Cofactor-Independent Amino Acid Epimerases with Catalytic Serines Instead of Cysteines
Bibliographic record
Abstract
D-amino acids play important roles in nature and are often produced from their L-stereoisomers by racemase or epimerase enzymes. One interesting class of amino acid racemases and epimerases are the cofactor-independent enzymes, which rely on a pair of active site cysteine residues for catalysis in an unusual chemical mechanism with seemingly mismatched acidity values. One classic example of these enzymes is diaminopimelic acid epimerase (DapF-CC), which produces D,L-diaminopimelic acid (DAP) as the penultimate step in lysine biosynthesis in most bacteria and photosynthetic organisms, and for Gram-negative bacterial peptidoglycan. In this work, we characterized for the first time enzymes of the cofactor-independent racemase and epimerase class that use paired catalytic serines (DapF-SS) instead of cysteines. DapF-SS enzymes catalyze reversible epimerization of DAP with similar kinetic parameters to that of DapF-CC enzymes. Sequence alignment and structural models suggest DapF-SS to have high homology to DapF-CC, and biochemical characterization provides evidence for a similar two-base mechanism. However, mutation of catalytic serine(s) to cysteine(s) nearly abolished activity, suggesting that these enzymes are not the result of simple mutations. A sequence similarity network identified thousands of other predicted DapF-SS enzymes from diverse bacterial phyla. Expression and isolation of several of these other enzymes found two with so far unidentified substrate(s), suggesting the Ser-Ser active site architecture may not be limited to just DAP epimerases. DapF-SS is active under oxidative conditions, while DapF-CC enzymes are inactivated by disulfide bond formation, providing a possible explanation as to why this second type of cofactor-independent epimerase evolved.
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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 | 0.001 |
| 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".