Screening antimicrobial peptides and probiotics using multiple deep learning and directed evolution strategies
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) alg...
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Published in | Acta pharmaceutica Sinica. B Vol. 14; no. 8; pp. 3476 - 3492 |
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Main Authors | , , , , , , , , , , , , , , , , , , , , , , |
Format | Journal Article |
Language | English |
Published |
Elsevier B.V
01.08.2024
Elsevier |
Subjects | |
Online Access | Get full text |
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Summary: | 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.
This study establishes an AI-based AMPs prediction model named COMDEL for effectively mining edible AMPs and probiotics, which are subsequently synthesized and directly evolved for industrial application, respectively. [Display omitted] |
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Bibliography: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 These authors made equal contributions to this work. |
ISSN: | 2211-3835 2211-3843 |
DOI: | 10.1016/j.apsb.2024.05.003 |