南方科技大学知识苑(SUSTech KC): Application of a deep generative model produces novel and diverse functional peptides against microbial resistance

题名	Application of a deep generative model produces novel and diverse functional peptides against microbial resistance
作者	Mao, Jiashun 1; Guan, Shenghui2 ; Chen, Yongqing 3; Zeb, Amir 1,4; Sun, Qingxiang 5; Lu, Ranlan 6; Dong, Jie 7; Wang, Jianmin 1; Cao, Dongsheng 7
通讯作者	Dong, Jie; Wang, Jianmin; Cao, Dongsheng
共同第一作者	Mao, Jiashun; Guan, Shenghui
发表日期	2023-01-25
DOI	10.1016/j.csbj.2022.12.029
发表期刊	Computational and Structural Biotechnology Journal 影响因子和分区
EISSN	2001-0370
卷号	21页码:463-471
摘要	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
收录类别	SCI ; EI
语种	英语
学校署名	共同第一 ; 其他
资助项目	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.
WOS记录号	WOS:000910121100001
出版者	Elsevier B.V.
EI入藏号	20225213300246
EI主题词	Cell Death ; Long Short-term Memory ; Microorganisms ; Peptides
EI分类号	Medicine And Pharmacology:461.6 ; Biology:461.9
来源库	EV Compendex
引用统计	被引频次[WOS]：8
成果类型	期刊论文
条目标识符	http://sustech.caswiz.com/handle/2SGJ60CL/519724
专题	生命科学学院_生物系生命科学学院
作者单位	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
推荐引用方式 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.
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.