Lipopeptides from Bacillus and Paenibacillus spp.: A Gold Mine of Antibiotic Candidates
Bibliographic record
Abstract
The emergence of multidrug-resistant bacteria has placed a strain on health care systems and highlighted the need for new classes of antibiotics. Bacterial lipopeptides are secondary metabolites, generally produced by nonribosomal peptide synthetases that often exhibit broad-spectrum antimicrobial activity. Only two new structural types of antibiotics have entered the market in the last 40 years, linezolid and the bacterial lipopeptide daptomycin. A wide variety of bacteria produce lipopeptides, however Bacillus and Paenibacillus spp. in particular have yielded several potent antimicrobial lipopeptides. Many of the lipopeptides produced by these bacteria have been known for decades and represent a potential gold mine of antibiotic candidates. This list includes the polymyxins, octapeptins, polypeptins, iturins, surfactins, fengycins, fusaricidins, and tridecaptins, as well as some novel examples, including the kurstakins. These lipopeptides have a wide variety of activities, ranging from antibacterial and antifungal, to anticancer and antiviral. This review presents a reasonably comprehensive list of each class of lipopeptide and their known homologues. Emphasis has been placed on their antimicrobial activities, as well other potential applications for this interesting class of substances.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".