Exploring the repository of de novo-designed bifunctional antimicrobial peptides through deep learning
Antimicrobial peptides (AMPs) are attractive candidates to combat antibiotic resistance for their capability to target biomembranes and restrict a wide range of pathogens. It is a daunting challenge to discover novel AMPs due to their sparse distributions in a vast peptide universe, especially for p...
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| Format: | Article |
| Language: | English |
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eLife Sciences Publications Ltd
2025-03-01
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| Series: | eLife |
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| Online Access: | https://elifesciences.org/articles/97330 |
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| author | Ruihan Dong Rongrong Liu Ziyu Liu Yangang Liu Gaomei Zhao Honglei Li Shiyuan Hou Xiaohan Ma Huarui Kang Jing Liu Fei Guo Ping Zhao Junping Wang Cheng Wang Xingan Wu Sheng Ye Cheng Zhu |
| author_facet | Ruihan Dong Rongrong Liu Ziyu Liu Yangang Liu Gaomei Zhao Honglei Li Shiyuan Hou Xiaohan Ma Huarui Kang Jing Liu Fei Guo Ping Zhao Junping Wang Cheng Wang Xingan Wu Sheng Ye Cheng Zhu |
| author_sort | Ruihan Dong |
| collection | DOAJ |
| description | Antimicrobial peptides (AMPs) are attractive candidates to combat antibiotic resistance for their capability to target biomembranes and restrict a wide range of pathogens. It is a daunting challenge to discover novel AMPs due to their sparse distributions in a vast peptide universe, especially for peptides that demonstrate potencies for both bacterial membranes and viral envelopes. Here, we establish a de novo AMP design framework by bridging a deep generative module and a graph-encoding activity regressor. The generative module learns hidden ‘grammars’ of AMP features and produces candidates sequentially pass antimicrobial predictor and antiviral classifiers. We discovered 16 bifunctional AMPs and experimentally validated their abilities to inhibit a spectrum of pathogens in vitro and in animal models. Notably, P076 is a highly potent bactericide with the minimal inhibitory concentration of 0.21 μM against multidrug-resistant Acinetobacter baumannii, while P002 broadly inhibits five enveloped viruses. Our study provides feasible means to uncover the sequences that simultaneously encode antimicrobial and antiviral activities, thus bolstering the function spectra of AMPs to combat a wide range of drug-resistant infections. |
| format | Article |
| id | doaj-art-a3cc7efaeefe498d8f6703762a5ec74b |
| institution | DOAJ |
| issn | 2050-084X |
| language | English |
| publishDate | 2025-03-01 |
| publisher | eLife Sciences Publications Ltd |
| record_format | Article |
| series | eLife |
| spelling | doaj-art-a3cc7efaeefe498d8f6703762a5ec74b2025-08-20T02:59:23ZengeLife Sciences Publications LtdeLife2050-084X2025-03-011310.7554/eLife.97330Exploring the repository of de novo-designed bifunctional antimicrobial peptides through deep learningRuihan Dong0https://orcid.org/0009-0001-5862-8410Rongrong Liu1https://orcid.org/0000-0002-9725-3463Ziyu Liu2Yangang Liu3Gaomei Zhao4Honglei Li5Shiyuan Hou6Xiaohan Ma7Huarui Kang8Jing Liu9Fei Guo10Ping Zhao11Junping Wang12Cheng Wang13https://orcid.org/0000-0002-6690-6433Xingan Wu14Sheng Ye15Cheng Zhu16https://orcid.org/0000-0003-0260-6287Frontiers Science Center for Synthetic Biology (Ministry of Education), Tianjin Key Laboratory of Function and Application of Biological Macromolecular Structures, School of Life Sciences, Faculty of Medicine, Tianjin University, Tianjin, China; Center for Quantitative Biology, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, ChinaDepartment of Microbiology, School of Basic Medicine, Fourth Military Medical University, Shaanxi, ChinaDepartment of Microbiology, School of Basic Medicine, Fourth Military Medical University, Shaanxi, ChinaDepartment of Microbiology, Second Military Medical University, Shanghai, ChinaState Key Laboratory of Trauma and Chemical Poisoning, Institute of Combined Injury of PLA, College of Preventive Medicine, Third Military Medical University (Army Medical University), Chongqing, ChinaTianjin Cancer Hospital Airport Hospital, Tianjin, ChinaDepartment of Microbiology, School of Basic Medicine, Fourth Military Medical University, Shaanxi, ChinaDepartment of Microbiology, School of Basic Medicine, Fourth Military Medical University, Shaanxi, ChinaDepartment of Microbiology, School of Basic Medicine, Fourth Military Medical University, Shaanxi, ChinaDepartment of Microbiology, School of Basic Medicine, Fourth Military Medical University, Shaanxi, ChinaSchool of Computer Science and Engineering, Central South University, Changsha, ChinaDepartment of Microbiology, Second Military Medical University, Shanghai, ChinaState Key Laboratory of Trauma and Chemical Poisoning, Institute of Combined Injury of PLA, College of Preventive Medicine, Third Military Medical University (Army Medical University), Chongqing, ChinaState Key Laboratory of Trauma and Chemical Poisoning, Institute of Combined Injury of PLA, College of Preventive Medicine, Third Military Medical University (Army Medical University), Chongqing, ChinaDepartment of Microbiology, School of Basic Medicine, Fourth Military Medical University, Shaanxi, ChinaFrontiers Science Center for Synthetic Biology (Ministry of Education), Tianjin Key Laboratory of Function and Application of Biological Macromolecular Structures, School of Life Sciences, Faculty of Medicine, Tianjin University, Tianjin, ChinaFrontiers Science Center for Synthetic Biology (Ministry of Education), Tianjin Key Laboratory of Function and Application of Biological Macromolecular Structures, School of Life Sciences, Faculty of Medicine, Tianjin University, Tianjin, ChinaAntimicrobial peptides (AMPs) are attractive candidates to combat antibiotic resistance for their capability to target biomembranes and restrict a wide range of pathogens. It is a daunting challenge to discover novel AMPs due to their sparse distributions in a vast peptide universe, especially for peptides that demonstrate potencies for both bacterial membranes and viral envelopes. Here, we establish a de novo AMP design framework by bridging a deep generative module and a graph-encoding activity regressor. The generative module learns hidden ‘grammars’ of AMP features and produces candidates sequentially pass antimicrobial predictor and antiviral classifiers. We discovered 16 bifunctional AMPs and experimentally validated their abilities to inhibit a spectrum of pathogens in vitro and in animal models. Notably, P076 is a highly potent bactericide with the minimal inhibitory concentration of 0.21 μM against multidrug-resistant Acinetobacter baumannii, while P002 broadly inhibits five enveloped viruses. Our study provides feasible means to uncover the sequences that simultaneously encode antimicrobial and antiviral activities, thus bolstering the function spectra of AMPs to combat a wide range of drug-resistant infections.https://elifesciences.org/articles/97330antimicrobial peptidesantiviral peptidesdeep learningdual-functiongenerative modelsequence design |
| spellingShingle | Ruihan Dong Rongrong Liu Ziyu Liu Yangang Liu Gaomei Zhao Honglei Li Shiyuan Hou Xiaohan Ma Huarui Kang Jing Liu Fei Guo Ping Zhao Junping Wang Cheng Wang Xingan Wu Sheng Ye Cheng Zhu Exploring the repository of de novo-designed bifunctional antimicrobial peptides through deep learning eLife antimicrobial peptides antiviral peptides deep learning dual-function generative model sequence design |
| title | Exploring the repository of de novo-designed bifunctional antimicrobial peptides through deep learning |
| title_full | Exploring the repository of de novo-designed bifunctional antimicrobial peptides through deep learning |
| title_fullStr | Exploring the repository of de novo-designed bifunctional antimicrobial peptides through deep learning |
| title_full_unstemmed | Exploring the repository of de novo-designed bifunctional antimicrobial peptides through deep learning |
| title_short | Exploring the repository of de novo-designed bifunctional antimicrobial peptides through deep learning |
| title_sort | exploring the repository of de novo designed bifunctional antimicrobial peptides through deep learning |
| topic | antimicrobial peptides antiviral peptides deep learning dual-function generative model sequence design |
| url | https://elifesciences.org/articles/97330 |
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