1
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Peng W, Reyes CDG, Gautam S, Yu A, Cho BG, Goli M, Donohoo K, Mondello S, Kobeissy F, Mechref Y. MS-based glycomics and glycoproteomics methods enabling isomeric characterization. MASS SPECTROMETRY REVIEWS 2023; 42:577-616. [PMID: 34159615 PMCID: PMC8692493 DOI: 10.1002/mas.21713] [Citation(s) in RCA: 37] [Impact Index Per Article: 37.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/26/2021] [Revised: 06/01/2021] [Accepted: 06/02/2021] [Indexed: 05/03/2023]
Abstract
Glycosylation is one of the most significant and abundant posttranslational modifications in mammalian cells. It mediates a wide range of biofunctions, including cell adhesion, cell communication, immune cell trafficking, and protein stability. Also, aberrant glycosylation has been associated with various diseases such as diabetes, Alzheimer's disease, inflammation, immune deficiencies, congenital disorders, and cancers. The alterations in the distributions of glycan and glycopeptide isomers are involved in the development and progression of several human diseases. However, the microheterogeneity of glycosylation brings a great challenge to glycomic and glycoproteomic analysis, including the characterization of isomers. Over several decades, different methods and approaches have been developed to facilitate the characterization of glycan and glycopeptide isomers. Mass spectrometry (MS) has been a powerful tool utilized for glycomic and glycoproteomic isomeric analysis due to its high sensitivity and rich structural information using different fragmentation techniques. However, a comprehensive characterization of glycan and glycopeptide isomers remains a challenge when utilizing MS alone. Therefore, various separation methods, including liquid chromatography, capillary electrophoresis, and ion mobility, were developed to resolve glycan and glycopeptide isomers before MS. These separation techniques were coupled to MS for a better identification and quantitation of glycan and glycopeptide isomers. Additionally, bioinformatic tools are essential for the automated processing of glycan and glycopeptide isomeric data to facilitate isomeric studies in biological cohorts. Here in this review, we discuss commonly employed MS-based techniques, separation hyphenated MS methods, and software, facilitating the separation, identification, and quantitation of glycan and glycopeptide isomers.
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Affiliation(s)
- Wenjing Peng
- Department of Chemistry and Biochemistry, Texas Tech University, Lubbock, Texas, USA
| | | | - Sakshi Gautam
- Department of Chemistry and Biochemistry, Texas Tech University, Lubbock, Texas, USA
| | - Aiying Yu
- Department of Chemistry and Biochemistry, Texas Tech University, Lubbock, Texas, USA
| | - Byeong Gwan Cho
- Department of Chemistry and Biochemistry, Texas Tech University, Lubbock, Texas, USA
| | - Mona Goli
- Department of Chemistry and Biochemistry, Texas Tech University, Lubbock, Texas, USA
| | - Kaitlyn Donohoo
- Department of Chemistry and Biochemistry, Texas Tech University, Lubbock, Texas, USA
| | | | - Firas Kobeissy
- Program for Neurotrauma, Neuroproteomics & Biomarkers Research, Departments of Emergency Medicine, University of Florida, Gainesville, Florida, USA
| | - Yehia Mechref
- Department of Chemistry and Biochemistry, Texas Tech University, Lubbock, Texas, USA
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2
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Li S, Zhu J, Lubman DM, Zhou H, Tang H. GlycoSLASH: Concurrent Glycopeptide Identification from Multiple Related LC-MS/MS Data Sets by Using Spectral Clustering and Library Searching. J Proteome Res 2023; 22:1501-1509. [PMID: 36802412 PMCID: PMC10164058 DOI: 10.1021/acs.jproteome.3c00066] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/23/2023]
Abstract
Liquid chromatography coupled with tandem mass spectrometry is commonly adopted in large-scale glycoproteomic studies involving hundreds of disease and control samples. The software for glycopeptide identification in such data (e.g., the commercial software Byonic) analyzes the individual data set and does not exploit the redundant spectra of glycopeptides presented in the related data sets. Herein, we present a novel concurrent approach for glycopeptide identification in multiple related glycoproteomic data sets by using spectral clustering and spectral library searching. The evaluation on two large-scale glycoproteomic data sets showed that the concurrent approach can identify 105%-224% more spectra as glycopeptides compared to the glycopeptide identification on individual data sets using Byonic alone. The improvement of glycopeptide identification also enabled the discovery of several potential biomarkers of protein glycosylations in hepatocellular carcinoma patients.
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Affiliation(s)
- Sujun Li
- Department of Blood Transfusion, The First Affiliated Hospital of Nanchang University, Nanchang 330000, China.,JiangXi Key Laboratory of Transfusion Medicine, Nanchang 330000, China.,Luddy School of Informatics, Computing and Engineering, Indiana University, Bloomington, Indiana 47408, United States
| | - Jianhui Zhu
- Department of Surgery, University of Michigan, Medical Center, Ann Arbor, Michigan 48109, United States
| | - David M Lubman
- Department of Surgery, University of Michigan, Medical Center, Ann Arbor, Michigan 48109, United States
| | - He Zhou
- Shenzhen Dengding Biopharma Co. Ltd., Shenzhen 518000, China
| | - Haixu Tang
- Luddy School of Informatics, Computing and Engineering, Indiana University, Bloomington, Indiana 47408, United States
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3
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Polasky DA, Nesvizhskii AI. Recent advances in computational algorithms and software for large-scale glycoproteomics. Curr Opin Chem Biol 2023; 72:102238. [PMID: 36525809 DOI: 10.1016/j.cbpa.2022.102238] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2022] [Revised: 11/12/2022] [Accepted: 11/14/2022] [Indexed: 12/15/2022]
Abstract
Glycoproteomics, or characterizing glycosylation events at a proteome scale, has seen rapid advances in methods for analyzing glycopeptides by tandem mass spectrometry in recent years. These advances have enabled acquisition of far more comprehensive and large-scale datasets, precipitating an urgent need for improved informatics methods to analyze the resulting data. A new generation of glycoproteomics search methods has recently emerged, using glycan fragmentation to split the identification of a glycopeptide into peptide and glycan components and solve each component separately. In this review, we discuss these new methods and their implications for large-scale glycoproteomics, as well as several outstanding challenges in glycoproteomics data analysis, including validation of glycan assignments and quantitation. Finally, we provide an outlook on the future of glycoproteomics from an informatics perspective, noting the key challenges to achieving widespread and reproducible glycopeptide annotation and quantitation.
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Affiliation(s)
- Daniel A Polasky
- University of Michigan Department of Pathology, Ann Arbor, MI, USA.
| | - Alexey I Nesvizhskii
- University of Michigan Department of Pathology, Ann Arbor, MI, USA; University of Michigan Department of Computational Medicine and Bioinformatics, Ann Arbor, MI, USA.
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4
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Mackay S, Hitefield NL, Oduor IO, Roberts AB, Burch TC, Lance RS, Cunningham TD, Troyer DA, Semmes OJ, Nyalwidhe JO. Site-Specific Intact N-Linked Glycopeptide Characterization of Prostate-Specific Membrane Antigen from Metastatic Prostate Cancer Cells. ACS OMEGA 2022; 7:29714-29727. [PMID: 36061737 PMCID: PMC9435049 DOI: 10.1021/acsomega.2c02265] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/11/2022] [Accepted: 08/04/2022] [Indexed: 06/15/2023]
Abstract
The composition of N-linked glycans that are conjugated to the prostate-specific membrane antigen (PSMA) and their functional significance in prostate cancer progression have not been fully characterized. PSMA was isolated from two metastatic prostate cancer cell lines, LNCaP and MDAPCa2b, which have different tissue tropism and localization. Isolated PSMA was trypsin-digested, and intact glycopeptides were subjected to LC-HCD-EThcD-MS/MS analysis on a Tribrid Orbitrap Fusion Lumos mass spectrometer. Differential qualitative and quantitative analysis of site-specific N-glycopeptides was performed using Byonic and Byologic software. Comparative quantitative analysis demonstrates that multiple glycopeptides at asparagine residues 51, 76, 121, 195, 336, 459, 476, and 638 were in significantly different abundance in the two cell lines (p < 0.05). Biochemical analysis using endoglycosidase treatment and lectin capture confirm the MS and site occupancy data. The data demonstrate the effectiveness of the strategy for comprehensive analysis of PSMA glycopeptides. This approach will form the basis of ongoing experiments to identify site-specific glycan changes in PSMA isolated from disease-stratified clinical samples to uncover targets that may be associated with disease progression and metastatic phenotypes.
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Affiliation(s)
- Stephen Mackay
- Leroy
T. Canoles Jr. Cancer Research Center, Eastern
Virginia Medical School, Norfolk, Virginia 23507, United States
- Department
of Microbiology and Molecular Cell Biology, Eastern Virginia Medical School, Norfolk, Virginia 23507, United States
- University
of North Carolina, Chapel Hill, North Carolina 27516, United States
| | - Naomi L. Hitefield
- Leroy
T. Canoles Jr. Cancer Research Center, Eastern
Virginia Medical School, Norfolk, Virginia 23507, United States
- Department
of Microbiology and Molecular Cell Biology, Eastern Virginia Medical School, Norfolk, Virginia 23507, United States
- University
of Georgia, Athens, Georgia 30602, United
States
| | - Ian O. Oduor
- Leroy
T. Canoles Jr. Cancer Research Center, Eastern
Virginia Medical School, Norfolk, Virginia 23507, United States
| | - Autumn B. Roberts
- Leroy
T. Canoles Jr. Cancer Research Center, Eastern
Virginia Medical School, Norfolk, Virginia 23507, United States
- Department
of Microbiology and Molecular Cell Biology, Eastern Virginia Medical School, Norfolk, Virginia 23507, United States
| | - Tanya C. Burch
- Leroy
T. Canoles Jr. Cancer Research Center, Eastern
Virginia Medical School, Norfolk, Virginia 23507, United States
- Department
of Microbiology and Molecular Cell Biology, Eastern Virginia Medical School, Norfolk, Virginia 23507, United States
| | - Raymond S. Lance
- Leroy
T. Canoles Jr. Cancer Research Center, Eastern
Virginia Medical School, Norfolk, Virginia 23507, United States
- Spokane
Urology, Spokane, Washington 99202, United States
| | - Tina D. Cunningham
- School of
Health Professions, Eastern Virginia Medical
School, Norfolk, Virginia 23507, United States
| | - Dean A. Troyer
- Leroy
T. Canoles Jr. Cancer Research Center, Eastern
Virginia Medical School, Norfolk, Virginia 23507, United States
- Department
of Microbiology and Molecular Cell Biology, Eastern Virginia Medical School, Norfolk, Virginia 23507, United States
| | - Oliver J. Semmes
- Leroy
T. Canoles Jr. Cancer Research Center, Eastern
Virginia Medical School, Norfolk, Virginia 23507, United States
- Department
of Microbiology and Molecular Cell Biology, Eastern Virginia Medical School, Norfolk, Virginia 23507, United States
| | - Julius O. Nyalwidhe
- Leroy
T. Canoles Jr. Cancer Research Center, Eastern
Virginia Medical School, Norfolk, Virginia 23507, United States
- Department
of Microbiology and Molecular Cell Biology, Eastern Virginia Medical School, Norfolk, Virginia 23507, United States
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5
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Polasky DA, Geiszler DJ, Yu F, Nesvizhskii AI. Multi-attribute Glycan Identification and FDR Control for Glycoproteomics. Mol Cell Proteomics 2022; 21:100205. [PMID: 35091091 PMCID: PMC8933705 DOI: 10.1016/j.mcpro.2022.100205] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2021] [Revised: 01/10/2022] [Accepted: 01/20/2022] [Indexed: 11/18/2022] Open
Abstract
Rapidly improving methods for glycoproteomics have enabled increasingly large-scale analyses of complex glycopeptide samples, but annotating the resulting mass spectrometry data with high confidence remains a major bottleneck. We recently introduced a fast and sensitive glycoproteomics search method in our MSFragger search engine, which reports glycopeptides as a combination of a peptide sequence and the mass of the attached glycan. In samples with complex glycosylation patterns, converting this mass to a specific glycan composition is not straightforward; however, as many glycans have similar or identical masses. Here, we have developed a new method for determining the glycan composition of N-linked glycopeptides fragmented by collisional or hybrid activation that uses multiple sources of information from the spectrum, including observed glycan B-type (oxonium) and Y-type ions and mass and precursor monoisotopic selection errors to discriminate between possible glycan candidates. Combined with false discovery rate estimation for the glycan assignment, we show that this method is capable of specifically and sensitively identifying glycans in complex glycopeptide analyses and effectively controls the rate of false glycan assignments. The new method has been incorporated into the PTM-Shepherd modification analysis tool to work directly with the MSFragger glyco search in the FragPipe graphical user interface, providing a complete computational pipeline for annotation of N-glycopeptide spectra with false discovery rate control of both peptide and glycan components that is both sensitive and robust against false identifications. Identifying the glycan on intact glycopeptides remains difficult in glycoproteomics. We developed a method to assign glycan compositions in N-glycoproteomics searches. We demonstrate well-controlled glycan FDR in multiple sample types. The method annotates more glycopeptide spectra than competing tools. The method is included PTM-Shepherd for a full glycoproteomics workflow in FragPipe.
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Affiliation(s)
- Daniel A Polasky
- Department of Pathology, University of Michigan, Ann Arbor, Michigan, USA
| | - Daniel J Geiszler
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan, USA
| | - Fengchao Yu
- Department of Pathology, University of Michigan, Ann Arbor, Michigan, USA
| | - Alexey I Nesvizhskii
- Department of Pathology, University of Michigan, Ann Arbor, Michigan, USA; Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan, USA.
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6
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Hackett WE, Zaia J. The Need for Community Standards to Enable Accurate Comparison of Glycoproteomics Algorithm Performance. Molecules 2021; 26:molecules26164757. [PMID: 34443345 PMCID: PMC8398183 DOI: 10.3390/molecules26164757] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2021] [Revised: 07/30/2021] [Accepted: 08/03/2021] [Indexed: 11/16/2022] Open
Abstract
Protein glycosylation that mediates interactions among viral proteins, host receptors, and immune molecules is an important consideration for predicting viral antigenicity. Viral spike proteins, the proteins responsible for host cell invasion, are especially important to be examined. However, there is a lack of consensus within the field of glycoproteomics regarding identification strategy and false discovery rate (FDR) calculation that impedes our examinations. As a case study in the overlap between software, here as a case study, we examine recently published SARS-CoV-2 glycoprotein datasets with four glycoproteomics identification software with their recommended protocols: GlycReSoft, Byonic, pGlyco2, and MSFragger-Glyco. These software use different Target-Decoy Analysis (TDA) forms to estimate FDR and have different database-oriented search methods with varying degrees of quantification capabilities. Instead of an ideal overlap between software, we observed different sets of identifications with the intersection. When clustering by glycopeptide identifications, we see higher degrees of relatedness within software than within glycosites. Taking the consensus between results yields a conservative and non-informative conclusion as we lose identifications in the desire for caution; these non-consensus identifications are often lower abundance and, therefore, more susceptible to nuanced changes. We conclude that present glycoproteomics softwares are not directly comparable, and that methods are needed to assess their overall results and FDR estimation performance. Once such tools are developed, it will be possible to improve FDR methods and quantify complex glycoproteomes with acceptable confidence, rather than potentially misleading broad strokes.
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Affiliation(s)
| | - Joseph Zaia
- Bioinformatics Program, Boston University, Boston, MA 02215, USA;
- Department of Biochemistry, School of Medicine, Boston University, Boston, MA 02215, USA
- Correspondence:
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7
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Zhang R, Zhu J, Lubman DM, Mechref Y, Tang H. GlycoHybridSeq: Automated Identification of N-Linked Glycopeptides Using Electron Transfer/High-Energy Collision Dissociation (EThcD). J Proteome Res 2021; 20:3345-3352. [PMID: 34010560 PMCID: PMC8185882 DOI: 10.1021/acs.jproteome.1c00245] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
![]()
Glycosylation is
one of the most common post-translational modifications
(PTM) occurring in a large variety of proteins with important biological
functions in human and other higher organisms. Liquid chromatography
tandem mass spectrometry (LC-MS/MS) has been routinely used to characterize
site-specific protein glycosylation at high throughput in complex
glycoproteomic samples. Recently, electron transfer/high-energy collision
dissociation (EThcD) was introduced for glycopeptide identification,
which offers rich structural information on glycopepides with the
fragment ions from the cleavages of both the glycan and the peptide
backbone. Herein, we present the software GlycoHybridSeq for automated
interpretation of EThcD-MS/MS spectra from glycoproteomic data using
a customized scoring function, which enables the functionalities of
identifying glycopeptides, characterizing glycosylation sites, and
distinguishing some isomeric glycans. We evaluate GlycoHybridSeq on
glycoproteomic data collected for cancer biomarker discovery. The
results showed that it achieved comparable or better performance than
that of Byonic and MSFragger. GlycoHybridSeq is released as an open
source software and is ready to be used in large-scale glycoproteomic
data analyses.
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Affiliation(s)
- Rui Zhang
- Department of Computer Science, Luddy School of Informatics, Computing, and Engineering, Indiana University, Bloomington, Indiana 47405, United States
| | - Jianhui Zhu
- Department of Surgery, University of Michigan Medical Center, Ann Arbor, Michigan 48109, United States
| | - David M Lubman
- Department of Surgery, University of Michigan Medical Center, Ann Arbor, Michigan 48109, United States
| | - Yehia Mechref
- Department of Chemistry and Biochemistry, Texas Tech University, Lubbock, Texas 79409, United States
| | - Haixu Tang
- Department of Computer Science, Luddy School of Informatics, Computing, and Engineering, Indiana University, Bloomington, Indiana 47405, United States
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8
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Delafield DG, Li L. Recent Advances in Analytical Approaches for Glycan and Glycopeptide Quantitation. Mol Cell Proteomics 2021; 20:100054. [PMID: 32576592 PMCID: PMC8724918 DOI: 10.1074/mcp.r120.002095] [Citation(s) in RCA: 53] [Impact Index Per Article: 17.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2020] [Indexed: 12/13/2022] Open
Abstract
Growing implications of glycosylation in physiological occurrences and human disease have prompted intensive focus on revealing glycomic perturbations through absolute and relative quantification. Empowered by seminal methodologies and increasing capacity for detection, identification, and characterization, the past decade has provided a significant increase in the number of suitable strategies for glycan and glycopeptide quantification. Mass-spectrometry-based strategies for glycomic quantitation have grown to include metabolic incorporation of stable isotopes, deposition of mass difference and mass defect isotopic labels, and isobaric chemical labeling, providing researchers with ample tools for accurate and robust quantitation. Beyond this, workflows have been designed to harness instrument capability for label-free quantification, and numerous software packages have been developed to facilitate reliable spectrum scoring. In this review, we present and highlight the most recent advances in chemical labeling and associated techniques for glycan and glycopeptide quantification.
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Affiliation(s)
- Daniel G Delafield
- Department of Chemistry, University of Wisconsin-Madison, Madison, Wisconsin, USA
| | - Lingjun Li
- Department of Chemistry, University of Wisconsin-Madison, Madison, Wisconsin, USA; School of Pharmacy, University of Wisconsin-Madison, Madison, Wisconsin, USA.
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9
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Cao W, Liu M, Kong S, Wu M, Zhang Y, Yang P. Recent Advances in Software Tools for More Generic and Precise Intact Glycopeptide Analysis. Mol Cell Proteomics 2021; 20:100060. [PMID: 33556625 PMCID: PMC8724820 DOI: 10.1074/mcp.r120.002090] [Citation(s) in RCA: 59] [Impact Index Per Article: 19.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022] Open
Abstract
Intact glycopeptide identification has long been known as a key and challenging barrier to the comprehensive and accurate understanding the role of glycosylation in an organism. Intact glycopeptide analysis is a blossoming field that has received increasing attention in recent years. MS-based strategies and relative software tools are major drivers that have greatly facilitated the analysis of intact glycopeptides, particularly intact N-glycopeptides. This article provides a systematic review of the intact glycopeptide-identification process using MS data generated in shotgun proteomic experiments, which typically focus on N-glycopeptide analysis. Particular attention is paid to the software tools that have been recently developed in the last decade for the interpretation and quality control of glycopeptide spectra acquired using different MS strategies. The review also provides information about the characteristics and applications of these software tools, discusses their advantages and disadvantages, and concludes with a discussion of outstanding tools.
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Affiliation(s)
- Weiqian Cao
- The Fifth People's Hospital of Fudan University and Institutes of Biomedical Sciences, Fudan University, Shanghai, China; NHC Key Laboratory of Glycoconjugates Research, Fudan University, Shanghai, China; The Shanghai Key Laboratory of Medical Epigenetics and the International Co-laboratory of Medical Epigenetics and Metabolism, Ministry of Science and Technology, Fudan University, Shanghai, China.
| | - Mingqi Liu
- The Fifth People's Hospital of Fudan University and Institutes of Biomedical Sciences, Fudan University, Shanghai, China
| | - Siyuan Kong
- The Fifth People's Hospital of Fudan University and Institutes of Biomedical Sciences, Fudan University, Shanghai, China
| | - Mengxi Wu
- The Fifth People's Hospital of Fudan University and Institutes of Biomedical Sciences, Fudan University, Shanghai, China; Department of Chemistry, Fudan University, Shanghai, China
| | - Yang Zhang
- The Fifth People's Hospital of Fudan University and Institutes of Biomedical Sciences, Fudan University, Shanghai, China; The Shanghai Key Laboratory of Medical Epigenetics and the International Co-laboratory of Medical Epigenetics and Metabolism, Ministry of Science and Technology, Fudan University, Shanghai, China
| | - Pengyuan Yang
- The Fifth People's Hospital of Fudan University and Institutes of Biomedical Sciences, Fudan University, Shanghai, China; NHC Key Laboratory of Glycoconjugates Research, Fudan University, Shanghai, China; The Shanghai Key Laboratory of Medical Epigenetics and the International Co-laboratory of Medical Epigenetics and Metabolism, Ministry of Science and Technology, Fudan University, Shanghai, China; Department of Chemistry, Fudan University, Shanghai, China.
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10
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Hackett WE, Zaia J. Calculating Glycoprotein Similarities From Mass Spectrometric Data. Mol Cell Proteomics 2021; 20:100028. [PMID: 32883803 PMCID: PMC8724611 DOI: 10.1074/mcp.r120.002223] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2020] [Revised: 08/24/2020] [Accepted: 09/03/2020] [Indexed: 12/23/2022] Open
Abstract
Complex protein glycosylation occurs through biosynthetic steps in the secretory pathway that create macro- and microheterogeneity of structure and function. Required for all life forms, glycosylation diversifies and adapts protein interactions with binding partners that underpin interactions at cell surfaces and pericellular and extracellular environments. Because these biological effects arise from heterogeneity of structure and function, it is necessary to measure their changes as part of the quest to understand nature. Quite often, however, the assumption behind proteomics that posttranslational modifications are discrete additions that can be modeled using the genome as a template does not apply to protein glycosylation. Rather, it is necessary to quantify the glycosylation distribution at each glycosite and to aggregate this information into a population of mature glycoproteins that exist in a given biological system. To date, mass spectrometric methods for assigning singly glycosylated peptides are well-established. But it is necessary to quantify glycosylation heterogeneity accurately in order to gauge the alterations that occur during biological processes. The task is to quantify the glycosylated peptide forms as accurately as possible and then apply appropriate bioinformatics algorithms to the calculation of micro- and macro-similarities. In this review, we summarize current approaches for protein quantification as they apply to this glycoprotein similarity problem.
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Affiliation(s)
- William E Hackett
- Bioinformatics Program, Boston University, Boston, Massachusetts, USA
| | - Joseph Zaia
- Bioinformatics Program, Boston University, Boston, Massachusetts, USA; Department of Biochemistry, Boston University, Boston, Massachusetts, USA.
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11
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Abstract
Glycoproteomics is unquestionably on the rise and its current development benefits from past experience in proteomics, in particular when attending to bioinformatics needs. An extensive range of software solutions is available, but the reproducibility of mass spectrometry data processing remains challenging. One of the key issues in running automated glycopeptide identification software is the selection of a reference glycan composition file. The default choices are often too broad, and a fastidious literature search to properly target this selection can be avoided. This chapter suggests the use of GlyConnect Compozitor to collect relevant information on glycosylation in a given tissue or cell line and shape an appropriate glycan composition set that can be input in the majority of search engines accommodating user-defined compositions.
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12
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Schulze S, Oltmanns A, Fufezan C, Krägenbring J, Mormann M, Pohlschröder M, Hippler M. SugarPy facilitates the universal, discovery-driven analysis of intact glycopeptides. Bioinformatics 2020; 36:5330-5336. [PMID: 33325487 DOI: 10.1093/bioinformatics/btaa1042] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2020] [Revised: 10/26/2020] [Accepted: 12/07/2020] [Indexed: 02/07/2023] Open
Abstract
MOTIVATION Protein glycosylation is a complex post-translational modification with crucial cellular functions in all domains of life. Currently, large-scale glycoproteomics approaches rely on glycan database dependent algorithms and are thus unsuitable for discovery-driven analyses of glycoproteomes. RESULTS Therefore, we devised SugarPy, a glycan database independent Python module, and validated it on the glycoproteome of human breast milk. We further demonstrated its applicability by analyzing glycoproteomes with uncommon glycans stemming from the green alga Chlamydomonas reinhardtii and the archaeon Haloferax volcanii. SugarPy also facilitated the novel characterization of glycoproteins from the red alga Cyanidioschyzon merolae. AVAILABILITY The source code is freely available on GitHub (https://github.com/SugarPy/SugarPy), and its implementation in Python ensures support for all operating systems. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Stefan Schulze
- University of Pennsylvania, Department of Biology, Leidy Laboratories, Philadelphia, USA.,University of Muenster, Institute of Plant Biology and Biotechnology, Muenster, Germany
| | - Anne Oltmanns
- University of Muenster, Institute of Plant Biology and Biotechnology, Muenster, Germany
| | - Christian Fufezan
- University of Muenster, Institute of Plant Biology and Biotechnology, Muenster, Germany.,Heidelberg University, Institute of Pharmacy and Molecular Biotechnology, Heidelberg, Germany
| | - Julia Krägenbring
- University of Muenster, Institute of Plant Biology and Biotechnology, Muenster, Germany.,University of Muenster, Institute for Hygiene, Muenster, Germany
| | - Michael Mormann
- University of Muenster, Institute for Hygiene, Muenster, Germany
| | - Mechthild Pohlschröder
- University of Pennsylvania, Department of Biology, Leidy Laboratories, Philadelphia, USA
| | - Michael Hippler
- University of Muenster, Institute of Plant Biology and Biotechnology, Muenster, Germany.,Institute of Plant Science and Resources, Okayama University, Kurashiki, Japan
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13
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Zhu H, Aloor A, Ma C, Kondengaden SM, Wang PG. Mass Spectrometric Analysis of Protein Glycosylation. ACS SYMPOSIUM SERIES 2020. [DOI: 10.1021/bk-2020-1346.ch010] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/09/2023]
Affiliation(s)
- He Zhu
- These authors contributed equally
| | | | | | | | - Peng George Wang
- Current Address: Department of Chemistry, Southern University of Science and Technology, Shenzhen, Guangdong 518055, P. R. China
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14
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Deng Z, Yi X, Chu J, Zhuang Y. A study on enhanced O-glycosylation strategy for improved production of recombinant human chorionic gonadotropin in Chinese hamster ovary cells. J Biotechnol 2019; 306:159-168. [PMID: 31604106 DOI: 10.1016/j.jbiotec.2019.10.006] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2019] [Revised: 09/24/2019] [Accepted: 10/07/2019] [Indexed: 11/20/2022]
Abstract
Human chorionic gonadotropin (hCG) is a glycoprotein hormone that exists as a heterodimer comprised of an α subunit and β subunit linked with disulfide bridges. The β subunit contains four O-glycosylation sites. Previous studies have found that the translation of mRNA to polypeptides of the β subunit was a severely limiting step for the expression of recombinant hCG protein in Chinese hamster ovary (CHO) cells. The effects of O-glycosylation on recombinant hCG protein expression were assessed by adding O-glycan precursors and overexpressing and knocking down key regulatory genes of O-glycan precursor synthesis and O-glycan sugar chain synthesis or hydrolases. The results indicated that O-glycosylation was indeed limiting in the expression of recombinant hCG protein, and N-acetylgalactosamine (GalNAc) was the major limiting precursor. Glutamine-fructose-6-phosphate transaminase 2 (Gfat2) and Uridine diphosphate-glucose pyrophosphorylase 2 (Ugp2), key regulatory genes of O-glycan precursor synthesis, were overexpressed. Ugp2 overexpression significantly increased the recombinant hCG protein level by 1.92 times compared to that of the control. The LC-MS/MS analysis and Phaseolus vulgaris leucoagglutinin (PHA-L) lectin blot analysis showed that Ugp2 overexpression significantly increased the total galactosylation levels of intracellular proteins and the O-glycosylation of recombinant hCG protein. The stability of the hCG protein to trypsin digestion was also enhanced. Ugp2 is the major limiting enzyme of the O-glycan precursor synthesis in recombinant hCG protein production. Furthermore, the effects and mechanisms of the key genes of O-glycan sugar chain synthesis and hydrolases such as polypeptide N-acetylgalactosaminyltransferase1 (Galnt1), Core 1 synthase, glycoprotein-N-acetylgalactosamine 3-beta-galactosyltransferase (C1galt1), O-linked N-acetylglucosamine transferase (Ogt) and Hexosaminidase (Hex), were evaluated. The results indicated that Galnt1 overexpression increased the recombinant hCG protein level by 1.57 times and improved the total galactosylation of intracellular proteins, O-glycosylation and the stability of recombinant hCG protein. Galnt1 is the major limiting enzyme of O-glycan sugar chain synthesis. Overexpression of Ugp2 and Galnt1 simultaneously improved the recombinant hCG protein level by 2.44 times, and both had synergistic effects. Based on the results of overexpression of Galnt1, the major limiting gene of O-Glycan chain synthesis, the precursors GalNAc and Gal were added and increased the recombinant hCG protein level by 3.68 times. This study revealed the major limiting factors of O-glycosylation of recombinant hCG protein in CHO cells and proposed an effective expression regulation strategy.
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Affiliation(s)
- Zhe Deng
- State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, Shanghai, 200137, China
| | - Xiaoping Yi
- State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, Shanghai, 200137, China.
| | - Ju Chu
- State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, Shanghai, 200137, China
| | - Yingping Zhuang
- State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, Shanghai, 200137, China
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15
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Shipman JT, Su X, Hua D, Desaire H. DecoyDeveloper: An On-Demand, De Novo Decoy Glycopeptide Generator. J Proteome Res 2019; 18:2896-2902. [PMID: 31129958 DOI: 10.1021/acs.jproteome.9b00203] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
Glycopeptide analysis is a growing field that is struggling to adopt effective, automated tools. Many creative workflows and software apps have emerged recently that offer promising capabilities for assigning glycopeptides to MS data in an automated fashion. The effectiveness of these tools is best measured and improved by determining how often they would select a glycopeptide decoy as a spectral match, instead of its correct assignment; yet generating the appropriate number and type of glycopeptide decoys can be challenging. To address this need, we have designed DecoyDeveloper, an on-demand decoy glycopeptide generator that can produce a high volume of decoys with low mass differences. DecoyDeveloper has a simple user interface and is capable of producing large sets of decoys containing complete, biologically relevant glycan and peptide sequences. We demonstrate the tool's efficiency by applying it to a set of 80 glycopeptide targets. This tool is freely available and can be found at http://glycopro.chem.ku.edu/J1.php .
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Affiliation(s)
- Joshua T Shipman
- Department of Chemistry , University of Kansas , Lawrence , Kansas 66045 , United States
| | - Xiaomeng Su
- Department of Chemistry , University of Kansas , Lawrence , Kansas 66045 , United States
| | - David Hua
- Department of Chemistry , University of Kansas , Lawrence , Kansas 66045 , United States
| | - Heather Desaire
- Department of Chemistry , University of Kansas , Lawrence , Kansas 66045 , United States
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16
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Choo MS, Wan C, Rudd PM, Nguyen-Khuong T. GlycopeptideGraphMS: Improved Glycopeptide Detection and Identification by Exploiting Graph Theoretical Patterns in Mass and Retention Time. Anal Chem 2019; 91:7236-7244. [PMID: 31079452 DOI: 10.1021/acs.analchem.9b00594] [Citation(s) in RCA: 36] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
The leading proteomic method for identifying N-glycosylated peptides is liquid chromatography coupled with tandem fragmentation mass spectrometry (LCMS/MS) followed by spectral matching of MS/MS fragment masses to a database of possible glycan and peptide combinations. Such database-dependent approaches come with challenges such as needing high-quality informative MS/MS spectra, ignoring unexpected glycan or peptide sequences, and making incorrect assignments because some glycan combinations are equivalent in mass to amino acids. To address these challenges, we present GlycopeptideGraphMS, a graph theoretical bioinformatic approach complementary to the database-dependent method. Using the AXL receptor tyrosine kinase (AXL) as a model glycoprotein with multiple N-glycosylation sites, we show that those LCMS features that could be grouped into graph networks on the basis of glycan mass and retention time differences were actually N-glycopeptides with the same peptide backbone but different N-glycan compositions. Conversely, unglycosylated peptides did not exhibit this grouping behavior. Furthermore, MS/MS sequencing of the glycan and peptide composition of just one N-glycopeptide in the graph was sufficient to identify the rest of the N-glycopeptides in the graph. By validating the identifications with exoglycosidase cocktails and MS/MS fragmentation, we determined the experimental false discovery rate of identifications to be 2.21%. GlycopeptideGraphMS detected more than 500 unique N-glycopeptides from AXL, triple the number found by a database search with Byonic software, and detected incorrect assignments due to a nonspecific protease cleavage. This method overcomes some limitations of the database approach and is a step closer to comprehensive automated glycoproteomics.
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Affiliation(s)
- Matthew S Choo
- Bioprocessing Technology Institute , 20 Biopolis Way #06-01 , Singapore 138668
| | - Corrine Wan
- Bioprocessing Technology Institute , 20 Biopolis Way #06-01 , Singapore 138668
| | - Pauline M Rudd
- Bioprocessing Technology Institute , 20 Biopolis Way #06-01 , Singapore 138668.,National Institute for Bioprocessing Research and Training , Conway Institute , Dublin , Ireland.,University College Dublin, Belfield , Dublin , Ireland
| | - Terry Nguyen-Khuong
- Bioprocessing Technology Institute , 20 Biopolis Way #06-01 , Singapore 138668
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17
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Yu A, Zhao J, Peng W, Banazadeh A, Williamson SD, Goli M, Huang Y, Mechref Y. Advances in mass spectrometry-based glycoproteomics. Electrophoresis 2018; 39:3104-3122. [PMID: 30203847 PMCID: PMC6375712 DOI: 10.1002/elps.201800272] [Citation(s) in RCA: 70] [Impact Index Per Article: 11.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2018] [Revised: 09/03/2018] [Accepted: 09/03/2018] [Indexed: 12/13/2022]
Abstract
Protein glycosylation, an important PTM, plays an essential role in a wide range of biological processes such as immune response, intercellular signaling, inflammation, and host-pathogen interaction. Aberrant glycosylation has been correlated with various diseases. However, studying protein glycosylation remains challenging because of low abundance, microheterogeneities of glycosylation sites, and poor ionization efficiency of glycopeptides. Therefore, the development of sensitive and accurate approaches to characterize protein glycosylation is crucial. The identification and characterization of protein glycosylation by MS is referred to as the field of glycoproteomics. Methods such as enrichment, metabolic labeling, and derivatization of glycopeptides in conjunction with different MS techniques and bioinformatics tools, have been developed to achieve an unequivocal quantitative and qualitative characterization of glycoproteins. This review summarizes the recent developments in the field of glycoproteomics over the past 6 years (2012 to 2018).
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Affiliation(s)
- Aiying Yu
- Department of Chemistry and Biochemistry, Texas Tech University, Lubbock, Texas 79409, United States
| | - Jingfu Zhao
- Department of Chemistry and Biochemistry, Texas Tech University, Lubbock, Texas 79409, United States
| | - Wenjing Peng
- Department of Chemistry and Biochemistry, Texas Tech University, Lubbock, Texas 79409, United States
| | - Alireza Banazadeh
- Department of Chemistry and Biochemistry, Texas Tech University, Lubbock, Texas 79409, United States
| | - Seth D. Williamson
- Department of Chemistry and Biochemistry, Texas Tech University, Lubbock, Texas 79409, United States
| | - Mona Goli
- Department of Chemistry and Biochemistry, Texas Tech University, Lubbock, Texas 79409, United States
| | - Yifan Huang
- Department of Chemistry and Biochemistry, Texas Tech University, Lubbock, Texas 79409, United States
| | - Yehia Mechref
- Department of Chemistry and Biochemistry, Texas Tech University, Lubbock, Texas 79409, United States
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18
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Pioch M, Hoffmann M, Pralow A, Reichl U, Rapp E. glyXtoolMS: An Open-Source Pipeline for Semiautomated Analysis of Glycopeptide Mass Spectrometry Data. Anal Chem 2018; 90:11908-11916. [DOI: 10.1021/acs.analchem.8b02087] [Citation(s) in RCA: 26] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Affiliation(s)
- Markus Pioch
- Bioprocess Engineering, Max Planck Institute for Dynamics of Complex Technical Systems, 39106, Magdeburg, Germany
| | - Marcus Hoffmann
- Bioprocess Engineering, Max Planck Institute for Dynamics of Complex Technical Systems, 39106, Magdeburg, Germany
| | - Alexander Pralow
- Bioprocess Engineering, Max Planck Institute for Dynamics of Complex Technical Systems, 39106, Magdeburg, Germany
- glyXera GmbH, 39120, Magdeburg, Germany
| | - Udo Reichl
- Bioprocess Engineering, Max Planck Institute for Dynamics of Complex Technical Systems, 39106, Magdeburg, Germany
- Bioprocess Engineering, Otto-von-Guericke University, 39106, Magdeburg, Germany
| | - Erdmann Rapp
- Bioprocess Engineering, Max Planck Institute for Dynamics of Complex Technical Systems, 39106, Magdeburg, Germany
- glyXera GmbH, 39120, Magdeburg, Germany
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19
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Harvey DJ. Analysis of carbohydrates and glycoconjugates by matrix-assisted laser desorption/ionization mass spectrometry: An update for 2013-2014. MASS SPECTROMETRY REVIEWS 2018; 37:353-491. [PMID: 29687922 DOI: 10.1002/mas.21530] [Citation(s) in RCA: 40] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/16/2016] [Accepted: 11/29/2016] [Indexed: 06/08/2023]
Abstract
This review is the eighth update of the original article published in 1999 on the application of Matrix-assisted laser desorption/ionization mass spectrometry (MALDI) mass spectrometry to the analysis of carbohydrates and glycoconjugates and brings coverage of the literature to the end of 2014. Topics covered in the first part of the review include general aspects such as theory of the MALDI process, matrices, derivatization, MALDI imaging, fragmentation, and arrays. The second part of the review is devoted to applications to various structural types such as oligo- and poly- saccharides, glycoproteins, glycolipids, glycosides, and biopharmaceuticals. Much of this material is presented in tabular form. The third part of the review covers medical and industrial applications of the technique, studies of enzyme reactions, and applications to chemical synthesis. © 2018 Wiley Periodicals, Inc. Mass Spec Rev 37:353-491, 2018.
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Affiliation(s)
- David J Harvey
- Target Discovery Institute, Nuffield Department of Medicine, University of Oxford, Roosevelt Drive, Oxford, OX3 7FZ, United Kingdom
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20
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Togayachi A, Tomioka A, Fujita M, Sukegawa M, Noro E, Takakura D, Miyazaki M, Shikanai T, Narimatsu H, Kaji H. Identification of Poly-N-Acetyllactosamine-Carrying Glycoproteins from HL-60 Human Promyelocytic Leukemia Cells Using a Site-Specific Glycome Analysis Method, Glyco-RIDGE. JOURNAL OF THE AMERICAN SOCIETY FOR MASS SPECTROMETRY 2018; 29:1138-1152. [PMID: 29675740 PMCID: PMC6004004 DOI: 10.1007/s13361-018-1938-6] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/01/2017] [Revised: 03/05/2018] [Accepted: 03/05/2018] [Indexed: 05/15/2023]
Abstract
To elucidate the relationship between the protein function and the diversity and heterogeneity of glycans conjugated to the protein, glycosylation sites, glycan variation, and glycan proportions at each site of the glycoprotein must be analyzed. Glycopeptide-based structural analysis technology using mass spectrometry has been developed; however, complicated analyses of complex spectra obtained by multistage fragmentation are necessary, and sensitivity and throughput of the analyses are low. Therefore, we developed a liquid chromatography/mass spectrometry (MS)-based glycopeptide analysis method to reveal the site-specific glycome (Glycan heterogeneity-based Relational IDentification of Glycopeptide signals on Elution profile, Glyco-RIDGE). This method used accurate masses and retention times of glycopeptides, without requiring MS2, and could be applied to complex mixtures. To increase the number of identified peptide, fractionation of sample glycopeptides for reduction of sample complexity is required. Therefore, in this study, glycopeptides were fractionated into four fractions by hydrophilic interaction chromatography, and each fraction was analyzed using the Glyco-RIDGE method. As a result, many glycopeptides having long glycans were enriched in the highest hydrophilic fraction. Based on the monosaccharide composition, these glycans were thought to be poly-N-acetyllactosamine (polylactosamine [pLN]), and 31 pLN-carrier proteins were identified in HL-60 cells. Gene ontology enrichment analysis revealed that pLN carriers included many molecules related to signal transduction, receptors, and cell adhesion. Thus, these findings provided important insights into the analysis of the glycoproteome using our novel Glyco-RIDGE method. Graphical Abstract ᅟ.
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Affiliation(s)
- Akira Togayachi
- Glycoscience & Glycotechnology Research Group, Biotechnology Research Institute for Drug Discovery, National Institute of Advanced Industrial Science & Technology, Tsukuba, Ibaraki, 305-8568, Japan
| | - Azusa Tomioka
- Glycoscience & Glycotechnology Research Group, Biotechnology Research Institute for Drug Discovery, National Institute of Advanced Industrial Science & Technology, Tsukuba, Ibaraki, 305-8568, Japan
| | - Mika Fujita
- Glycoscience & Glycotechnology Research Group, Biotechnology Research Institute for Drug Discovery, National Institute of Advanced Industrial Science & Technology, Tsukuba, Ibaraki, 305-8568, Japan
| | - Masako Sukegawa
- Glycoscience & Glycotechnology Research Group, Biotechnology Research Institute for Drug Discovery, National Institute of Advanced Industrial Science & Technology, Tsukuba, Ibaraki, 305-8568, Japan
| | - Erika Noro
- Glycoscience & Glycotechnology Research Group, Biotechnology Research Institute for Drug Discovery, National Institute of Advanced Industrial Science & Technology, Tsukuba, Ibaraki, 305-8568, Japan
| | - Daisuke Takakura
- Project for utilizing glycans in the development of innovative drug discovery technologies, Japan Bioindustry Association (JBA), Hatchobori, Chuo-ku, Tokyo, 104-0032, Japan
| | - Michiyo Miyazaki
- Project for utilizing glycans in the development of innovative drug discovery technologies, Japan Bioindustry Association (JBA), Hatchobori, Chuo-ku, Tokyo, 104-0032, Japan
| | - Toshihide Shikanai
- Glycoscience & Glycotechnology Research Group, Biotechnology Research Institute for Drug Discovery, National Institute of Advanced Industrial Science & Technology, Tsukuba, Ibaraki, 305-8568, Japan
| | - Hisashi Narimatsu
- Glycoscience & Glycotechnology Research Group, Biotechnology Research Institute for Drug Discovery, National Institute of Advanced Industrial Science & Technology, Tsukuba, Ibaraki, 305-8568, Japan.
| | - Hiroyuki Kaji
- Glycoscience & Glycotechnology Research Group, Biotechnology Research Institute for Drug Discovery, National Institute of Advanced Industrial Science & Technology, Tsukuba, Ibaraki, 305-8568, Japan.
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21
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Bollineni RC, Koehler CJ, Gislefoss RE, Anonsen JH, Thiede B. Large-scale intact glycopeptide identification by Mascot database search. Sci Rep 2018; 8:2117. [PMID: 29391424 PMCID: PMC5795011 DOI: 10.1038/s41598-018-20331-2] [Citation(s) in RCA: 52] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2017] [Accepted: 01/15/2018] [Indexed: 01/16/2023] Open
Abstract
Workflows capable of determining glycopeptides in large-scale are missing in the field of glycoproteomics. We present an approach for automated annotation of intact glycopeptide mass spectra. The steps in adopting the Mascot search engine for intact glycopeptide analysis included: (i) assigning one letter codes for monosaccharides, (ii) linearizing glycan sequences and (iii) preparing custom glycoprotein databases. Automated annotation of both N- and O-linked glycopeptides was proven using standard glycoproteins. In a large-scale study, a total of 257 glycoproteins containing 970 unique glycosylation sites and 3447 non-redundant N-linked glycopeptide variants were identified in 24 serum samples. Thus, a single tool was developed that collectively allows the (i) elucidation of N- and O-linked glycopeptide spectra, (ii) matching glycopeptides to known protein sequences, and (iii) high-throughput, batch-wise analysis of large-scale glycoproteomics data sets.
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Affiliation(s)
| | | | - Randi Elin Gislefoss
- Cancer Registry of Norway, Institute of Population-based Cancer Research, Oslo, Norway
| | | | - Bernd Thiede
- Department of Biosciences, University of Oslo, Oslo, Norway.
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22
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Affiliation(s)
- Nicholas
M. Riley
- Department
of Chemistry, University of Wisconsin-Madison, Madison, Wisconsin 53706, United States
- Genome
Center of Wisconsin, University of Wisconsin-Madison, Madison, Wisconsin 53706, United States
| | - Joshua J. Coon
- Department
of Chemistry, University of Wisconsin-Madison, Madison, Wisconsin 53706, United States
- Genome
Center of Wisconsin, University of Wisconsin-Madison, Madison, Wisconsin 53706, United States
- Department
of Biomolecular Chemistry, University of
Wisconsin-Madison, Madison, Wisconsin 53706, United States
- Morgridge
Institute for Research, Madison, Wisconsin 53715, United States
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23
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Lakbub JC, Su X, Zhu Z, Patabandige MW, Hua D, Go EP, Desaire H. Two New Tools for Glycopeptide Analysis Researchers: A Glycopeptide Decoy Generator and a Large Data Set of Assigned CID Spectra of Glycopeptides. J Proteome Res 2017; 16:3002-3008. [PMID: 28691494 DOI: 10.1021/acs.jproteome.7b00289] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Abstract
The glycopeptide analysis field is tightly constrained by a lack of effective tools that translate mass spectrometry data into meaningful chemical information, and perhaps the most challenging aspect of building effective glycopeptide analysis software is designing an accurate scoring algorithm for MS/MS data. We provide the glycoproteomics community with two tools to address this challenge. The first tool, a curated set of 100 expert-assigned CID spectra of glycopeptides, contains a diverse set of spectra from a variety of glycan types; the second tool, Glycopeptide Decoy Generator, is a new software application that generates glycopeptide decoys de novo. We developed these tools so that emerging methods of assigning glycopeptides' CID spectra could be rigorously tested. Software developers or those interested in developing skills in expert (manual) analysis can use these tools to facilitate their work. We demonstrate the tools' utility in assessing the quality of one particular glycopeptide software package, GlycoPep Grader, which assigns glycopeptides to CID spectra. We first acquired the set of 100 expert assigned CID spectra; then, we used the Decoy Generator (described herein) to generate 20 decoys per target glycopeptide. The assigned spectra and decoys were used to test the accuracy of GlycoPep Grader's scoring algorithm; new strengths and weaknesses were identified in the algorithm using this approach. Both newly developed tools are freely available. The software can be downloaded at http://glycopro.chem.ku.edu/GPJ.jar.
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Affiliation(s)
- Jude C Lakbub
- Ralph N. Adams Institute for Bioanalytical Chemistry, Department of Chemistry, University of Kansas , Lawrence, Kansas 66047, United States
| | - Xiaomeng Su
- Ralph N. Adams Institute for Bioanalytical Chemistry, Department of Chemistry, University of Kansas , Lawrence, Kansas 66047, United States
| | - Zhikai Zhu
- Ralph N. Adams Institute for Bioanalytical Chemistry, Department of Chemistry, University of Kansas , Lawrence, Kansas 66047, United States
| | - Milani W Patabandige
- Ralph N. Adams Institute for Bioanalytical Chemistry, Department of Chemistry, University of Kansas , Lawrence, Kansas 66047, United States
| | - David Hua
- Ralph N. Adams Institute for Bioanalytical Chemistry, Department of Chemistry, University of Kansas , Lawrence, Kansas 66047, United States
| | - Eden P Go
- Ralph N. Adams Institute for Bioanalytical Chemistry, Department of Chemistry, University of Kansas , Lawrence, Kansas 66047, United States
| | - Heather Desaire
- Ralph N. Adams Institute for Bioanalytical Chemistry, Department of Chemistry, University of Kansas , Lawrence, Kansas 66047, United States
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24
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Hu H, Khatri K, Zaia J. Algorithms and design strategies towards automated glycoproteomics analysis. MASS SPECTROMETRY REVIEWS 2017; 36:475-498. [PMID: 26728195 PMCID: PMC4931994 DOI: 10.1002/mas.21487] [Citation(s) in RCA: 71] [Impact Index Per Article: 10.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/10/2015] [Accepted: 11/30/2015] [Indexed: 05/09/2023]
Abstract
Glycoproteomics involves the study of glycosylation events on protein sequences ranging from purified proteins to whole proteome scales. Understanding these complex post-translational modification (PTM) events requires elucidation of the glycan moieties (monosaccharide sequences and glycosidic linkages between residues), protein sequences, as well as site-specific attachment of glycan moieties onto protein sequences, in a spatial and temporal manner in a variety of biological contexts. Compared with proteomics, bioinformatics for glycoproteomics is immature and many researchers still rely on tedious manual interpretation of glycoproteomics data. As sample preparation protocols and analysis techniques have matured, the number of publications on glycoproteomics and bioinformatics has increased substantially; however, the lack of consensus on tool development and code reuse limits the dissemination of bioinformatics tools because it requires significant effort to migrate a computational tool tailored for one method design to alternative methods. This review discusses algorithms and methods in glycoproteomics, and refers to the general proteomics field for potential solutions. It also introduces general strategies for tool integration and pipeline construction in order to better serve the glycoproteomics community. © 2016 Wiley Periodicals, Inc. Mass Spec Rev 36:475-498, 2017.
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Affiliation(s)
- Han Hu
- Bioinformatics Program, Boston University, Boston, Massachusetts 02215, USA
- Center for Biomedical Mass Spectrometry, Department of Biochemistry, Boston University School of Medicine, Boston University, Boston, Massachusetts 02118, USA
| | - Kshitij Khatri
- Center for Biomedical Mass Spectrometry, Department of Biochemistry, Boston University School of Medicine, Boston University, Boston, Massachusetts 02118, USA
| | - Joseph Zaia
- Center for Biomedical Mass Spectrometry, Department of Biochemistry, Boston University School of Medicine, Boston University, Boston, Massachusetts 02118, USA
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25
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Zhu H, Qiu C, Ruth AC, Keire DA, Ye H. A LC-MS All-in-One Workflow for Site-Specific Location, Identification and Quantification of N-/O- Glycosylation in Human Chorionic Gonadotropin Drug Products. AAPS JOURNAL 2017; 19:846-855. [DOI: 10.1208/s12248-017-0062-z] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/22/2016] [Accepted: 02/14/2017] [Indexed: 01/01/2023]
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26
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Abstract
Protein glycosylation is one of the most important posttranslational modifications. Numerous biological functions are related to protein glycosylation. However, analytical challenges remain in the glycoprotein analysis. To overcome the challenges associated with glycoprotein analysis, many analytical techniques were developed in recent years. Enrichment methods were used to improve the sensitivity of detection, while HPLC and mass spectrometry methods were developed to facilitate the separation of glycopeptides/proteins and enhance detection, respectively. Fragmentation techniques applied in modern mass spectrometers allow the structural interpretation of glycopeptides/proteins, while automated software tools started replacing manual processing to improve the reliability and throughput of the analysis. In this chapter, the current methodologies of glycoprotein analysis were discussed. Multiple analytical techniques are compared, and advantages and disadvantages of each technique are highlighted.
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27
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Banazadeh A, Veillon L, Wooding KM, Zabet-Moghaddam M, Mechref Y. Recent advances in mass spectrometric analysis of glycoproteins. Electrophoresis 2016; 38:162-189. [PMID: 27757981 DOI: 10.1002/elps.201600357] [Citation(s) in RCA: 71] [Impact Index Per Article: 8.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2016] [Revised: 09/23/2016] [Accepted: 09/24/2016] [Indexed: 12/13/2022]
Abstract
Glycosylation is one of the most common posttranslational modifications of proteins that plays essential roles in various biological processes, including protein folding, host-pathogen interaction, immune response, and inflammation and aberrant protein glycosylation is a well-known event in various disease states including cancer. As a result, it is critical to develop rapid and sensitive methods for the analysis of abnormal glycoproteins associated with diseases. Mass spectrometry (MS) in conjunction with different separation methods, such as capillary electrophoresis (CE), ion mobility (IM), and high performance liquid chromatography (HPLC) has become a popular tool for glycoprotein analysis, providing highly informative fragments for structural identification of glycoproteins. This review provides an overview of the developments and accomplishments in the field of glycomics and glycoproteomics reported between 2014 and 2016.
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Affiliation(s)
- Alireza Banazadeh
- Department of Chemistry and Biochemistry, Texas Tech University, Lubbock, TX, USA
| | - Lucas Veillon
- Department of Chemistry and Biochemistry, Texas Tech University, Lubbock, TX, USA
| | - Kerry M Wooding
- Department of Chemistry and Biochemistry, Texas Tech University, Lubbock, TX, USA
| | | | - Yehia Mechref
- Department of Chemistry and Biochemistry, Texas Tech University, Lubbock, TX, USA.,Center for Biotechnology and Genomics, Texas Tech University, Lubbock, TX, USA
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28
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Lee JY, Lee HK, Park GW, Hwang H, Jeong HK, Yun KN, Ji ES, Kim KH, Kim JS, Kim JW, Yun SH, Choi CW, Kim SI, Lim JS, Jeong SK, Paik YK, Lee SY, Park J, Kim SY, Choi YJ, Kim YI, Seo J, Cho JY, Oh MJ, Seo N, An HJ, Kim JY, Yoo JS. Characterization of Site-Specific N-Glycopeptide Isoforms of α-1-Acid Glycoprotein from an Interlaboratory Study Using LC-MS/MS. J Proteome Res 2016; 15:4146-4164. [PMID: 27760464 DOI: 10.1021/acs.jproteome.5b01159] [Citation(s) in RCA: 34] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
Abstract
Glycoprotein conformations are complex and heterogeneous. Currently, site-specific characterization of glycopeptides is a challenge. We sought to establish an efficient method of N-glycoprotein characterization using mass spectrometry (MS). Using alpha-1-acid glycoprotein (AGP) as a model N-glycoprotein, we identified its tryptic N-glycopeptides and examined the data reproducibility in seven laboratories running different LC-MS/MS platforms. We used three test samples and one blind sample to evaluate instrument performance with entire sample preparation workflow. 165 site-specific N-glycopeptides representative of all N-glycosylation sites were identified from AGP 1 and AGP 2 isoforms. The glycopeptide fragmentations by collision-induced dissociation or higher-energy collisional dissociation (HCD) varied based on the MS analyzer. Orbitrap Elite identified the greatest number of AGP N-glycopeptides, followed by Triple TOF and Q-Exactive Plus. Reproducible generation of oxonium ions, glycan-cleaved glycopeptide fragment ions, and peptide backbone fragment ions was essential for successful identification. Laboratory proficiency affected the number of identified N-glycopeptides. The relative quantities of the 10 major N-glycopeptide isoforms of AGP detected in four laboratories were compared to assess reproducibility. Quantitative analysis showed that the coefficient of variation was <25% for all test samples. Our analytical protocol yielded identification and quantification of site-specific N-glycopeptide isoforms of AGP from control and disease plasma sample.
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Affiliation(s)
- Ju Yeon Lee
- Biomedical Omics Group, Korea Basic Science Institute , Ochang 28119, Republic of Korea
| | - Hyun Kyoung Lee
- Biomedical Omics Group, Korea Basic Science Institute , Ochang 28119, Republic of Korea.,Graduate School of Analytical Science and Technology, Chungnam National University , Daejeon 34134, Republic of Korea
| | - Gun Wook Park
- Biomedical Omics Group, Korea Basic Science Institute , Ochang 28119, Republic of Korea.,Graduate School of Analytical Science and Technology, Chungnam National University , Daejeon 34134, Republic of Korea
| | - Heeyoun Hwang
- Biomedical Omics Group, Korea Basic Science Institute , Ochang 28119, Republic of Korea
| | - Hoi Keun Jeong
- Biomedical Omics Group, Korea Basic Science Institute , Ochang 28119, Republic of Korea.,Graduate School of Analytical Science and Technology, Chungnam National University , Daejeon 34134, Republic of Korea
| | - Ki Na Yun
- Biomedical Omics Group, Korea Basic Science Institute , Ochang 28119, Republic of Korea.,Department of Chemistry, Sogang University , Seoul 04107, Republic of Korea
| | - Eun Sun Ji
- Biomedical Omics Group, Korea Basic Science Institute , Ochang 28119, Republic of Korea.,Department of Chemistry, Hannam University , Daejeon 34430, Republic of Korea
| | - Kwang Hoe Kim
- Biomedical Omics Group, Korea Basic Science Institute , Ochang 28119, Republic of Korea.,Graduate School of Analytical Science and Technology, Chungnam National University , Daejeon 34134, Republic of Korea
| | - Jun Seok Kim
- Department of Biomedical Systems Engineering, Korea Polytechnics , Gyeonggi 13590, Republic of Korea
| | - Jong Won Kim
- New Drug Development Center, Osong Medical Innovation Foundation , Cheongju 28160, Republic of Korea
| | - Sung Ho Yun
- Drug & Disease Target Group, Korea Basic Science Institute , Daejeon 34133, Republic of Korea
| | - Chi-Won Choi
- Drug & Disease Target Group, Korea Basic Science Institute , Daejeon 34133, Republic of Korea
| | - Seung Il Kim
- Drug & Disease Target Group, Korea Basic Science Institute , Daejeon 34133, Republic of Korea
| | - Jong-Sun Lim
- Yonsei Proteome Research Center, Yonsei University , Seoul 03722, Republic of Korea
| | - Seul-Ki Jeong
- Yonsei Proteome Research Center, Yonsei University , Seoul 03722, Republic of Korea
| | - Young-Ki Paik
- Yonsei Proteome Research Center, Yonsei University , Seoul 03722, Republic of Korea
| | - Soo-Youn Lee
- Department of Laboratory & Genetics, Samsung Medical Center, Sungkyunkwan University of Medicine , Seoul 06351, Republic of Korea.,Department of Clinical Pharmacology and Therapeutics, Samsung Medical Center , Seoul 06351, Republic of Korea
| | - Jisook Park
- Samsung Biomedical Research Institute, Samsung Medical Center, Sungkyunkwan University School of Medicine , Seoul 06351, Republic of Korea
| | - Su Yeon Kim
- Department of Clinical Research Supporting Team, Clinical Research Institute, Samsung Medical Center , Seoul 06351, Republic of Korea
| | - Young-Jin Choi
- Department of Biochemistry, BK21 PLUS Program for Creative Veterinary Science Research and Research Institute for Veterinary Science, College of Veterinary Medicine, Seoul National University , Seoul 08826, Republic of Korea
| | - Yong-In Kim
- Department of Biochemistry, BK21 PLUS Program for Creative Veterinary Science Research and Research Institute for Veterinary Science, College of Veterinary Medicine, Seoul National University , Seoul 08826, Republic of Korea
| | - Jawon Seo
- Department of Biochemistry, BK21 PLUS Program for Creative Veterinary Science Research and Research Institute for Veterinary Science, College of Veterinary Medicine, Seoul National University , Seoul 08826, Republic of Korea
| | - Je-Yoel Cho
- Department of Biochemistry, BK21 PLUS Program for Creative Veterinary Science Research and Research Institute for Veterinary Science, College of Veterinary Medicine, Seoul National University , Seoul 08826, Republic of Korea
| | - Myoung Jin Oh
- Graduate School of Analytical Science and Technology, Chungnam National University , Daejeon 34134, Republic of Korea
| | - Nari Seo
- Graduate School of Analytical Science and Technology, Chungnam National University , Daejeon 34134, Republic of Korea
| | - Hyun Joo An
- Graduate School of Analytical Science and Technology, Chungnam National University , Daejeon 34134, Republic of Korea
| | - Jin Young Kim
- Biomedical Omics Group, Korea Basic Science Institute , Ochang 28119, Republic of Korea
| | - Jong Shin Yoo
- Biomedical Omics Group, Korea Basic Science Institute , Ochang 28119, Republic of Korea.,Graduate School of Analytical Science and Technology, Chungnam National University , Daejeon 34134, Republic of Korea
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29
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Lee LY, Moh ESX, Parker BL, Bern M, Packer NH, Thaysen-Andersen M. Toward Automated N-Glycopeptide Identification in Glycoproteomics. J Proteome Res 2016; 15:3904-3915. [DOI: 10.1021/acs.jproteome.6b00438] [Citation(s) in RCA: 89] [Impact Index Per Article: 11.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/24/2023]
Affiliation(s)
- Ling Y. Lee
- Department
of Chemistry and Biomolecular Sciences, Macquarie University, Sydney, NSW 2109, Australia
| | - Edward S. X. Moh
- Department
of Chemistry and Biomolecular Sciences, Macquarie University, Sydney, NSW 2109, Australia
| | - Benjamin L. Parker
- Charles
Perkins Centre, School of Molecular Bioscience, The University of Sydney, Sydney, Australia
| | - Marshall Bern
- Protein Metrics
Inc., San Carlos, California 94070, United States
| | - Nicolle H. Packer
- Department
of Chemistry and Biomolecular Sciences, Macquarie University, Sydney, NSW 2109, Australia
| | - Morten Thaysen-Andersen
- Department
of Chemistry and Biomolecular Sciences, Macquarie University, Sydney, NSW 2109, Australia
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30
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Nasir W, Toledo AG, Noborn F, Nilsson J, Wang M, Bandeira N, Larson G. SweetNET: A Bioinformatics Workflow for Glycopeptide MS/MS Spectral Analysis. J Proteome Res 2016; 15:2826-40. [PMID: 27399812 DOI: 10.1021/acs.jproteome.6b00417] [Citation(s) in RCA: 44] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Glycoproteomics has rapidly become an independent analytical platform bridging the fields of glycomics and proteomics to address site-specific protein glycosylation and its impact in biology. Current glycopeptide characterization relies on time-consuming manual interpretations and demands high levels of personal expertise. Efficient data interpretation constitutes one of the major challenges to be overcome before true high-throughput glycopeptide analysis can be achieved. The development of new glyco-related bioinformatics tools is thus of crucial importance to fulfill this goal. Here we present SweetNET: a data-oriented bioinformatics workflow for efficient analysis of hundreds of thousands of glycopeptide MS/MS-spectra. We have analyzed MS data sets from two separate glycopeptide enrichment protocols targeting sialylated glycopeptides and chondroitin sulfate linkage region glycopeptides, respectively. Molecular networking was performed to organize the glycopeptide MS/MS data based on spectral similarities. The combination of spectral clustering, oxonium ion intensity profiles, and precursor ion m/z shift distributions provided typical signatures for the initial assignment of different N-, O- and CS-glycopeptide classes and their respective glycoforms. These signatures were further used to guide database searches leading to the identification and validation of a large number of glycopeptide variants including novel deoxyhexose (fucose) modifications in the linkage region of chondroitin sulfate proteoglycans.
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Affiliation(s)
- Waqas Nasir
- Department of Clinical Chemistry and Transfusion Medicine, Institute of Biomedicine, Sahlgrenska Academy at the University of Gothenburg , SE 413 45 Gothenburg, Sweden
| | - Alejandro Gomez Toledo
- Department of Clinical Chemistry and Transfusion Medicine, Institute of Biomedicine, Sahlgrenska Academy at the University of Gothenburg , SE 413 45 Gothenburg, Sweden
| | - Fredrik Noborn
- Department of Clinical Chemistry and Transfusion Medicine, Institute of Biomedicine, Sahlgrenska Academy at the University of Gothenburg , SE 413 45 Gothenburg, Sweden
| | - Jonas Nilsson
- Department of Clinical Chemistry and Transfusion Medicine, Institute of Biomedicine, Sahlgrenska Academy at the University of Gothenburg , SE 413 45 Gothenburg, Sweden
| | - Mingxun Wang
- Department of Computer Science and Engineering, Center for Computational Mass Spectrometry, CSE, and Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego , La Jolla, California 92093, United States
| | - Nuno Bandeira
- Department of Computer Science and Engineering, Center for Computational Mass Spectrometry, CSE, and Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego , La Jolla, California 92093, United States
| | - Göran Larson
- Department of Clinical Chemistry and Transfusion Medicine, Institute of Biomedicine, Sahlgrenska Academy at the University of Gothenburg , SE 413 45 Gothenburg, Sweden
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31
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Yu CY, Mayampurath A, Zhu R, Zacharias L, Song E, Wang L, Mechref Y, Tang H. Automated Glycan Sequencing from Tandem Mass Spectra of N-Linked Glycopeptides. Anal Chem 2016; 88:5725-32. [PMID: 27111718 PMCID: PMC4899231 DOI: 10.1021/acs.analchem.5b04858] [Citation(s) in RCA: 29] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
Abstract
Mass spectrometry has become a routine experimental tool for proteomic biomarker analysis of human blood samples, partly due to the large availability of informatics tools. As one of the most common protein post-translational modifications (PTMs) in mammals, protein glycosylation has been observed to alter in multiple human diseases and thus may potentially be candidate markers of disease progression. While mass spectrometry instrumentation has seen advancements in capabilities, discovering glycosylation-related markers using existing software is currently not straightforward. Complete characterization of protein glycosylation requires the identification of intact glycopeptides in samples, including identification of the modification site as well as the structure of the attached glycans. In this paper, we present GlycoSeq, an open-source software tool that implements a heuristic iterated glycan sequencing algorithm coupled with prior knowledge for automated elucidation of the glycan structure within a glycopeptide from its collision-induced dissociation tandem mass spectrum. GlycoSeq employs rules of glycosidic linkage as defined by glycan synthetic pathways to eliminate improbable glycan structures and build reasonable glycan trees. We tested the tool on two sets of tandem mass spectra of N-linked glycopeptides cell lines acquired from breast cancer patients. After employing enzymatic specificity within the N-linked glycan synthetic pathway, the sequencing results of GlycoSeq were highly consistent with the manually curated glycan structures. Hence, GlycoSeq is ready to be used for the characterization of glycan structures in glycopeptides from MS/MS analysis. GlycoSeq is released as open source software at https://github.com/chpaul/GlycoSeq/ .
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Affiliation(s)
- Chuan-Yih Yu
- School of Informatics and Computing, Indiana University, Bloomington, IN, USA
| | | | - Rui Zhu
- Department of Chemistry and Biochemistry, Texas Tech University, Lubbock, TX, USA
| | - Lauren Zacharias
- Department of Chemistry and Biochemistry, Texas Tech University, Lubbock, TX, USA
| | - Ehwang Song
- Department of Chemistry and Biochemistry, Texas Tech University, Lubbock, TX, USA
| | - Lei Wang
- School of Informatics and Computing, Indiana University, Bloomington, IN, USA
| | - Yehia Mechref
- Department of Chemistry and Biochemistry, Texas Tech University, Lubbock, TX, USA
| | - Haixu Tang
- School of Informatics and Computing, Indiana University, Bloomington, IN, USA
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32
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Khatri K, Klein JA, White MR, Grant OC, Leymarie N, Woods RJ, Hartshorn KL, Zaia J. Integrated Omics and Computational Glycobiology Reveal Structural Basis for Influenza A Virus Glycan Microheterogeneity and Host Interactions. Mol Cell Proteomics 2016; 15:1895-912. [PMID: 26984886 PMCID: PMC5083086 DOI: 10.1074/mcp.m116.058016] [Citation(s) in RCA: 79] [Impact Index Per Article: 9.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2016] [Revised: 03/04/2016] [Indexed: 02/04/2023] Open
Abstract
Despite sustained biomedical research effort, influenza A virus remains an imminent threat to the world population and a major healthcare burden. The challenge in developing vaccines against influenza is the ability of the virus to mutate rapidly in response to selective immune pressure. Hemagglutinin is the predominant surface glycoprotein and the primary determinant of antigenicity, virulence and zoonotic potential. Mutations leading to changes in the number of HA glycosylation sites are often reported. Such genetic sequencing studies predict at best the disruption or creation of sequons for N-linked glycosylation; they do not reflect actual phenotypic changes in HA structure. Therefore, combined analysis of glycan micro and macro-heterogeneity and bioassays will better define the relationships among glycosylation, viral bioactivity and evolution. We present a study that integrates proteomics, glycomics and glycoproteomics of HA before and after adaptation to innate immune system pressure. We combined this information with glycan array and immune lectin binding data to correlate the phenotypic changes with biological activity. Underprocessed glycoforms predominated at the glycosylation sites found to be involved in viral evolution in response to selection pressures and interactions with innate immune-lectins. To understand the structural basis for site-specific glycan microheterogeneity at these sites, we performed structural modeling and molecular dynamics simulations. We observed that the presence of immature, high-mannose type glycans at a particular site correlated with reduced accessibility to glycan remodeling enzymes. Further, the high mannose glycans at sites implicated in immune lectin recognition were predicted to be capable of forming trimeric interactions with the immune-lectin surfactant protein-D.
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Affiliation(s)
- Kshitij Khatri
- From the ‡Center for Biomedical Mass Spectrometry, Department of Biochemistry, Boston University School of Medicine, Boston, Massachusetts 02118
| | - Joshua A Klein
- From the ‡Center for Biomedical Mass Spectrometry, Department of Biochemistry, Boston University School of Medicine, Boston, Massachusetts 02118; §Bioinformatics Program, Boston University, Boston, Massachusetts 02215
| | - Mitchell R White
- ¶Department of Medicine, Boston University School of Medicine, Boston, Massachusetts 02118
| | - Oliver C Grant
- ‖Complex Carbohydrate Research Center, University of Georgia, Athens, Georgia 30602
| | - Nancy Leymarie
- From the ‡Center for Biomedical Mass Spectrometry, Department of Biochemistry, Boston University School of Medicine, Boston, Massachusetts 02118
| | - Robert J Woods
- ‖Complex Carbohydrate Research Center, University of Georgia, Athens, Georgia 30602
| | - Kevan L Hartshorn
- ¶Department of Medicine, Boston University School of Medicine, Boston, Massachusetts 02118
| | - Joseph Zaia
- From the ‡Center for Biomedical Mass Spectrometry, Department of Biochemistry, Boston University School of Medicine, Boston, Massachusetts 02118; §Bioinformatics Program, Boston University, Boston, Massachusetts 02215;
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33
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Chandler KB, Costello CE. Glycomics and glycoproteomics of membrane proteins and cell-surface receptors: Present trends and future opportunities. Electrophoresis 2016; 37:1407-19. [PMID: 26872045 PMCID: PMC4889498 DOI: 10.1002/elps.201500552] [Citation(s) in RCA: 54] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2015] [Revised: 02/02/2016] [Accepted: 02/02/2016] [Indexed: 12/16/2022]
Abstract
Membrane proteins mediate cell-cell interactions and adhesion, the transfer of ions and metabolites, and the transmission of signals from the extracellular environment to the cell interior. The extracellular domains of most cell membrane proteins are glycosylated, often at multiple sites. There is a growing awareness that glycosylation impacts the structure, interaction, and function of membrane proteins. The application of glycoproteomics and glycomics methods to membrane proteins has great potential. However, challenges also arise from the unique physical properties of membrane proteins. Successful analytical workflows must be developed and disseminated to advance functional glycoproteomics and glycomics studies of membrane proteins. This review explores the opportunities and challenges related to glycomic and glycoproteomic analysis of membrane proteins, including discussion of sample preparation, enrichment, and MS/MS analyses, with a focus on recent successful workflows for analysis of N- and O-linked glycosylation of mammalian membrane proteins.
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Affiliation(s)
- Kevin Brown Chandler
- Center for Biomedical Mass Spectrometry, Department of Biochemistry, Boston University School of Medicine, Boston, MA, USA
| | - Catherine E Costello
- Center for Biomedical Mass Spectrometry, Department of Biochemistry, Boston University School of Medicine, Boston, MA, USA
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34
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Thaysen-Andersen M, Packer NH, Schulz BL. Maturing Glycoproteomics Technologies Provide Unique Structural Insights into the N-glycoproteome and Its Regulation in Health and Disease. Mol Cell Proteomics 2016; 15:1773-90. [PMID: 26929216 PMCID: PMC5083109 DOI: 10.1074/mcp.o115.057638] [Citation(s) in RCA: 137] [Impact Index Per Article: 17.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2015] [Revised: 02/09/2016] [Indexed: 12/21/2022] Open
Abstract
The glycoproteome remains severely understudied because of significant analytical challenges associated with glycoproteomics, the system-wide analysis of intact glycopeptides. This review introduces important structural aspects of protein N-glycosylation and summarizes the latest technological developments and applications in LC-MS/MS-based qualitative and quantitative N-glycoproteomics. These maturing technologies provide unique structural insights into the N-glycoproteome and its synthesis and regulation by complementing existing methods in glycoscience. Modern glycoproteomics is now sufficiently mature to initiate efforts to capture the molecular complexity displayed by the N-glycoproteome, opening exciting opportunities to increase our understanding of the functional roles of protein N-glycosylation in human health and disease.
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Affiliation(s)
- Morten Thaysen-Andersen
- From the ‡Department of Chemistry and Biomolecular Sciences, Macquarie University, Sydney, NSW, Australia;
| | - Nicolle H Packer
- From the ‡Department of Chemistry and Biomolecular Sciences, Macquarie University, Sydney, NSW, Australia
| | - Benjamin L Schulz
- §School of Chemistry & Molecular Biosciences, St Lucia, The University of Queensland, Brisbane, QLD, Australia
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35
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Lih TM, Choong WK, Chen CC, Cheng CW, Lin HN, Chen CT, Chang HY, Hsu WL, Sung TY. MAGIC-web: a platform for untargeted and targeted N-linked glycoprotein identification. Nucleic Acids Res 2016; 44:W575-80. [PMID: 27084943 PMCID: PMC4987873 DOI: 10.1093/nar/gkw254] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2016] [Accepted: 04/02/2016] [Indexed: 01/25/2023] Open
Abstract
MAGIC-web is the first web server, to the best of our knowledge, that performs both untargeted and targeted analyses of mass spectrometry-based glycoproteomics data for site-specific N-linked glycoprotein identification. The first two modules, MAGIC and MAGIC+, are designed for untargeted and targeted analysis, respectively. MAGIC is implemented with our previously proposed novel Y1-ion pattern matching method, which adequately detects Y1- and Y0-ion without prior information of proteins and glycans, and then generates in silico MS2 spectra that serve as input to a database search engine (e.g. Mascot) to search against a large-scale protein sequence database. On top of that, the newly implemented MAGIC+ allows users to determine glycopeptide sequences using their own protein sequence file. The third module, Reports Integrator, provides the service of combining protein identification results from Mascot and glycan-related information from MAGIC-web to generate a complete site-specific protein-glycan summary report. The last module, Glycan Search, is designed for the users who are interested in finding possible glycan structures with specific numbers and types of monosaccharides. The results from MAGIC, MAGIC+ and Reports Integrator can be downloaded via provided links whereas the annotated spectra and glycan structures can be visualized in the browser. MAGIC-web is accessible from http://ms.iis.sinica.edu.tw/MAGIC-web/index.html.
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Affiliation(s)
- T Mamie Lih
- Bioinformatics Program, Taiwan International Graduate Program, Academia Sinica, Taipei 11529, Taiwan Institute of Information Science, Academia Sinica, Taipei 11529, Taiwan Institute of Biomedical Informatics, National Yang-Ming University, Taipei 11221, Taiwan
| | - Wai-Kok Choong
- Institute of Information Science, Academia Sinica, Taipei 11529, Taiwan
| | - Chen-Chun Chen
- Genomics Research Center, Academia Sinica, Taipei 11529, Taiwan Department of Chemistry, National Taiwan University, Taipei 10617, Taiwan
| | - Cheng-Wei Cheng
- Bioinformatics Program, Taiwan International Graduate Program, Academia Sinica, Taipei 11529, Taiwan Institute of Information Science, Academia Sinica, Taipei 11529, Taiwan Institute of Biomedical Informatics, National Yang-Ming University, Taipei 11221, Taiwan
| | - Hsin-Nan Lin
- Institute of Information Science, Academia Sinica, Taipei 11529, Taiwan
| | - Ching-Tai Chen
- Institute of Information Science, Academia Sinica, Taipei 11529, Taiwan
| | - Hui-Yin Chang
- Bioinformatics Program, Taiwan International Graduate Program, Academia Sinica, Taipei 11529, Taiwan Institute of Information Science, Academia Sinica, Taipei 11529, Taiwan Institute of Biomedical Informatics, National Yang-Ming University, Taipei 11221, Taiwan
| | - Wen-Lian Hsu
- Institute of Information Science, Academia Sinica, Taipei 11529, Taiwan
| | - Ting-Yi Sung
- Institute of Information Science, Academia Sinica, Taipei 11529, Taiwan
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36
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A review of methods for interpretation of glycopeptide tandem mass spectral data. Glycoconj J 2015; 33:285-96. [PMID: 26612686 DOI: 10.1007/s10719-015-9633-3] [Citation(s) in RCA: 62] [Impact Index Per Article: 6.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2015] [Revised: 10/13/2015] [Accepted: 10/21/2015] [Indexed: 12/25/2022]
Abstract
Despite the publication of several software tools for analysis of glycopeptide tandem mass spectra, there remains a lack of consensus regarding the most effective and appropriate methods. In part, this reflects problems with applying standard methods for proteomics database searching and false discovery rate calculation. While the analysis of small post-translational modifications (PTMs) may be regarded as an extension of proteomics database searching, glycosylation requires specialized approaches. This is because glycans are large and heterogeneous by nature, causing glycopeptides to exist as multiple glycosylated variants. Thus, the mass of the peptide cannot be calculated directly from that of the intact glycopeptide. In addition, the chemical nature of the glycan strongly influences product ion patterns observed for glycopeptides. As a result, glycopeptidomics requires specialized bioinformatics methods. We summarize the recent progress towards a consensus for effective glycopeptide tandem mass spectrometric analysis.
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37
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Liu G, Neelamegham S. Integration of systems glycobiology with bioinformatics toolboxes, glycoinformatics resources, and glycoproteomics data. WILEY INTERDISCIPLINARY REVIEWS-SYSTEMS BIOLOGY AND MEDICINE 2015; 7:163-81. [PMID: 25871730 DOI: 10.1002/wsbm.1296] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/24/2014] [Revised: 02/08/2015] [Accepted: 03/04/2015] [Indexed: 12/22/2022]
Abstract
The glycome constitutes the entire complement of free carbohydrates and glycoconjugates expressed on whole cells or tissues. 'Systems Glycobiology' is an emerging discipline that aims to quantitatively describe and analyse the glycome. Here, instead of developing a detailed understanding of single biochemical processes, a combination of computational and experimental tools are used to seek an integrated or 'systems-level' view. This can explain how multiple biochemical reactions and transport processes interact with each other to control glycome biosynthesis and function. Computational methods in this field commonly build in silico reaction network models to describe experimental data derived from structural studies that measure cell-surface glycan distribution. While considerable progress has been made, several challenges remain due to the complex and heterogeneous nature of this post-translational modification. First, for the in silico models to be standardized and shared among laboratories, it is necessary to integrate glycan structure information and glycosylation-related enzyme definitions into the mathematical models. Second, as glycoinformatics resources grow, it would be attractive to utilize 'Big Data' stored in these repositories for model construction and validation. Third, while the technology for profiling the glycome at the whole-cell level has been standardized, there is a need to integrate mass spectrometry derived site-specific glycosylation data into the models. The current review discusses progress that is being made to resolve the above bottlenecks. The focus is on how computational models can bridge the gap between 'data' generated in wet-laboratory studies with 'knowledge' that can enhance our understanding of the glycome.
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Affiliation(s)
- Gang Liu
- Department of Chemical and Biological Engineering, State University of New York, Buffalo, NY, USA
| | - Sriram Neelamegham
- Department of Chemical and Biological Engineering, State University of New York, Buffalo, NY, USA
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