Structure and inhibition of diaminopimelic acid epimerase by slow‐binding <i>α</i>‐methyl amino acids
Bibliographic record
Abstract
~ 29) and are of interest not only because of their biocatalytic potential for D-amino acid production, but also because many play key roles in biology and are antibiotic targets. However, obtaining crystal structures of these enzymes, especially in their closed, substrate- or inhibitor-bound conformations, is difficult. In this work, we characterized diaminopimelic acid (DAP) epimerase from the cyanobacterium Anabaena. DAP epimerase has long been of interest as an antibiotic target as it converts L,L-DAP to D,L-DAP for lysine and peptidoglycan biosynthesis. We solved three crystal structures of this enzyme in its closed, inhibitor-bound conformation, up to a resolution of 1.5 Å. Two structures show the enzyme covalently bound through its catalytic cysteine residues to previously reported aziridine-based inhibitors. One structure unexpectedly shows the enzyme bound to a different compound, D,L-α-methylDAP, presumably produced as a synthetic byproduct. Stereoselective synthesis of L,L- and D,L-α-methylDAP followed by inhibition assays shows that these compounds are slow-binding inhibitors of DAP epimerase. α-MethylDAP inhibitors provide a more accessible alternative to aziridine-based inhibitors to obtain crystal structures of DAP epimerase in its closed conformation. Comparisons of bacterial, cyanobacterial, and plant DAP epimerases provided here offer new insights into functional and structural differences between these enzymes.
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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) | 0.000 | 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".