A specific peptide inhibitor of the class B metallo-β-lactamase L-1 from Stenotrophomonas maltophilia identified using phage display
Bibliographic record
Abstract
OBJECTIVES: In Gram-negative bacteria, resistance to beta-lactam antibiotics and to known inhibitors mediated by metallo-beta-lactamases is a major concern and a serious threat to public health. Since no clinically useful inhibitors are available against class B metallo-beta-lactamases, the aim of the study was to identify peptides as inhibitors. METHODS: The L-1 metalloenzyme from Stenotrophomonas maltophilia was cloned, over-expressed, purified to homogeneity and used in screening of peptide libraries by phage display with a selective and competitive biopanning assay. This was based upon the high affinity of L-1 for cefoxitin and its slow hydrolysis. RESULTS: From six peptides, the consensus sequence Cys-Val-His-Ser-Pro-Asn-Arg-Glu-Cys was identified as a promising inhibitor of L-1 hydrolytic activity. This peptide showed a mixed inhibition of L-1 with a K(i competitive) of 16 +/- 4 microM and a K(i uncompetitive) of 9 +/- 1 microM. The same peptide was prepared without flanking Cys residues and demonstrated no detectable inhibition of L-1 hydrolytic activity with nitrocefin as a substrate. These data confirmed the importance of the peptide conformation for the inhibition of L-1 hydrolytic activity. Further analysis revealed rescue by Zn2+ ions. The mixed inhibition indicated peptide binding near the active site of L-1 and blocking of zinc atoms for optimal conformation in the pocket of the active site. CONCLUSION: This is the first report of a peptide inhibitor for Class B metallo-beta-lactamases. It will be used as a lead to identify more potent small molecule inhibitors via peptidomimetics.
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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) | 0.000 | 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) | 0.001 | 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".