MS/MS Spectrum Prediction for Modified Peptides Using pDeep2 Trained by Transfer Learning

doi:10.1021/acs.analchem.9b01262

	MS/MS Spectrum Prediction for Modified Peptides Using pDeep2 Trained by Transfer Learning
	Zeng, Wen-Feng 1,3; Zhou, Xie-Xuan 2,3; Zhou, Wen-Jing 1,3; Chi, Hao 1,3; Zhan, Jianfeng 2,3; He, Si-Min 1,3
	2019-08-06
发表期刊	ANALYTICAL CHEMISTRY
ISSN	0003-2700
卷号	91 期号:15 页码:9724-9731
摘要	In the past decade, tandem mass spectrometry (MS/MS)-based bottom-up proteomics has become the method of choice for analyzing post-translational modifications (PTMs) in complex mixtures. The key to the identification of the PTM-containing peptides and localization of the PTM-modified residues is to measure the similarities between the theoretical spectra and the experimental ones. An accurate prediction of the theoretical MS/MS spectra of the modified peptides will improve the similarity measurement. Here, we proposed the deeplearning-based pDeep2 model for PTMs. We used the transfer learning technique to train pDeep2, facilitating the training with a limited scale of benchmark PTM data. Using the public synthetic PTM data sets, including the synthetic phosphopeptides and 21 synthetic PTMs from ProteomeTools, we showed that the model trained by transfer learning was accurate (>80% Pearson correlation coefficients were higher than 0.9), and was significantly better than the models trained without transfer learning. We also showed that accurate prediction of the fragment ion intensities of the PTM neutral loss, for example, the phosphoric acid loss (-98 Da) of the phosphopeptide, will improve the discriminating power to distinguish the true phosphorylated residue from its adjacent candidate sites. pDeep2 is available at https://github.com/pFindStudio/pDeep/tree/master/pDeep2.
DOI	10.1021/acs.analchem.9b01262
收录类别	SCI
语种	英语
资助项目	National Key Research and Development Program of China[2016YFA0501301] ; ICT Innovation Program[Y806111000]
WOS研究方向	Chemistry
WOS类目	Chemistry, Analytical
WOS记录号	WOS:000480499200055
出版者	AMER CHEMICAL SOC
引用统计	被引频次：64[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/4444
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Zeng, Wen-Feng
作者单位	1.Chinese Acad Sci, Key Lab Intelligent Informat Proc, Beijing 100190, Peoples R China 2.Chinese Acad Sci, Inst Comp Technol, State Key Lab Comp Architecture, Beijing 100190, Peoples R China 3.Univ Chinese Acad Sci, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Zeng, Wen-Feng,Zhou, Xie-Xuan,Zhou, Wen-Jing,et al. MS/MS Spectrum Prediction for Modified Peptides Using pDeep2 Trained by Transfer Learning[J]. ANALYTICAL CHEMISTRY,2019,91(15):9724-9731.
APA	Zeng, Wen-Feng,Zhou, Xie-Xuan,Zhou, Wen-Jing,Chi, Hao,Zhan, Jianfeng,&He, Si-Min.(2019).MS/MS Spectrum Prediction for Modified Peptides Using pDeep2 Trained by Transfer Learning.ANALYTICAL CHEMISTRY,91(15),9724-9731.
MLA	Zeng, Wen-Feng,et al."MS/MS Spectrum Prediction for Modified Peptides Using pDeep2 Trained by Transfer Learning".ANALYTICAL CHEMISTRY 91.15(2019):9724-9731.