The Basis for Carbapenem Hydrolysis by Class A β-Lactamases: A Combined Investigation using Crystallography and Simulations
Bibliographic record
Abstract
Carbapenems are the most potent β-lactam antibiotics and key drugs for treating infections by Gram-negative bacteria. In such organisms, β-lactam resistance arises principally from β-lactamase production. Although carbapenems escape the activity of most β-lactamases, due in the class A enzymes to slow deacylation of the covalent acylenzyme intermediate, carbapenem-hydrolyzing class A β-lactamases are now disseminating in clinically relevant bacteria. The reasons why carbapenems are substrates for these enzymes, but inhibit other class A β-lactamases, remain to be fully established. Here, we present crystal structures of the class A carbapenemase SFC-1 from Serratia fonticola and of complexes of its Ser70 Ala (Michaelis) and Glu166 Ala (acylenzyme) mutants with the carbapenem meropenem. These are the first crystal structures of carbapenem complexes of a class A carbapenemase. Our data reveal that, in the SFC-1 acylenzyme complex, the meropenem 6α-1R-hydroxyethyl group interacts with Asn132, but not with the deacylating water molecule. Molecular dynamics simulations indicate that this mode of binding occurs in both the Michaelis and acylenzyme complexes of wild-type SFC-1. In carbapenem-inhibited class A β-lactamases, it is proposed that the deacylating water molecule is deactivated by interaction with the carbapenem 6α-1R-hydroxyethyl substituent. Structural comparisons with such enzymes suggest that in SFC-1 subtle repositioning of key residues (Ser70, Ser130, Asn132 and Asn170) enlarges the active site, permitting rotation of the carbapenem 6α-1R-hydroxyethyl group and abolishing this contact. Our data show that SFC-1, and by implication other such carbapenem-hydrolyzing enzymes, uses Asn132 to orient bound carbapenems for efficient deacylation and prevent their interaction with the deacylating water molecule.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".