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DeepDigest: Prediction of Protein Proteolytic Digestion with Deep Learning

文献类型：期刊论文

作者Yang, Jinghan3,4; Gao, Zhiqiang3,4; Ren, Xiuhan2; Sheng, Jie1; Xu, Ping1; Chang, Cheng1; Fu, Yan3,4
刊名ANALYTICAL CHEMISTRY
出版日期2021-04-20
卷号93期号:15页码:6094-6103
ISSN号0003-2700
DOI10.1021/acs.analchem.0c04704
英文摘要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.
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
语种英语
WOS记录号WOS:000643539400015
出版者AMER CHEMICAL SOC
源URL[http://ir.amss.ac.cn/handle/2S8OKBNM/58713]  
专题应用数学研究所
通讯作者Chang, Cheng; Fu, Yan
作者单位1.Beijing Inst Lifeom, Natl Ctr Prot Sci Beijing, Beijing Proteome Res Ctr, State Key Lab Proteom, Beijing 102206, Peoples R China
2.China Univ Min & Technol, Sch Sci, Beijing 100083, Peoples R China
3.Univ Chinese Acad Sci, Sch Math Sci, Beijing 100049, Peoples R China
4.Chinese Acad Sci, Acad Math & Syst Sci, CEMS, NCMIS,RCSDS, Beijing 100190, 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.

入库方式： OAI收割

来源：数学与系统科学研究院

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