Exploring Bacterial Carboxylate Reductases for the Reduction of Bifunctional Carboxylic Acids
Bibliographic record
Abstract
Carboxylic acid reductases (CARs) selectively reduce carboxylic acids to aldehydes using ATP and NADPH as cofactors under mild conditions. Although CARs attracts significant interest, only a few enzymes have been characterized to date, whereas the vast majority of CARs have yet to be examined. Herein the authors report that 12 bacterial CARs reduces a broad range of bifunctional carboxylic acids containing oxo-, hydroxy-, amino-, or second carboxyl groups with several enzymes showing activity toward 4-hydroxybutanoic (4-HB) and adipic acids. These CARs exhibits significant reductase activity against substrates whose second functional group is separated from the carboxylate by at least three carbons with both carboxylate groups being reduced in dicarboxylic acids. Purified CARs supplemented with cofactor regenerating systems (for ATP and NADPH), an inorganic pyrophosphatase, and an aldo-keto reductase catalyzes a high conversion (50-76%) of 4-HB to 1,4-butanediol (1,4-BDO) and adipic acid to 1,6-hexanediol (1,6-HDO). Likewise, Escherichia coli strains expressing eight different CARs efficiently reduces 4-HB to 1,4-BDO with 50-95% conversion, whereas adipic acid is reduced to a mixture of 6-hydroxyhexanoic acid (6-HHA) and 1,6-HDO. Thus, our results illustrate the broad biochemical diversity of bacterial CARs and their compatibility with other enzymes for applications in biocatalysis.
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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 | 0.000 |
| 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".