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Screening antimicrobial peptides and probiotics using multiple deep learning and directed evolution strategies
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作者 Yu Zhang li-Hua liu +20 位作者 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 《Acta Pharmaceutica Sinica B》 SCIE CAS CSCD 2024年第8期3476-3492,共17页
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 predi... 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. 展开更多
关键词 Antimicrobial peptide Deep learning Cell-free synthesis PROBIOTICS L.plantarum
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