Molecular basis of antibiotic resistance and β-lactamase inhibition by mechanism-based inactivators: perspectives and future directions
Bibliographic record
Abstract
Antibacterial chemotherapy is particularly striking in the family of penicillins and cephalosporins. Over 40 structurally different beta-lactam molecules are available in 73 formulations and the majority of them are currently prescribed for medical use in hospitals. beta-Lactams are well tolerated by humans with few side effects. They interact very specifically with their bacterial target, the D-alanyl-D-alanine carboxypeptidase-transpeptidase usually referred to as DD-peptidase. The outstanding number of beta-lactamases produced by bacteria represent a serious threat to the clinical utility of beta-lactams. The discovery of beta-lactamase inhibitors was thought to solve, in part, the problem of resistance. Unfortunately, bacteria have evolved new mechanisms of resistance to overcome the inhibitory effects of beta-lactamase inactivators. Here, we summarize the diversified mechanistic features of class A beta-lactamases interactions with mechanism-based inhibitors using available microbiological, kinetic and structural data for the prototype TEM beta-lactamases. A brief historical overview of the strategies developed to counteract beta-lactamases will be presented followed by a short description of the chemical events which lead to the inactivation of TEM beta-lactamase by inhibitors from different classes. Finally, an update on the clinical prevalence of natural and inhibitor-resistant enzyme mutants, the total chemical synthesis to design and synthesize a new structure and produced a broad spectrum beta-lactamase inhibitor that mimics the beta-lactam ring, but does not contain it is discussed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".