| Autor: |
Yu Zhang, Li-Hua Liu, Bo Xu, Zhiqian Zhang, Min Yang, Yiyang He, Jingjing Chen, Yang Zhang, Yucheng Hu, Xipeng Chen, Zitong Sun, Qijun Ge, Song Wu, Wei Lei, Kaizheng Li, Hua Cui, Gangzhu Yang, Xuemei Zhao, Man Wang, Jiaqi Xia, Zhen Cao, Ao Jiang, Yi-Rui Wu |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
|
| Zdroj: |
Acta Pharmaceutica Sinica B, Vol 14, Iss 8, Pp 3476-3492 (2024) |
| Druh dokumentu: |
article |
| ISSN: |
2211-3835 |
| DOI: |
10.1016/j.apsb.2024.05.003 |
| Popis: |
Owing to their limited accuracy and narrow applicability, current antimicrobial peptide (AMP) prediction models face obstacles in industrial application. To address these limitations, we developed and improved an AMP prediction model using Comparing and Optimizing Multiple DEep Learning (COMDEL) algorithms, coupled with high-throughput AMP screening method, finally reaching an accuracy of 94.8% in test and 88% in experiment verification, surpassing other state-of-the-art models. In conjunction with COMDEL, we employed the phage-assisted evolution method to screen Sortase in vivo and developed a cell-free AMP synthesis system in vitro, ultimately increasing AMPs yields to a range of 0.5–2.1 g/L within hours. Moreover, by multi-omics analysis using COMDEL, we identified Lactobacillus plantarum as the most promising candidate for AMP generation among 35 edible probiotics. Following this, we developed a microdroplet sorting approach and successfully screened three L. plantarum mutants, each showing a twofold increase in antimicrobial ability, underscoring their substantial industrial application values. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
|
