GM-Pep: A High Efficiency Strategy to De Novo Design Functional Peptide Sequences

doi:10.1021/acs.jcim.2c00089

	GM-Pep: A High Efficiency Strategy to De Novo Design Functional Peptide Sequences
	Chen, Qushuo 1; Yang, Changyan 1; Xie, Yihao 1; Wang, Yuqiang 2; Li, Xiaoxu 3; Wang, Kairong 1; Huang, Jinqi 4; Yan, Wenjin 1
	2022-05-23
发表期刊	JOURNAL OF CHEMICAL INFORMATION AND MODELING
ISSN	1549-9596
卷号	62 期号:10 页码:2617-2629
摘要	Although peptides are regarded as ideal therapeutic agents, only a small proportion of the marketed drugs are peptides. In the past decade, pharmacists have paid great attention to the development of peptide therapeutics. Except a few approved chemically/rationally designed peptides, most attempts failed due to unsatisfactory efficacy or safety. Luckily, computation methods, such as artificial intelligence, have been utilized to accelerate the discovery of therapeutic peptides by predicting the activity, toxicity, and absorption, distribution, metabolism, and excretion of polypeptides. Usually, a specific biological activity of a peptide could be accurately determined by an interest-oriented binary classification constructed of a positive set and another unexperimentally validated negative set regardless of other characteristics, which suggests that it could be challenging to realize the comprehensive evaluation of the research object in the early stage of drug research and development. Herein, we proposed an integrated method (GM-Pep) that contained a conditional variational autoencoder model (CVAE) and a positive sample training multiclassifier (Deep-Multiclassifier) to effectively generate a single bioactive peptide sequence without toxicity and referential side effects. The results showed that our Deep-Multiclassifier model gave a sequence accuracy of up to 96.41% [toxicity (94.48%), antifungal (96.58%), antihypertensive (97.18%), and antibacterial (96.91%), respectively]. The properties of Deep-Multiclassifier and CVAE were validated through 12 first synthesized antibacterial peptides or compared to random peptides. The source code and data sets are available at https://github.com/TimothyChen225/GM-Pep.
关键词	BioactivityDrug interactionsLearning systemsToxicity Auto encodersComputation methodsDe novo designFunctional peptidesHigher efficiencyMulti-classifierPeptide sequencesPeptide therapeuticsTherapeutic agentsTherapeutic peptides
DOI	10.1021/acs.jcim.2c00089
收录类别	SCIE ; EI
语种	英语
WOS研究方向	Pharmacology & Pharmacy ; Chemistry ; Computer Science
WOS类目	Chemistry, Medicinal ; Chemistry, Multidisciplinary ; Computer Science, Information Systems ; Computer Science, Interdisciplinary Applications
WOS记录号	WOS:000805762200032
出版者	AMER CHEMICAL SOC
EI入藏号	20222212176703
EI主题词	Peptides
EI分类号	461.6 Medicine and Pharmacology461.7 Health Care461.9 Biology802.2 Chemical Reactions
来源库	WOS
引用统计	被引频次：1[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	https://ir.lut.edu.cn/handle/2XXMBERH/158686
专题	兰州理工大学
通讯作者	Li, Xiaoxu; Wang, Kairong; Huang, Jinqi; Yan, Wenjin
作者单位	1.Lanzhou Univ, Sch Basic Med Sci, Inst Pharmacol, Key Lab Preclin Study New Drugs Gansu Prov, Lanzhou 730000, Gansu, Peoples R China; 2.Lanzhou Univ, Sch Stomatol, Lanzhou 730000, Gansu, Peoples R China; 3.Lanzhou Univ Technol, Sch Comp & Commun, Lanzhou 730050, Gansu, Peoples R China; 4.Guangdong Med Univ, Dept Hematol, Affiliated Hosp, Zhanjiang 524000, Guangdong, Peoples R China
通讯作者单位	兰州理工大学
推荐引用方式 GB/T 7714	Chen, Qushuo,Yang, Changyan,Xie, Yihao,et al. GM-Pep: A High Efficiency Strategy to De Novo Design Functional Peptide Sequences[J]. JOURNAL OF CHEMICAL INFORMATION AND MODELING,2022,62(10):2617-2629.
APA	Chen, Qushuo.,Yang, Changyan.,Xie, Yihao.,Wang, Yuqiang.,Li, Xiaoxu.,...&Yan, Wenjin.(2022).GM-Pep: A High Efficiency Strategy to De Novo Design Functional Peptide Sequences.JOURNAL OF CHEMICAL INFORMATION AND MODELING,62(10),2617-2629.
MLA	Chen, Qushuo,et al."GM-Pep: A High Efficiency Strategy to De Novo Design Functional Peptide Sequences".JOURNAL OF CHEMICAL INFORMATION AND MODELING 62.10(2022):2617-2629.