizing the dosage of DrNAAAR and adding 1 mmol/L Zn 2+ or Mg 2+ further improved the catalytic efficiency. In the scaled-up reaction containing 70 mmol/L racemic substrate, 77.1% of the substrate was converted into TAla in 24 h. After product isolation and purification, 0.454 g solid powder of TAla with high chemical (95.2%) and optical (99.9% ee ) purities was obtained. This study established an overall process for TAla bioproduction by coupling a novel ACY and a previously reported NAAAR via dynamic kinetic resolution.
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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) | 0.000 | 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".