Amphipathic β-Strand Mimics as Potential Membrane Disruptive Antibiotics
Bibliographic record
Abstract
In recent years, there have been increasing numbers of bacterial strains emerging that are resistant to the currently available antibiotics. In the search for new antibiotics, attention has been focused on natural antimicrobial peptides that act by selectively disrupting the membranes of bacterial cells, a mechanism that is thought to be nonconducive to the development of resistance. It is desirable to mimic the structures and activities of these peptides while introducing properties such as resistance to proteolytic degradation, which make molecules more ideal for development as drugs. Described here is the design and synthesis of beta-strand mimetic oligomers based on alternating alpha-amino acids and azacyclohexenone units that segregate cationic lysine and hydrophobic valine side chains on opposite faces of the beta-strand. (1)H NMR dilution studies demonstrated that despite the incorporation of alternating d- and l-amino acids in order to obtain facial amphiphilicity, these oligomers are capable of dimerizing to beta-sheet mimics in a manner similar to the oligomers containing all l-amino acids. The ability of the molecules to disrupt phospholipid vesicles mimicking the membranes of both bacterial and mammalian cells was investigated using a fluorescent dye leakage assay. Several of the oligomers were found to exhibit activity and selectivity for the bacterial over mammalian membranes. Overall, these studies demonstrate the promise of this class of molecules for the development of new potential antibiotics and provide information on the structural features that are important for activity.
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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) | 0.001 | 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".