MS/MS Spectrum Prediction for Modified Peptides Using pDeep2 Trained by Transfer Learning | |
Zeng, Wen-Feng2,3; Zhou, Xie-Xuan1,3; Zhou, Wen-Jing2,3; Chi, Hao2,3; Zhan, Jianfeng1,3; He, Si-Min2,3 | |
刊名 | ANALYTICAL CHEMISTRY |
2019-08-06 | |
卷号 | 91期号:15页码:9724-9731 |
ISSN号 | 0003-2700 |
DOI | 10.1021/acs.analchem.9b01262 |
英文摘要 | 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. |
资助项目 | National Key Research and Development Program of China[2016YFA0501301] ; ICT Innovation Program[Y806111000] |
WOS研究方向 | Chemistry |
语种 | 英语 |
出版者 | AMER CHEMICAL SOC |
WOS记录号 | WOS:000480499200055 |
内容类型 | 期刊论文 |
源URL | [http://119.78.100.204/handle/2XEOYT63/4443] |
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, Inst Comp Technol, State Key Lab Comp Architecture, Beijing 100190, Peoples R China 2.Chinese Acad Sci, Key Lab Intelligent Informat Proc, 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. |
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