Structure-Based Design Guides the Improved Efficacy of Deacylation Transition State Analogue Inhibitors of TEM-1 β-Lactamase<sup>,</sup>
Bibliographic record
Abstract
Transition state analogue boronic acid inhibitors mimicking the structures and interactions of good penicillin substrates for the TEM-1 beta-lactamase of Escherchia coli were designed using graphic analyses based on the enzyme's 1.7 A crystallographic structure. The synthesis of two of these transition state analogues, (1R)-1-phenylacetamido-2-(3-carboxyphenyl)ethylboronic acid (1) and (1R)-1-acetamido-2-(3-carboxy-2-hydroxyphenyl)ethylboronic acid (2), is reported. Kinetic measurements show that, as designed, compounds 1 and 2 are highly effective deacylation transition state analogue inhibitors of TEM-1 beta-lactamase, with inhibition constants of 5.9 and 13 nM, respectively. These values identify them as among the most potent competitive inhibitors yet reported for a beta-lactamase. The best inhibitor of the current series was (1R)-1-phenylacetamido-2-(3-carboxyphenyl)ethylboronic acid (1, K(I) = 5.9 nM), which resembles most closely the best known substrate of TEM-1, benzylpenicillin (penicillin G). The high-resolution crystallographic structures of these two inhibitors covalently bound to TEM-1 are also described. In addition to verifying the design features, these two structures show interesting and unanticipated changes in the active site area, including strong hydrogen bond formation, water displacement, and rearrangement of side chains. The structures provide new insights into the further design of this potent class of beta-lactamase inhibitors.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 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".