Application of a deep generative model produces novel and diverse functional peptides against microbial resistance
Antimicrobial resistance could threaten millions of lives in the immediate future. Antimicrobial peptides (AMPs) are an alternative to conventional antibiotics practice against infectious diseases. Despite the potential contribution of AMPs to the antibiotic’s world, their development and optimizati...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-01-01
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| Series: | Computational and Structural Biotechnology Journal |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2001037022005864 |
| _version_ | 1827577712573677568 |
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| author | Jiashun Mao Shenghui Guan Yongqing Chen Amir Zeb Qingxiang Sun Ranlan Lu Jie Dong Jianmin Wang Dongsheng Cao |
| author_facet | Jiashun Mao Shenghui Guan Yongqing Chen Amir Zeb Qingxiang Sun Ranlan Lu Jie Dong Jianmin Wang Dongsheng Cao |
| author_sort | Jiashun Mao |
| collection | DOAJ |
| description | Antimicrobial resistance could threaten millions of lives in the immediate future. Antimicrobial peptides (AMPs) are an alternative to conventional antibiotics practice against infectious diseases. Despite the potential contribution of AMPs to the antibiotic’s world, their development and optimization have encountered serious challenges. Cutting-edge methods with novel and improved selectivity toward resistant targets must be established to create AMPs-driven treatments. Here, we present AMPTrans-lstm, a deep generative network-based approach for the rational design of AMPs. The AMPTrans-lstm pipeline involves pre-training, transfer learning, and module identification. The AMPTrans-lstm model has two sub-models, namely, (long short-term memory) LSTM sampler and Transformer converter, which can be connected in series to make full use of the stability of LSTM and the novelty of Transformer model. These elements could generate AMPs candidates, which can then be tailored for specific applications. By analyzing the generated sequence and trained AMPs, we prove that AMPTrans-lstm can expand the design space of the trained AMPs and produce reasonable and brand-new AMPs sequences. AMPTrans-lstm can generate functional peptides for antimicrobial resistance with good novelty and diversity, so it is an efficient AMPs design tool. |
| first_indexed | 2024-03-08T21:31:46Z |
| format | Article |
| id | doaj.art-f9027552d41648139c9ab1eaefbfad42 |
| institution | Directory Open Access Journal |
| issn | 2001-0370 |
| language | English |
| last_indexed | 2024-03-08T21:31:46Z |
| publishDate | 2023-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Computational and Structural Biotechnology Journal |
| spelling | doaj.art-f9027552d41648139c9ab1eaefbfad422023-12-21T07:30:33ZengElsevierComputational and Structural Biotechnology Journal2001-03702023-01-0121463471Application of a deep generative model produces novel and diverse functional peptides against microbial resistanceJiashun Mao0Shenghui Guan1Yongqing Chen2Amir Zeb3Qingxiang Sun4Ranlan Lu5Jie Dong6Jianmin Wang7Dongsheng Cao8The Interdisciplinary Graduate Program in Integrative Biotechnology and Translational Medicine, Yonsei University, Incheon 21983, the Republic of KoreaDepartment of Biology, School of Life Sciences, Southern University of Science and Technology, Shenzhen 518055, Guangdong, ChinaSchool of Information and Communication Engineering, Hainan University, Haikou 510000, Hainan, ChinaThe Interdisciplinary Graduate Program in Integrative Biotechnology and Translational Medicine, Yonsei University, Incheon 21983, the Republic of Korea; Department of Natural and Basic Sciences, University of Turbat, Kech, Turbat, Balochistan 92600, PakistanSchool of Economics and Management, Southwest Petroleum University, Chengdu 610500, Sichuan, ChinaCollege of Mechanical and Electronic Engineering, Dalian MinZu University, Dalian 116600, Liaoning, ChinaXiangya School of Pharmaceutical Sciences, Central South University, Changsha 410013, Hunan, China; Corresponding authors.The Interdisciplinary Graduate Program in Integrative Biotechnology and Translational Medicine, Yonsei University, Incheon 21983, the Republic of Korea; Corresponding authors.Xiangya School of Pharmaceutical Sciences, Central South University, Changsha 410013, Hunan, China; Corresponding authors.Antimicrobial resistance could threaten millions of lives in the immediate future. Antimicrobial peptides (AMPs) are an alternative to conventional antibiotics practice against infectious diseases. Despite the potential contribution of AMPs to the antibiotic’s world, their development and optimization have encountered serious challenges. Cutting-edge methods with novel and improved selectivity toward resistant targets must be established to create AMPs-driven treatments. Here, we present AMPTrans-lstm, a deep generative network-based approach for the rational design of AMPs. The AMPTrans-lstm pipeline involves pre-training, transfer learning, and module identification. The AMPTrans-lstm model has two sub-models, namely, (long short-term memory) LSTM sampler and Transformer converter, which can be connected in series to make full use of the stability of LSTM and the novelty of Transformer model. These elements could generate AMPs candidates, which can then be tailored for specific applications. By analyzing the generated sequence and trained AMPs, we prove that AMPTrans-lstm can expand the design space of the trained AMPs and produce reasonable and brand-new AMPs sequences. AMPTrans-lstm can generate functional peptides for antimicrobial resistance with good novelty and diversity, so it is an efficient AMPs design tool.http://www.sciencedirect.com/science/article/pii/S2001037022005864AMPsDeep generative modelAntimicrobial resistanceTransformerLSTM |
| spellingShingle | Jiashun Mao Shenghui Guan Yongqing Chen Amir Zeb Qingxiang Sun Ranlan Lu Jie Dong Jianmin Wang Dongsheng Cao Application of a deep generative model produces novel and diverse functional peptides against microbial resistance Computational and Structural Biotechnology Journal AMPs Deep generative model Antimicrobial resistance Transformer LSTM |
| title | Application of a deep generative model produces novel and diverse functional peptides against microbial resistance |
| title_full | Application of a deep generative model produces novel and diverse functional peptides against microbial resistance |
| title_fullStr | Application of a deep generative model produces novel and diverse functional peptides against microbial resistance |
| title_full_unstemmed | Application of a deep generative model produces novel and diverse functional peptides against microbial resistance |
| title_short | Application of a deep generative model produces novel and diverse functional peptides against microbial resistance |
| title_sort | application of a deep generative model produces novel and diverse functional peptides against microbial resistance |
| topic | AMPs Deep generative model Antimicrobial resistance Transformer LSTM |
| url | http://www.sciencedirect.com/science/article/pii/S2001037022005864 |
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