{"title":"糖肽串联质谱的深度学习预测为糖蛋白组学提供动力","authors":"Yu Zong, Yuxin Wang, Xipeng Qiu, Xuanjing Huang, Liang Qiao","doi":"10.1038/s42256-024-00875-x","DOIUrl":null,"url":null,"abstract":"Protein glycosylation, a post-translational modification of proteins by glycans, plays an important role in numerous physiological and pathological cellular functions. Glycoproteomics, the study of protein glycosylation on a proteome-wide scale, utilizes liquid chromatography coupled with tandem mass spectrometry (MS/MS) to get combinational information on glycosylation site, glycosylation level and glycan structure. However, current database searching methods for glycoproteomics often struggle with glycan structure determination due to the limited occurrence of structure-determining ions. Although spectral searching methods can leverage fragment intensity to facilitate the structure identification of glycopeptides, their application is hindered by difficulties in spectral library construction. In this work, we present DeepGP, a hybrid deep learning framework based on transformer and graph neural networks, for the prediction of MS/MS spectra and retention time of glycopeptides. Two graph neural network modules are employed to capture the branched glycan structure and predict glycan ion intensity, respectively. Additionally, a pretraining strategy is implemented to alleviate the insufficiency of glycoproteomics data. Testing on multiple biological datasets, DeepGP accurately predicts MS/MS spectra and retention time of glycopeptides, closely aligning with the experimental results. Comprehensive benchmarking of DeepGP on synthetic and biological datasets validates its effectiveness in distinguishing similar glycans. Based on various decoy methods, DeepGP in combination with database searching can increase glycopeptide detection sensitivity. We anticipate that DeepGP can inspire extensive future work in glycoproteomics. Glycosylation, a prevalent type of post-translational modification of proteins by glycan molecules, plays a major role in the proteome. Zong et al. present DeepGP, a hybrid deep learning framework based on transformer and graph neural network architectures that accurately predicts tandem mass spectra and retention times of glycopeptides, providing information on glycosylation and glycan structure.","PeriodicalId":48533,"journal":{"name":"Nature Machine Intelligence","volume":"6 8","pages":"950-961"},"PeriodicalIF":18.8000,"publicationDate":"2024-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Deep learning prediction of glycopeptide tandem mass spectra powers glycoproteomics\",\"authors\":\"Yu Zong, Yuxin Wang, Xipeng Qiu, Xuanjing Huang, Liang Qiao\",\"doi\":\"10.1038/s42256-024-00875-x\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Protein glycosylation, a post-translational modification of proteins by glycans, plays an important role in numerous physiological and pathological cellular functions. Glycoproteomics, the study of protein glycosylation on a proteome-wide scale, utilizes liquid chromatography coupled with tandem mass spectrometry (MS/MS) to get combinational information on glycosylation site, glycosylation level and glycan structure. However, current database searching methods for glycoproteomics often struggle with glycan structure determination due to the limited occurrence of structure-determining ions. Although spectral searching methods can leverage fragment intensity to facilitate the structure identification of glycopeptides, their application is hindered by difficulties in spectral library construction. In this work, we present DeepGP, a hybrid deep learning framework based on transformer and graph neural networks, for the prediction of MS/MS spectra and retention time of glycopeptides. Two graph neural network modules are employed to capture the branched glycan structure and predict glycan ion intensity, respectively. Additionally, a pretraining strategy is implemented to alleviate the insufficiency of glycoproteomics data. Testing on multiple biological datasets, DeepGP accurately predicts MS/MS spectra and retention time of glycopeptides, closely aligning with the experimental results. Comprehensive benchmarking of DeepGP on synthetic and biological datasets validates its effectiveness in distinguishing similar glycans. Based on various decoy methods, DeepGP in combination with database searching can increase glycopeptide detection sensitivity. We anticipate that DeepGP can inspire extensive future work in glycoproteomics. Glycosylation, a prevalent type of post-translational modification of proteins by glycan molecules, plays a major role in the proteome. Zong et al. present DeepGP, a hybrid deep learning framework based on transformer and graph neural network architectures that accurately predicts tandem mass spectra and retention times of glycopeptides, providing information on glycosylation and glycan structure.\",\"PeriodicalId\":48533,\"journal\":{\"name\":\"Nature Machine Intelligence\",\"volume\":\"6 8\",\"pages\":\"950-961\"},\"PeriodicalIF\":18.8000,\"publicationDate\":\"2024-07-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Nature Machine Intelligence\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://www.nature.com/articles/s42256-024-00875-x\",\"RegionNum\":1,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Nature Machine Intelligence","FirstCategoryId":"94","ListUrlMain":"https://www.nature.com/articles/s42256-024-00875-x","RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
Deep learning prediction of glycopeptide tandem mass spectra powers glycoproteomics
Protein glycosylation, a post-translational modification of proteins by glycans, plays an important role in numerous physiological and pathological cellular functions. Glycoproteomics, the study of protein glycosylation on a proteome-wide scale, utilizes liquid chromatography coupled with tandem mass spectrometry (MS/MS) to get combinational information on glycosylation site, glycosylation level and glycan structure. However, current database searching methods for glycoproteomics often struggle with glycan structure determination due to the limited occurrence of structure-determining ions. Although spectral searching methods can leverage fragment intensity to facilitate the structure identification of glycopeptides, their application is hindered by difficulties in spectral library construction. In this work, we present DeepGP, a hybrid deep learning framework based on transformer and graph neural networks, for the prediction of MS/MS spectra and retention time of glycopeptides. Two graph neural network modules are employed to capture the branched glycan structure and predict glycan ion intensity, respectively. Additionally, a pretraining strategy is implemented to alleviate the insufficiency of glycoproteomics data. Testing on multiple biological datasets, DeepGP accurately predicts MS/MS spectra and retention time of glycopeptides, closely aligning with the experimental results. Comprehensive benchmarking of DeepGP on synthetic and biological datasets validates its effectiveness in distinguishing similar glycans. Based on various decoy methods, DeepGP in combination with database searching can increase glycopeptide detection sensitivity. We anticipate that DeepGP can inspire extensive future work in glycoproteomics. Glycosylation, a prevalent type of post-translational modification of proteins by glycan molecules, plays a major role in the proteome. Zong et al. present DeepGP, a hybrid deep learning framework based on transformer and graph neural network architectures that accurately predicts tandem mass spectra and retention times of glycopeptides, providing information on glycosylation and glycan structure.
期刊介绍:
Nature Machine Intelligence is a distinguished publication that presents original research and reviews on various topics in machine learning, robotics, and AI. Our focus extends beyond these fields, exploring their profound impact on other scientific disciplines, as well as societal and industrial aspects. We recognize limitless possibilities wherein machine intelligence can augment human capabilities and knowledge in domains like scientific exploration, healthcare, medical diagnostics, and the creation of safe and sustainable cities, transportation, and agriculture. Simultaneously, we acknowledge the emergence of ethical, social, and legal concerns due to the rapid pace of advancements.
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