Antimicrobial peptides of the genus <i>Bacillus</i>: a new era for antibiotics
Bibliographic record
Abstract
The rapid onset of resistance reduces the efficacy of most conventional antimicrobial drugs and is a general cause of concern for human well-being. Thus, there is great demand for a continuous supply of novel antibiotics to combat this problem. Bacteria-derived antimicrobial peptides (AMPs) have long been used as food preservatives; moreover, prior to the development of conventional antibiotics, these AMPs served as an efficient source of antibiotics. Recently, peptides produced by members of the genus Bacillus were shown to have a broad spectrum of antimicrobial activity against pathogenic microbes. Bacillus-derived AMPs can be synthesized both ribosomally and nonribosomally and can be classified according to peptide biosynthesis, structure, and molecular weight. The precise mechanism of action of these AMPs is not yet clear; however, one proposed mechanism is that these AMPs kill bacteria by forming channels in and (or) disrupting the bacterial cell wall. Bacillus-derived AMPs have potential in the pharmaceutical industry, as well as the food and agricultural sectors. Here, we focus on Bacillus-derived AMPs as a novel alternative approach to antibacterial drug development. We also provide an overview of the biosynthesis, mechanisms of action, applications, and effectiveness of different AMPs produced by members of the Bacillus genus, including several recently identified novel AMPs.
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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 | 0.001 |
| 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 | 0.002 |
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".