Substrate-product analogue inhibitors of isoleucine 2-epimerase from <i>Lactobacillus buchneri</i> by rational design
Bibliographic record
Abstract
A rational approach that may be applied to a broad class of enzyme-catalyzed reactions to design enzyme inhibitors affords a powerful strategy, facilitating the development of drugs and/or molecular probes of enzyme mechanisms. A strategy for the development of substrate-product analogues (SPAs) as inhibitors of racemases and epimerases is elaborated using isoleucine 2-epimerase from Lactobacillus buchneri (LbIleE) as a model enzyme. LbIleE catalyzes the PLP-dependent, reversible, racemization or epimerization of nonpolar amino acids at the C-2 position. The enzyme plays an important role in the biosynthesis of branched-chain d-amino acids and is a potential target for the development of antimicrobial agents. 3-Ethyl-3-methyl-l-norvaline (Ki = 2.9 ± 0.2 mM) and 3-ethyl-3-methyl-d-norvaline (Ki = 1.5 ± 0.2 mM) were designed as SPAs based on the movement of the sec-butyl side chain of the substrate l-Ile during catalysis, and were competitive inhibitors with binding affinities exceeding that of l-Ile by 1.3- and 2.5-fold, respectively. Surprisingly, these compounds were not substrates, but the corresponding compounds lacking the 3-methyl group were substrates. Unlike serine, glutamate, and proline racemases, which exhibit stringent steric requirements at their active sites, the active site of LbIleE was amenable to binding bulky SPAs. Moreover, LbIleE bound the SPA 2,2-di-n-butylglycine (Ki = 11.0 ± 0.2 mM) as a competitive inhibitor, indicating that the hydrophobic binding pocket at the active site was sufficiently plastic to tolerate gem-dialkyl substitution at the α-carbon of an amino acid. Overall, these results reveal that amino acid racemases/epimerases are amenable to inhibition by SPAs provided that they possess a capacious active site.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".