One-Pot Biocatalytic Transformation of Adipic Acid to 6-Aminocaproic Acid and 1,6-Hexamethylenediamine Using Carboxylic Acid Reductases and Transaminases
Why this work is in the frame
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Bibliographic record
Abstract
Production of platform chemicals from renewable feedstocks is becoming increasingly important due to concerns on environmental contamination, climate change, and depletion of fossil fuels. Adipic acid (AA), 6-aminocaproic acid (6-ACA) and 1,6-hexamethylenediamine (HMD) are key precursors for nylon synthesis, which are currently produced primarily from petroleum-based feedstocks. In recent years, the biosynthesis of adipic acid from renewable feedstocks has been demonstrated using both bacterial and yeast cells. Here we report the biocatalytic conversion/transformation of AA to 6-ACA and HMD by carboxylic acid reductases (CARs) and transaminases (TAs), which involves two rounds (cascades) of reduction/amination reactions (AA → 6-ACA → HMD). Using purified wild type CARs and TAs supplemented with cofactor regenerating systems for ATP, NADPH, and amine donor, we established a one-pot enzyme cascade catalyzing up to 95% conversion of AA to 6-ACA. To increase the cascade activity for the transformation of 6-ACA to HMD, we determined the crystal structure of the CAR substrate-binding domain in complex with AMP and succinate and engineered three mutant CARs with enhanced activity against 6-ACA. In combination with TAs, the CAR L342E protein showed 50-75% conversion of 6-ACA to HMD. For the transformation of AA to HMD (via 6-ACA), the wild type CAR was combined with the L342E variant and two different TAs resulting in up to 30% conversion to HMD and 70% to 6-ACA. Our results highlight the suitability of CARs and TAs for several rounds of reduction/amination reactions in one-pot cascade systems and their potential for the biobased synthesis of terminal amines.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| 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 it