Recombinant expression of antimicrobial peptides using a novel self-cleaving aggregation tag in<i>Escherichia coli</i>
Bibliographic record
Abstract
Antimicrobial peptides (AMPs) are part of the innate immune system of complex multicellular organisms. Despite the fact that AMPs show great potential as a novel class of antibiotics, the lack of a cost-effective means for their mass production limits both basic research and clinical use. In this work, we describe a novel expression system for the production of antimicrobial peptides in Escherichia coli by combining ΔI-CM mini-intein with the self-assembling amphipathic peptide 18A to drive the formation of active aggregates. Two AMPs, human β-defensin 2 and LL-37, were fused to the self-cleaving tag and expressed as active protein aggregates. The active aggregates were recovered by centrifugation and the intact antimicrobial peptides were released into solution by an intein-mediated cleavage reaction in cleaving buffer (phosphate-buffered saline supplemented with 40 mmol/L Bis-Tris, 2 mmol/L EDTA, pH 6.2). The peptides were further purified by cation-exchange chromatography. Peptides yields of 0.82 ± 0.24 and 0.59 ± 0.11 mg/L were achieved for human β-defensin 2 and LL-37, respectively, with demonstrated antimicrobial activity. Using our expression system, intact antimicrobial peptides were recovered by simple centrifugation from active protein aggregates after the intein-mediated cleavage reaction. Thus, we provide an economical and efficient way to produce intact antimicrobial peptides in E. coli.
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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".