Structurally diverse dehydroshikimate dehydratase variants participate in microbial quinate catabolism
Bibliographic record
Abstract
Quinate and shikimate can be degraded by a number of microbes. Dehydroshikimate dehydratases (DSDs) play a central role in this process, catalyzing the conversion of 3-dehydroshikimate to protocatechuate, a common intermediate of aromatic degradation pathways. DSDs have applications in metabolic engineering for the production of valuable protocatechuate-derived molecules. Although a number of Gram-negative bacteria are known to catabolize quinate and shikimate, only limited information exists on the quinate/shikimate catabolic enzymes found in these organisms. Here, we have functionally and structurally characterized a putative DSD designated QuiC1, which is present in some pseudomonads. The QuiC1 protein is not related by sequence with previously identified DSDs from the Gram-negative genus, Acinetobacter, but instead shows limited sequence identity in its N-terminal half with fungal DSDs. Analysis of a Pseudomonas aeruginosa quiC1 gene knock-out demonstrates that it is important for growth on either quinate or shikimate. The structure of a QuiC1 enzyme from P. putida reveals that the protein is a fusion of two distinct modules: an N-terminal sugar phosphate isomerase-like domain associated with DSD activity and a novel C-terminal hydroxyphenylpyruvate dioxygenase-like domain. The results of this study highlight the considerable diversity of enzymes that participate in quinate/shikimate catabolism in different microbes.
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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) | 0.000 | 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) | 0.001 | 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".