Inhibiting the VIM-2 Metallo-β-Lactamase by Graphene Oxide and Carbon Nanotubes
Bibliographic record
Abstract
Metallo-β-lactamases (MBLs) degrade a broad spectrum of antibiotics including the latest carbapenems. So far, limited success has been achieved in developing its inhibitors using small organic molecules. VIM-2 is one of the most studied and important MBLs. In this work, we screened 10 nanomaterials, covering a diverse range of surface properties including charge, hydrophobicity, and specific chemical bonding. Among these, graphene oxide and carbon nanotubes are the most potent inhibitors, while most other materials do not show much inhibition effect. The inhibition is noncompetitive and is attributed to the hydrophobic interaction with the enzyme. Adsorption of VIM-2 was further probed using protein displacement assays where it cannot displace or be displaced by bovine serum albumin (BSA). This information is useful for rational design inhibitors for MBLs and more specific inhibition might be achieved by further surface modifications on these nanocarbons.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".