High-level biosynthesis and purification of the antimicrobial peptide Kiadin based on non-chromatographic purification and acid cleavage methods
Bibliographic record
Abstract
Antimicrobial peptides (AMPs) are renowned for their potent bacteriostatic activity and safety, rendering them invaluable in animal husbandry, food safety, and medicine. Despite their potential, the physiological toxicity of AMPs to host cells significantly hampers their biosynthetic production. This study presents a novel approach for the biosynthesis of the antimicrobial peptide Kiadin by engineering a DAMP4-DPS-Kiadin fusion protein to mitigate host cell toxicity and achieve high-level expression. Leveraging the unique properties of the DAMP4 protein, we developed a non-chromatographic purification method to isolate the DAMP4-DPS-Kiadin fusion protein with high purity. The instability of the D-P peptide bond under acidic conditions, combined with the thermal and saline stability of DAMP4, enabled efficient separation of Kiadin through acid cleavage and isoelectric precipitation, yielding Kiadin with 96% purity and a production yield of 29.3 mg/L. Our optimization of acid cleavage temperature, duration, and isoelectric precipitation conditions proved critical for maximizing the purification efficiency and expression levels of Kiadin. The biosynthesized Kiadin exhibited robust bacteriostatic activity against Escherichia coli, Pseudomonas aeruginosa, Acinetobacter baumannii, Bacillus cereus and Staphylococcus aureus. Notably, Kiadin demonstrated significant post-antibiotic effects by disrupting bacterial membrane integrity, inducing cytoplasmic leakage, and inhibiting biofilm formation in E. coli K88 and S. aureus Mu50, without cytotoxicity towards mouse macrophages. In vivo studies further confirmed Kiadin's exceptional therapeutic efficacy against abdominal infections caused by E. coli K88. The acid cleavage and non-chromatographic purification techniques developed in this study offer a cost-effective and efficient strategy for the high-purity production of 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) | 0.000 | 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".