Dataset Information

Machine Learning Classifies Core and Outer Fucosylation of N-Glycoproteins Using Mass Spectrometry.

ABSTRACT: Protein glycosylation is known to be involved in biological progresses such as cell recognition, growth, differentiation, and apoptosis. Fucosylation of glycoproteins plays an important role for structural stability and function of N-linked glycoproteins. Although many of biological and clinical studies of protein fucosylation by fucosyltransferases has been reported, structural classification of fucosylated N-glycoproteins such as core or outer isoforms remains a challenge. Here, we report for the first time the classification of N-glycopeptides as core- and outer-fucosylated types using tandem mass spectrometry (MS/MS) and machine learning algorithms such as the deep neural network (DNN) and support vector machine (SVM). Training and test sets of more than 800 MS/MS spectra of N-glycopeptides from the immunoglobulin gamma and alpha 1-acid-glycoprotein standards were selected for classification of the fucosylation types using supervised learning models. The best-performing model had an accuracy of more than 99% against manual characterization and area under the curve values greater than 0.99, which were calculated by probability scores from target and decoy datasets. Finally, this model was applied to classify fucosylated N-glycoproteins from human plasma. A total of 82N-glycopeptides, with 54 core-, 24 outer-, and 4 dual-fucosylation types derived from 54 glycoproteins, were commonly classified as the same type in both the DNN and SVM. Specifically, outer fucosylation was dominant in tri- and tetra-antennary N-glycopeptides, while core fucosylation was dominant in the mono-, bi-antennary and hybrid types of N-glycoproteins in human plasma. Thus, the machine learning methods can be combined with MS/MS to distinguish between different isoforms of fucosylated N-glycopeptides.

SUBMITTER: Hwang H

PROVIDER: S-EPMC6962204 | biostudies-literature | 2020 Jan

REPOSITORIES: biostudies-literature

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Publications

Machine Learning Classifies Core and Outer Fucosylation of N-Glycoproteins Using Mass Spectrometry.

Hwang Heeyoun H Jeong Hoi Keun HK Lee Hyun Kyoung HK Park Gun Wook GW Lee Ju Yeon JY Lee Soo Youn SY Kang Young-Mook YM An Hyun Joo HJ Kang Jeong Gu JG Ko Jeong-Heon JH Kim Jin Young JY Yoo Jong Shin JS

Scientific reports 20200115 1

Protein glycosylation is known to be involved in biological progresses such as cell recognition, growth, differentiation, and apoptosis. Fucosylation of glycoproteins plays an important role for structural stability and function of N-linked glycoproteins. Although many of biological and clinical studies of protein fucosylation by fucosyltransferases has been reported, structural classification of fucosylated N-glycoproteins such as core or outer isoforms remains a challenge. Here, we report for ...[more]

PMID: 31941975

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Project description:Advances in the field of targeted proteomics and mass spectrometry have significantly improved assay sensitivity and multiplexing capacity. The high-throughput nature of targeted proteomics experiments has increased the rate of data production, which requires development of novel analytical tools to keep up with data processing demand. Currently, development and validation of targeted mass spectrometry assays require manual inspection of chromatographic peaks from large datasets to ensure quality, a process that is time consuming, prone to inter- and intra-operator variability and limits the efficiency of data interpretation from targeted proteomics analyses. To address this challenge, we have developed TargetedMSQC, an R package that facilitates quality control and verification of chromatographic peaks from targeted proteomics datasets. This tool calculates metrics to quantify several quality aspects of a chromatographic peak, e.g. symmetry, jaggedness and modality, co-elution and shape similarity of monitored transitions in a peak group, as well as the consistency of transitions' ratios between endogenous analytes and isotopically labeled internal standards and consistency of retention time across multiple runs. The algorithm takes advantage of supervised machine learning to identify peaks with interference or poor chromatography based on a set of peaks that have been annotated by an expert analyst. Using TargetedMSQC to analyze targeted proteomics data reduces the time spent on manual inspection of peaks and improves both speed and accuracy of interference detection. Additionally, by allowing the analysts to customize the tool for application on different datasets, TargetedMSQC gives the users the flexibility to define the acceptable quality for specific datasets. Furthermore, automated and quantitative assessment of peak quality offers a more objective and systematic framework for high throughput analysis of targeted mass spectrometry assay datasets and is a step towards more robust and faster assay implementation.

Project description:Fucosylation (Fuc) of glycoproteins plays an important role in regulating protein function and has been associated with the development of several cancer types including prostate cancer (Pca). Therefore, the research of Fuc glycoproteins has attracted increasing attention recently in the analytical field. Herein, a strategy based on lectin affinity enrichments of intact glycopeptides followed by mass spectrometry has been established to evaluate the specificities of various Fuc-binding lectins for glycosite-specific Fuc analysis of nonaggressive (NAG) and aggressive (AG) Pca cell lines. The enrichment specificities of Fuc glycopeptides using lectins (LCA, PSA, AAL, LTL, UEA I, and AOL) and MAX extraction cartridges alone, or in tandem, were evaluated. Our results showed that the use of lectin enrichment significantly increased the ratio of fucosylated glycopeptides to total glycopeptides compared to MAX enrichment. Furthermore, tandem use of lectin followed by MAX increased the number of identifications of Fuc glycopeptides compared to using lectin enrichment alone. LCA, PSA, and AOL showed stronger binding capacity than AAL, LTL, and UEA I. Also, LCA and PSA bound specifically to core Fuc, whereas AOL, AAL, and UEA I showed binding to both core Fuc and branch Fuc. The optimized enrichment method with tandem enrichment of LCA followed by MAX (LCA-MAX) was then applied to examine the Fuc glycoproteomes in two NAG and two AG Pca cell lines. In total, 973 intact Fuc glycopeptides were identified and quantified from 252 Fuc proteins by using the tandem-mass-tags (TMT) labeling and nanoliquid chromatography-mass spectrometry (nanoLC-MS/MS) analysis. Further data analysis revealed that 51 Fuc glycopeptides were overexpressed more than 2-fold in AG cell lines compared to NAG cells. The analysis of protein core fucosylation has great potential for aiding our understanding of invasive activity of AG Pca and may lead to the development of diagnostic approaches for AG Pca.

Dataset Information

Machine Learning Classifies Core and Outer Fucosylation of N-Glycoproteins Using Mass Spectrometry.

Publications

Machine Learning Classifies Core and Outer Fucosylation of N-Glycoproteins Using Mass Spectrometry.

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