DeepDigest: Prediction of Protein Proteolytic Digestion with Deep Learning

doi:10.1021/acs.analchem.0c04704

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	DeepDigest: Prediction of Protein Proteolytic Digestion with Deep Learning
	Yang, Jinghan1,2 ; Gao, Zhiqiang1,2 ; Ren, Xiuhan 3; Sheng, Jie 4; Xu, Ping 4; Chang, Cheng 4; Fu, Yan1,2
	2021-04-20
发表期刊	ANALYTICAL CHEMISTRY
ISSN	0003-2700
卷号	93 期号:15 页码:6094-6103
摘要	Proteolytic digestion of proteins by one or more proteases is a key step in shotgun proteomics, in which the proteolytic products, i.e., peptides, are taken as the surrogates of their parent proteins for further qualitative or quantitative analysis. The proteases generally cleave proteins at specific amino acid residue sites, but digestion is hardly complete (wide existence of missed cleavage sites). Therefore, it would be of great help to improve the prior experimental design and the posterior data analysis if the digestion behaviors of proteases can be accurately modeled and predicted. At present, systematic studies about the commonly used proteases in proteomics are insufficient, and there is a lack of easy-to-use tools to predict the cleavage sites of different proteases. Here, we propose a novel sequence-based deep learning algorithm-DeepDigest, which integrates convolutional neural networks and long short-term memory networks for protein digestion prediction. DeepDigest can predict the cleavage probability of each potential cleavage site on the protein sequences for eight popular proteases including trypsin, ArgC, chymotrypsin, GluC, LysC, AspN, LysN, and LysargiNase. We compared DeepDigest with three traditional machine learning algorithms, i.e., logistic regression, random forest, and support vector machine. On the eight training data sets, the 10-fold cross-validation accuracies (AUCs) of DeepDigest were 0.956-0.982, significantly higher than those of the three traditional algorithms. On the 11 independent test data sets, DeepDigest achieved AUCs between 0.849 and 0.978, outperforming the other traditional algorithms in most cases. Transfer learning then further improved the prediction accuracy. Besides, some interesting characteristics of different proteases were revealed and discussed. Ultimately, as an application, we used DeepDigest to predict the digestibilities of peptides and demonstrated that peptide digestibility is an informative new feature to discriminate between correct and incorrect peptide identifications.
DOI	10.1021/acs.analchem.0c04704
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of China[32070668] ; National Key R&D Program of China[2020YFE0202200] ; Innovation Foundation of Medicine of China[20SWAQX34]
WOS研究方向	Chemistry
WOS类目	Chemistry, Analytical
WOS记录号	WOS:000643539400015
出版者	AMER CHEMICAL SOC
引用统计
文献类型	期刊论文
条目标识符	http://ir.amss.ac.cn/handle/2S8OKBNM/58714
专题	应用数学研究所
通讯作者	Chang, Cheng; Fu, Yan
作者单位	1.Chinese Acad Sci, Acad Math & Syst Sci, CEMS, NCMIS,RCSDS, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Sch Math Sci, Beijing 100049, Peoples R China 3.China Univ Min & Technol, Sch Sci, Beijing 100083, Peoples R China 4.Beijing Inst Lifeom, Natl Ctr Prot Sci Beijing, Beijing Proteome Res Ctr, State Key Lab Proteom, Beijing 102206, Peoples R China
推荐引用方式 GB/T 7714	Yang, Jinghan,Gao, Zhiqiang,Ren, Xiuhan,et al. DeepDigest: Prediction of Protein Proteolytic Digestion with Deep Learning[J]. ANALYTICAL CHEMISTRY,2021,93(15):6094-6103.
APA	Yang, Jinghan.,Gao, Zhiqiang.,Ren, Xiuhan.,Sheng, Jie.,Xu, Ping.,...&Fu, Yan.(2021).DeepDigest: Prediction of Protein Proteolytic Digestion with Deep Learning.ANALYTICAL CHEMISTRY,93(15),6094-6103.
MLA	Yang, Jinghan,et al."DeepDigest: Prediction of Protein Proteolytic Digestion with Deep Learning".ANALYTICAL CHEMISTRY 93.15(2021):6094-6103.