Strategies for the Discovery and Advancement of Novel Cationic Antimicrobial Peptides
Bibliographic record
Abstract
Multi-drug resistant bacteria are appearing at an alarming rate and impose significant burdens on healthcare systems worldwide. Cationic peptides have shown great promise as broad spectrum antimicrobial agents with a demonstrated ability to kill resistant bacteria, however, issues such as protease susceptibility and toxicity issues have delayed their clinical development. This review summarizes recent progress in the advancement of cationic antimicrobial peptides for the treatment of bacterial infections. The major focus of the discussion relates to recent advances in the areas of screening and in silico modeling. A selection of novel strategies that diverge from classical linear α-peptide antimicrobials is also discussed. A diverse array of candidate structures will be key to the ultimate development of a stable platform for clinical development. The ability to accurately predict peptide activity in silico and in a high-throughput manner should benefit all classes of cationic antimicrobial peptides and provide a larger set of candidate structures for clinical evaluation. Keywords: Antimicrobial, Cationic, Peptide/kwd, >, QSAR, screening, in silico, Cationic Antimi-crobial Peptides, Multi-drug resistant, broad spectrum, protease susceptibility, Peptide, Gram negative bacteria, Pseudomonas aeruginosa, Gram-positive, innate immunity, structure activity relationships, host defense pep-tides, glycosaminoglycans, red blood cell hemolysis, lipopolysaccharides, teichoic, zwitterionic lipids, threshold level, nucleic acid synthesis, second-generation peptides, SPOT synthesis technique, luxCDABE-expressing Pseudo-monas aeuruginosa, Bac2a- and Bac2a-, EC50 values, artificial neural network, methicillin-resistant S. aureus, ana-phylatoxin peptide C3a., benchmark peptide LL37, lipids phosphatidylcholine, phosphatidylglycerol, dipiccolinic acid, linear -peptide antimicrobials, neurokinin derived peptides GKH17 and HKH17, PEGylation of peptides, Polymyxins, cationic cyclic lipopeptides, hydrophobic antibiotics, Peptide Mimics
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".