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

doi:10.1021/acs.jcim.2c00089

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	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
刊名	JOURNAL OF CHEMICAL INFORMATION AND MODELING
	2022-05-23
卷号	62 期号:10 页码:2617-2629
关键词	BioactivityDrug interactionsLearning systemsToxicity Auto encodersComputation methodsDe novo designFunctional peptidesHigher efficiencyMulti-classifierPeptide sequencesPeptide therapeuticsTherapeutic agentsTherapeutic peptides
ISSN号	1549-9596
DOI	10.1021/acs.jcim.2c00089
英文摘要	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.
WOS研究方向	Pharmacology & Pharmacy ; Chemistry ; Computer Science
语种	英语
出版者	AMER CHEMICAL SOC
WOS记录号	WOS:000805762200032
内容类型	期刊论文
源URL	[http://ir.lut.edu.cn/handle/2XXMBERH/158686]
专题	兰州理工大学
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
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.