| Title | Application of a deep generative model produces novel and diverse functional peptides against microbial resistance |
| Author | |
| Corresponding Author | Dong, Jie; Wang, Jianmin; Cao, Dongsheng |
| Joint first author | Mao, Jiashun; Guan, Shenghui |
| Publication Years | 2023-01
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| DOI | |
| Source Title | |
| EISSN | 2001-0370
|
| Volume | 21Pages:463-471 |
| Abstract | 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.
© 2022 |
| Indexed By | |
| Language | English
|
| SUSTech Authorship | Others
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| Funding Project | This work was supported by the National Key Research and Development Program of China (2021YFF1201400), National Natural Science Foundation of China (22173118), Hunan Provincial Science Fund for Distinguished Young Scholars (2021JJ10068), Science and Technology Innovation Program of Hunan Province (2021RC4011), Changsha Municipal Natural Science Foundation (kq2014144), Changsha Science and Technology Bureau Project (kq2001034), and HKBU Strategic Development Fund Project (SDF19 0402 P02). We also acknowledge the support provided by Haikun Xu and the High Performance Computing Center of Central South University. The study was approved by the university’s review board.The authors thank Beijing Seetatech Technology Co. Ltd. (https://www.autodl.com/) and the public media platform DrugAI for providing a computing source and support. The authors declare no competing financial interest. All source codes for the methods, experiments, and visualizations presented in this work are available under the MIT license via the Github project repository (https://github.com/AspirinCode/AMPTrans-lstm). This work was supported by the National Key Research and Development Program of China (2021YFF1201400), National Natural Science Foundation of China (22173118), Hunan Provincial Science Fund for Distinguished Young Scholars (2021JJ10068), Science and Technology Innovation Program of Hunan Province (2021RC4011), Changsha Municipal Natural Science Foundation (kq2014144), Changsha Science and Technology Bureau Project (kq2001034), and HKBU Strategic Development Fund Project (SDF19 0402 P02). We also acknowledge the support provided by Haikun Xu and the High Performance Computing Center of Central South University. The study was approved by the university's review board.
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| WOS Accession No | WOS:000910121100001
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| Publisher | |
| EI Accession Number | 20225213300246
|
| EI Keywords | Cell Death
; Long Short-term Memory
; Microorganisms
; Peptides
|
| ESI Classification Code | Medicine And Pharmacology:461.6
; Biology:461.9
|
| Data Source | EV Compendex
|
| Citation statistics |
Cited Times [WOS]:1
|
| Document Type | Journal Article |
| Identifier | http://kc.sustech.edu.cn/handle/2SGJ60CL/519724 |
| Department | Department of Biology 生命科学学院 |
| Affiliation | 1.The Interdisciplinary Graduate Program in Integrative Biotechnology and Translational Medicine, Yonsei University, Incheon; 21983, Korea, Republic of 2.Department of Biology, School of Life Sciences, Southern University of Science and Technology, Guangdong, Shenzhen; 518055, China 3.School of Information and Communication Engineering, Hainan University, Hainan, Haikou; 510000, China 4.Department of Natural and Basic Sciences, University of Turbat, Kech, Balochistan, Turbat; 92600, Pakistan 5.School of Economics and Management, Southwest Petroleum University, Sichuan, Chengdu; 610500, China 6.College of Mechanical and Electronic Engineering, Dalian MinZu University, Liaoning, Dalian; 116600, China 7.Xiangya School of Pharmaceutical Sciences, Central South University, Hunan, Changsha; 410013, China |
| Recommended Citation GB/T 7714 |
Mao, Jiashun,Guan, Shenghui,Chen, Yongqing,et al. Application of a deep generative model produces novel and diverse functional peptides against microbial resistance[J]. Computational and Structural Biotechnology Journal,2023,21:463-471.
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| APA |
Mao, Jiashun.,Guan, Shenghui.,Chen, Yongqing.,Zeb, Amir.,Sun, Qingxiang.,...&Cao, Dongsheng.(2023).Application of a deep generative model produces novel and diverse functional peptides against microbial resistance.Computational and Structural Biotechnology Journal,21,463-471.
|
| MLA |
Mao, Jiashun,et al."Application of a deep generative model produces novel and diverse functional peptides against microbial resistance".Computational and Structural Biotechnology Journal 21(2023):463-471.
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