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Mariethoz J, Alocci D, Karlsson NG, Packer NH, Lisacek F. An Interactive View of Glycosylation. Methods Mol Biol 2022; 2370:41-65. [PMID: 34611864 DOI: 10.1007/978-1-0716-1685-7_3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
The present chapter focuses on the interactive and explorative aspects of bioinformatics resources that have been recently released in glycobiology. The comparative analysis of data in a field where knowledge is scattered, incomplete, and disconnected from main biology requires efficient visualization, integration, and interactive tools that are currently only partially implemented. This overview highlights converging efforts toward building a consistent picture of protein glycosylation.
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Affiliation(s)
- Julien Mariethoz
- Proteome Informatics Group, SIB Swiss Institute of Bioinformatics, Geneva, Switzerland
- Computer Science Department, University of Geneva, Geneva, Switzerland
| | - Davide Alocci
- Proteome Informatics Group, SIB Swiss Institute of Bioinformatics, Geneva, Switzerland
| | - Niclas G Karlsson
- Department of Medical Biochemistry and Cell Biology, Institute of Biomedicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Nicolle H Packer
- Department of Molecular Sciences and ARC Centre of Excellence for Nanoscale Biophotonics, Macquarie University, Sydney, NSW, Australia
- Institute for Glycomics, Griffith University, Gold Coast, QLD, Australia
| | - Frédérique Lisacek
- Proteome Informatics Group, SIB Swiss Institute of Bioinformatics, University of Geneva, Geneva, Switzerland.
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2
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Ma B, Guan X, Li Y, Shang S, Li J, Tan Z. Protein Glycoengineering: An Approach for Improving Protein Properties. Front Chem 2020; 8:622. [PMID: 32793559 PMCID: PMC7390894 DOI: 10.3389/fchem.2020.00622] [Citation(s) in RCA: 45] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2020] [Accepted: 06/15/2020] [Indexed: 12/12/2022] Open
Abstract
Natural proteins are an important source of therapeutic agents and industrial enzymes. While many of them have the potential to be used as highly effective medical treatments for a wide range of diseases or as catalysts for conversion of a range of molecules into important product types required by modern society, problems associated with poor biophysical and biological properties have limited their applications. Engineering proteins with reduced side-effects and/or improved biophysical and biological properties is therefore of great importance. As a common protein modification, glycosylation has the capacity to greatly influence these properties. Over the past three decades, research from many disciplines has established the importance of glycoengineering in overcoming the limitations of proteins. In this review, we will summarize the methods that have been used to glycoengineer proteins and briefly discuss some representative examples of these methods, with the goal of providing a general overview of this research area.
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Affiliation(s)
- Bo Ma
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Xiaoyang Guan
- Department of Chemistry and Biochemistry and BioFrontiers Institute, University of Colorado, Boulder, CO, United States
| | - Yaohao Li
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.,Department of Chemistry and Biochemistry and BioFrontiers Institute, University of Colorado, Boulder, CO, United States
| | - Shiying Shang
- School of Pharmaceutical Sciences, Tsinghua University, Beijing, China
| | - Jing Li
- Beijing Key Laboratory of DNA Damage Response and College of Life Sciences, Capital Normal University, Beijing, China
| | - Zhongping Tan
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
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3
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Bonnardel F, Mariethoz J, Salentin S, Robin X, Schroeder M, Perez S, Lisacek F, Imberty A. UniLectin3D, a database of carbohydrate binding proteins with curated information on 3D structures and interacting ligands. Nucleic Acids Res 2020; 47:D1236-D1244. [PMID: 30239928 PMCID: PMC6323968 DOI: 10.1093/nar/gky832] [Citation(s) in RCA: 66] [Impact Index Per Article: 16.5] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2018] [Accepted: 09/07/2018] [Indexed: 01/02/2023] Open
Abstract
Lectins, and related receptors such as adhesins and toxins, are glycan-binding proteins from all origins that decipher the glycocode, i.e. the structural information encoded in the conformation of complex carbohydrates present on the surface of all cells. Lectins are still poorly classified and annotated, but since their functions are based on ligand recognition, their 3D-structures provide a solid foundation for characterization. UniLectin3D is a curated database that classifies lectins on origin and fold, with cross-links to literature, other databases in glycosciences and functional data such as known specificity. The database provides detailed information on lectins, their bound glycan ligands, and features their interactions using the Protein–Ligand Interaction Profiler (PLIP) server. Special care was devoted to the description of the bound glycan ligands with the use of simple graphical representation and numerical format for cross-linking to other databases in glycoscience. We conceived the design of the database architecture and the navigation tools to account for all organisms, as well as to search for oligosaccharide epitopes complexed within specified binding sites. UniLectin3D is accessible at https://www.unilectin.eu/unilectin3D.
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Affiliation(s)
- François Bonnardel
- Univ. Grenoble Alpes, CNRS, CERMAV, 38000 Grenoble, France.,Proteome Informatics Group, SIB Swiss Institute of Bioinformatics, CH-1227 Geneva, Switzerland.,Department of Computer Science, University of Geneva, Route de Drize 7, CH-1227 Geneva, Switzerland
| | - Julien Mariethoz
- Proteome Informatics Group, SIB Swiss Institute of Bioinformatics, CH-1227 Geneva, Switzerland.,Department of Computer Science, University of Geneva, Route de Drize 7, CH-1227 Geneva, Switzerland
| | - Sebastian Salentin
- Biotechnology Center (BIOTEC), TU Dresden, Tatzberg 47-49, 01307 Dresden, Germany
| | - Xavier Robin
- Biozentrum, University of Basel, Klingelbergstrasse 50-70, CH-4056 Basel, Switzerland.,Computational Structural Biology Group, SIB Swiss Institute of Bioinformatics, CH-4056 Basel, Switzerland
| | - Michael Schroeder
- Biotechnology Center (BIOTEC), TU Dresden, Tatzberg 47-49, 01307 Dresden, Germany
| | - Serge Perez
- Univ. Grenoble Alpes, CNRS, DPM, 38000 Grenoble, France
| | - Frédérique Lisacek
- Proteome Informatics Group, SIB Swiss Institute of Bioinformatics, CH-1227 Geneva, Switzerland.,Department of Computer Science, University of Geneva, Route de Drize 7, CH-1227 Geneva, Switzerland.,Section of Biology, University of Geneva, CH-1205 Geneva, Switzerland
| | - Anne Imberty
- Univ. Grenoble Alpes, CNRS, CERMAV, 38000 Grenoble, France
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4
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Böhm M, Bohne-Lang A, Frank M, Loss A, Rojas-Macias MA, Lütteke T. Glycosciences.DB: an annotated data collection linking glycomics and proteomics data (2018 update). Nucleic Acids Res 2020; 47:D1195-D1201. [PMID: 30357361 PMCID: PMC6323918 DOI: 10.1093/nar/gky994] [Citation(s) in RCA: 54] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2018] [Accepted: 10/12/2018] [Indexed: 12/21/2022] Open
Abstract
Glycosciences.DB, the glycan structure database of the Glycosciences.de portal, collects various kinds of data on glycan structures, including carbohydrate moieties from worldwide Protein Data Bank (wwPDB) structures. This way it forms a bridge between glycomics and proteomics resources. A major update of this database combines a redesigned web interface with a series of new functions. These include separate entry pages not only for glycan structures but also for literature references and wwPDB entries, improved substructure search options, a newly available keyword search covering all types of entries in one query, and new types of information that is added to glycan structures. These new features are described in detail in this article, and options how users can provide information to the database are discussed as well. Glycosciences.DB is available at http://www.glycosciences.de/database/ and can be freely accessed.
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Affiliation(s)
- Michael Böhm
- Institute of Veterinary Physiology and Biochemistry, Justus-Liebig University Giessen, Frankfurter Str. 100, 35392 Giessen, Germany
| | - Andreas Bohne-Lang
- Medical Faculty Mannheim, University Heidelberg, Ludolf-Krehl-Str. 13-17, 68167 Mannheim, Germany
| | - Martin Frank
- Biognos AB, Generatorsgatan 1, Box 8963, 40274 Göteborg, Sweden
| | - Alexander Loss
- Gebrüder Gerstenberg GmbH & Co. KG, EDV, Rathausstraße 18-20, 31134 Hildesheim, Germany
| | - Miguel A Rojas-Macias
- Institute of Veterinary Physiology and Biochemistry, Justus-Liebig University Giessen, Frankfurter Str. 100, 35392 Giessen, Germany
| | - Thomas Lütteke
- Institute of Veterinary Physiology and Biochemistry, Justus-Liebig University Giessen, Frankfurter Str. 100, 35392 Giessen, Germany
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5
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Vos RA, Katayama T, Mishima H, Kawano S, Kawashima S, Kim JD, Moriya Y, Tokimatsu T, Yamaguchi A, Yamamoto Y, Wu H, Amstutz P, Antezana E, Aoki NP, Arakawa K, Bolleman JT, Bolton E, Bonnal RJP, Bono H, Burger K, Chiba H, Cohen KB, Deutsch EW, Fernández-Breis JT, Fu G, Fujisawa T, Fukushima A, García A, Goto N, Groza T, Hercus C, Hoehndorf R, Itaya K, Juty N, Kawashima T, Kim JH, Kinjo AR, Kotera M, Kozaki K, Kumagai S, Kushida T, Lütteke T, Matsubara M, Miyamoto J, Mohsen A, Mori H, Naito Y, Nakazato T, Nguyen-Xuan J, Nishida K, Nishida N, Nishide H, Ogishima S, Ohta T, Okuda S, Paten B, Perret JL, Prathipati P, Prins P, Queralt-Rosinach N, Shinmachi D, Suzuki S, Tabata T, Takatsuki T, Taylor K, Thompson M, Uchiyama I, Vieira B, Wei CH, Wilkinson M, Yamada I, Yamanaka R, Yoshitake K, Yoshizawa AC, Dumontier M, Kosaki K, Takagi T. BioHackathon 2015: Semantics of data for life sciences and reproducible research. F1000Res 2020; 9:136. [PMID: 32308977 PMCID: PMC7141167 DOI: 10.12688/f1000research.18236.1] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 02/05/2020] [Indexed: 01/08/2023] Open
Abstract
We report on the activities of the 2015 edition of the BioHackathon, an annual event that brings together researchers and developers from around the world to develop tools and technologies that promote the reusability of biological data. We discuss issues surrounding the representation, publication, integration, mining and reuse of biological data and metadata across a wide range of biomedical data types of relevance for the life sciences, including chemistry, genotypes and phenotypes, orthology and phylogeny, proteomics, genomics, glycomics, and metabolomics. We describe our progress to address ongoing challenges to the reusability and reproducibility of research results, and identify outstanding issues that continue to impede the progress of bioinformatics research. We share our perspective on the state of the art, continued challenges, and goals for future research and development for the life sciences Semantic Web.
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Affiliation(s)
- Rutger A. Vos
- Institute of Biology Leiden, Leiden University, Leiden, The Netherlands
- Naturalis Biodiversity Center, Leiden, The Netherlands
| | | | - Hiroyuki Mishima
- Department of Human Genetics, Nagasaki University Graduate School of Biomedical Sciences, Nagasaki, Japan
| | - Shin Kawano
- Database Center for Life Science, Tokyo, Japan
| | | | | | - Yuki Moriya
- Database Center for Life Science, Tokyo, Japan
| | | | | | | | - Hongyan Wu
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | | | - Erick Antezana
- Department of Biology, Norwegian University of Science and Technology, Trondheim, Norway
| | - Nobuyuki P. Aoki
- Faculty of Science and Engineering, SOKA University, Tokyo, Japan
| | - Kazuharu Arakawa
- Institute for Advanced Biosciences, Keio University, Tokyo, Japan
| | - Jerven T. Bolleman
- SIB Swiss Institute of Bioinformatics, Centre Medical Universitaire, Lausanne, Switzerland
| | - Evan Bolton
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, USA
| | - Raoul J. P. Bonnal
- Istituto Nazionale Genetica Molecolare, Romeo ed Enrica Invernizzi, Milan, Italy
| | | | - Kees Burger
- Dutch Techcentre for Life Sciences, Utrecht, The Netherlands
| | - Hirokazu Chiba
- National Institute for Basic Biology, National Institutes of Natural Sciences, Okazaki, Japan
| | - Kevin B. Cohen
- Computational Bioscience Program, University of Colorado School of Medicine, Denver, USA
- Université Paris-Saclay, LIMSI, CNRS, Paris, France
| | | | | | - Gang Fu
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, USA
| | | | | | | | - Naohisa Goto
- Research Institute for Microbial Diseases, Osaka University, Osaka, Japan
| | - Tudor Groza
- St Vincent's Clinical School, Faculty of Medicine, University of New South Wales, Darlinghurst, Australia
- Kinghorn Centre for Clinical Genomics, Garvan Institute of Medical Research, Darlinghurst, Australia
| | - Colin Hercus
- Novocraft Technologies Sdn. Bhd., Selangor, Malaysia
| | - Robert Hoehndorf
- Computational Bioscience Research Center, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia
| | - Kotone Itaya
- Institute for Advanced Biosciences, Keio University, Tokyo, Japan
| | - Nick Juty
- European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, UK
| | | | - Jee-Hyub Kim
- European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, UK
| | - Akira R. Kinjo
- Institute for Protein Research, Osaka University, Osaka, Japan
| | - Masaaki Kotera
- School of Life Science and Technology, Tokyo Institute of Technology, Tokyo, Japan
| | - Kouji Kozaki
- The Institute of Scientific and Industrial Research, Osaka University, Osaka, Japan
| | | | - Tatsuya Kushida
- National Bioscience Database Center, Japan Science and Technology Agency, Tokyo, Japan
| | - Thomas Lütteke
- Institute of Veterinary Physiology and Biochemistry, Justus-Liebig University Giessen, Giessen, Germany
- Gesellschaft für innovative Personalwirtschaftssysteme mbH (GIP GmbH), Offenbach, Germany
| | | | | | - Attayeb Mohsen
- National Institutes of Biomedical Innovation, Health and Nutrition, Osaka, Japan
| | - Hiroshi Mori
- Center for Information Biology, National Institute of Genetics, Mishima, Japan
| | - Yuki Naito
- Database Center for Life Science, Tokyo, Japan
| | | | | | | | - Naoki Nishida
- Department of Systems Science, Osaka University, Osaka, Japan
| | - Hiroyo Nishide
- National Institute for Basic Biology, National Institutes of Natural Sciences, Okazaki, Japan
| | - Soichi Ogishima
- Tohoku Medical Megabank Organization, Tohoku University, Sendai, Japan
| | - Tazro Ohta
- Database Center for Life Science, Tokyo, Japan
| | - Shujiro Okuda
- Niigata University Graduate School of Medical and Dental Sciences, Niigata, Japan
| | - Benedict Paten
- UC Santa Cruz Genomics Institute, University of California, Santa Cruz, USA
| | | | - Philip Prathipati
- National Institutes of Biomedical Innovation, Health and Nutrition, Osaka, Japan
| | - Pjotr Prins
- University Medical Center Utrecht, Utrecht, The Netherlands
- University of Tennessee Health Science Center, Memphis, USA
| | - Núria Queralt-Rosinach
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA
| | | | - Shinya Suzuki
- School of Life Science and Technology, Tokyo Institute of Technology, Tokyo, Japan
| | - Tsuyosi Tabata
- Graduate School of Pharmaceutical Sciences, Kyoto University, Kyoto, Japan
| | | | - Kieron Taylor
- European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, UK
| | - Mark Thompson
- Leiden University Medical Center, Leiden, The Netherlands
| | - Ikuo Uchiyama
- National Institute for Basic Biology, National Institutes of Natural Sciences, Okazaki, Japan
| | - Bruno Vieira
- WurmLab, School of Biological & Chemical Sciences, Queen Mary University of London, London, UK
| | - Chih-Hsuan Wei
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, USA
| | - Mark Wilkinson
- Escuela Técnica Superior de Ingeniería Agronómica, Alimentaria y de Biosistemas, Universidad Politécnica de Madrid, Madrid, Spain
| | | | | | - Kazutoshi Yoshitake
- Graduate School of Agricultural and Life Sciences, The University of Tokyo, Tokyo, Japan
| | | | - Michel Dumontier
- Institute of Data Science, Maastricht University, Maastricht, The Netherlands
| | - Kenjiro Kosaki
- Center for Medical Genetics, Keio University School of Medicine, Tokyo, Japan
| | - Toshihisa Takagi
- National Bioscience Database Center, Japan Science and Technology Agency, Tokyo, Japan
- Department of Biological Sciences, Graduate School of Science, The University of Tokyo, Tokyo, Japan
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6
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Abrahams JL, Taherzadeh G, Jarvas G, Guttman A, Zhou Y, Campbell MP. Recent advances in glycoinformatic platforms for glycomics and glycoproteomics. Curr Opin Struct Biol 2019; 62:56-69. [PMID: 31874386 DOI: 10.1016/j.sbi.2019.11.009] [Citation(s) in RCA: 65] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2019] [Revised: 11/05/2019] [Accepted: 11/15/2019] [Indexed: 12/16/2022]
Abstract
Protein glycosylation is the most complex and prevalent post-translation modification in terms of the number of proteins modified and the diversity generated. To understand the functional roles of glycoproteins it is important to gain an insight into the repertoire of oligosaccharides present. The comparison and relative quantitation of glycoforms combined with site-specific identification and occupancy are necessary steps in this direction. Computational platforms have continued to mature assisting researchers with the interpretation of such glycomics and glycoproteomics data sets, but frequently support dedicated workflows and users rely on the manual interpretation of data to gain insights into the glycoproteome. The growth of site-specific knowledge has also led to the implementation of machine-learning algorithms to predict glycosylation which is now being integrated into glycoproteomics pipelines. This short review describes commercial and open-access databases and software with an emphasis on those that are actively maintained and designed to support current analytical workflows.
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Affiliation(s)
- Jodie L Abrahams
- Institute for Glycomics, Griffith University, Gold Coast, QLD, Australia
| | - Ghazaleh Taherzadeh
- School of Information and Communication Technology, Griffith University, Gold Coast, QLD, Australia
| | - Gabor Jarvas
- Translational Glycomics Research Group, Research Institute of Biomolecular and Chemical Engineering, University of Pannonia, Veszprém, Hungary; Horváth Csaba Laboratory of Bioseparation Sciences, Research Centre for Molecular Medicine, Faculty of Medicine, University of Debrecen, Debrecen, Hungary
| | - Andras Guttman
- Translational Glycomics Research Group, Research Institute of Biomolecular and Chemical Engineering, University of Pannonia, Veszprém, Hungary; Horváth Csaba Laboratory of Bioseparation Sciences, Research Centre for Molecular Medicine, Faculty of Medicine, University of Debrecen, Debrecen, Hungary; SCIEX, Brea, CA, USA
| | - Yaoqi Zhou
- School of Information and Communication Technology, Griffith University, Gold Coast, QLD, Australia
| | - Matthew P Campbell
- Institute for Glycomics, Griffith University, Gold Coast, QLD, Australia.
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7
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Toukach PV, Egorova KS. New Features of Carbohydrate Structure Database Notation (CSDB Linear), As Compared to Other Carbohydrate Notations. J Chem Inf Model 2019; 60:1276-1289. [DOI: 10.1021/acs.jcim.9b00744] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Philip V. Toukach
- N.D. Zelinsky Institute of Organic Chemistry, Russian Academy of Sciences, Leninsky prosect 47, Moscow, Russia 119991
- National Research University Higher School of Economics, Myasnitskaya 20, Moscow, Russia 101000
| | - Ksenia S. Egorova
- N.D. Zelinsky Institute of Organic Chemistry, Russian Academy of Sciences, Leninsky prosect 47, Moscow, Russia 119991
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8
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Duivelshof BL, Jiskoot W, Beck A, Veuthey JL, Guillarme D, D’Atri V. Glycosylation of biosimilars: Recent advances in analytical characterization and clinical implications. Anal Chim Acta 2019; 1089:1-18. [DOI: 10.1016/j.aca.2019.08.044] [Citation(s) in RCA: 30] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2019] [Revised: 08/15/2019] [Accepted: 08/17/2019] [Indexed: 12/14/2022]
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9
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Abstract
Glycoinformatics is a critical resource for the study of glycobiology, and glycobiology is a necessary component for understanding the complex interface between intra- and extracellular spaces. Despite this, there is limited software available to scientists studying these topics, requiring each to create fundamental data structures and representations anew for each of their applications. This leads to poor uptake of standardization and loss of focus on the real problems. We present glypy, a library written in Python for reading, writing, manipulating, and transforming glycans at several levels of precision. In addition to understanding several common formats for textual representation of glycans, the library also provides application programming interfaces (APIs) for major community databases, including GlyTouCan and UnicarbKB. The library is freely available under the Apache 2 common license with source code available at https://github.com/mobiusklein/ and documentation at https://glypy.readthedocs.io/ .
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Affiliation(s)
- Joshua Klein
- Program for Bioinformatics , Boston University , Boston , Massachusetts 02215 , United States
| | - Joseph Zaia
- Program for Bioinformatics , Boston University , Boston , Massachusetts 02215 , United States.,Department of Biochemistry , Boston University , Boston , Massachusetts 02215 , United States
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10
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A Bioinformatics View of Glycan⁻Virus Interactions. Viruses 2019; 11:v11040374. [PMID: 31018588 PMCID: PMC6521074 DOI: 10.3390/v11040374] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2019] [Revised: 04/05/2019] [Accepted: 04/15/2019] [Indexed: 02/06/2023] Open
Abstract
Evidence of the mediation of glycan molecules in the interaction between viruses and their hosts is accumulating and is now partially reflected in several online databases. Bioinformatics provides convenient and efficient means of searching, visualizing, comparing, and sometimes predicting, interactions in numerous and diverse molecular biology applications related to the -omics fields. As viromics is gaining momentum, bioinformatics support is increasingly needed. We propose a survey of the current resources for searching, visualizing, comparing, and possibly predicting host–virus interactions that integrate the presence and role of glycans. To the best of our knowledge, we have mapped the specialized and general-purpose databases with the appropriate focus. With an illustration of their potential usage, we also discuss the strong and weak points of the current bioinformatics landscape in the context of understanding viral infection and the immune response to it.
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11
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Alocci D, Mariethoz J, Gastaldello A, Gasteiger E, Karlsson NG, Kolarich D, Packer NH, Lisacek F. GlyConnect: Glycoproteomics Goes Visual, Interactive, and Analytical. J Proteome Res 2018; 18:664-677. [DOI: 10.1021/acs.jproteome.8b00766] [Citation(s) in RCA: 62] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Affiliation(s)
- Davide Alocci
- Proteome Informatics Group, SIB Swiss Institute of Bioinformatics, Rue Michel-Servet 1, CH-1211 Geneva, Switzerland
- Computer Science Department, University of Geneva, CH-1227 Geneva, Switzerland
| | - Julien Mariethoz
- Proteome Informatics Group, SIB Swiss Institute of Bioinformatics, Rue Michel-Servet 1, CH-1211 Geneva, Switzerland
- Computer Science Department, University of Geneva, CH-1227 Geneva, Switzerland
| | - Alessandra Gastaldello
- Proteome Informatics Group, SIB Swiss Institute of Bioinformatics, Rue Michel-Servet 1, CH-1211 Geneva, Switzerland
- Computer Science Department, University of Geneva, CH-1227 Geneva, Switzerland
| | - Elisabeth Gasteiger
- Swiss-Prot Group, SIB Swiss Institute of Bioinformatics, CH-1211 Geneva, Switzerland
| | - Niclas G. Karlsson
- Department of Medical Biochemistry and Cell Biology, Institute of Biomedicine, University of Gothenburg, 40530 Gothenburg, Sweden
| | - Daniel Kolarich
- Institute for Glycomics, Griffith University, Southport, Queensland 4215, Australia
- ARC Centre for Nanoscale BioPhotonics, Macquarie University and Griffith University, Sydney, New South Wales 2109, Australia
| | - Nicolle H. Packer
- Institute for Glycomics, Griffith University, Southport, Queensland 4215, Australia
- ARC Centre for Nanoscale BioPhotonics, Macquarie University and Griffith University, Sydney, New South Wales 2109, Australia
- Department of Molecular Sciences, Macquarie University, Sydney, New South Wales 2109, Australia
| | - Frédérique Lisacek
- Proteome Informatics Group, SIB Swiss Institute of Bioinformatics, Rue Michel-Servet 1, CH-1211 Geneva, Switzerland
- Computer Science Department, University of Geneva, CH-1227 Geneva, Switzerland
- Section of Biology, University of Geneva, CH-1211 Geneva, Switzerland
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12
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Alocci D, Suchánková P, Costa R, Hory N, Mariethoz J, Vařeková RS, Toukach P, Lisacek F. SugarSketcher: Quick and Intuitive Online Glycan Drawing. Molecules 2018; 23:E3206. [PMID: 30563078 PMCID: PMC6320881 DOI: 10.3390/molecules23123206] [Citation(s) in RCA: 12] [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: 09/14/2018] [Revised: 11/23/2018] [Accepted: 11/29/2018] [Indexed: 01/24/2023] Open
Abstract
SugarSketcher is an intuitive and fast JavaScript interface module for online drawing of glycan structures in the popular Symbol Nomenclature for Glycans (SNFG) notation and exporting them to various commonly used formats encoding carbohydrate sequences (e.g., GlycoCT) or quality images (e.g., svg). It does not require a backend server or any specific browser plugins and can be integrated in any web glycoinformatics project. SugarSketcher allows drawing glycans both for glycobiologists and non-expert users. The "quick mode" allows a newcomer to build up a glycan structure having only a limited knowledge in carbohydrate chemistry. The "normal mode" integrates advanced options which enable glycobiologists to tailor complex carbohydrate structures. The source code is freely available on GitHub and glycoinformaticians are encouraged to participate in the development process while users are invited to test a prototype available on the ExPASY web-site and send feedback.
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Affiliation(s)
- Davide Alocci
- Proteome Informatics Group, SIB Swiss Institute of Bioinformatics, 1211 Geneva, Switzerland.
- Computer Science Department, University of Geneva, 1211 Geneva, Switzerland.
| | - Pavla Suchánková
- CEITEC⁻Central European Institute of Technology, Masaryk University Brno, 625 00 Brno-Bohunice, Czech Republic.
- National Centre for Biomolecular Research, Faculty of Science, 625 00 Brno-Bohunice, Czech Republic.
| | - Renaud Costa
- Polytech Nice Sophia, Campus SophiaTech, 06903 Sophia-Antipolis, France.
| | - Nicolas Hory
- Polytech Nice Sophia, Campus SophiaTech, 06903 Sophia-Antipolis, France.
| | - Julien Mariethoz
- Proteome Informatics Group, SIB Swiss Institute of Bioinformatics, 1211 Geneva, Switzerland.
- Computer Science Department, University of Geneva, 1211 Geneva, Switzerland.
| | - Radka Svobodová Vařeková
- CEITEC⁻Central European Institute of Technology, Masaryk University Brno, 625 00 Brno-Bohunice, Czech Republic.
- National Centre for Biomolecular Research, Faculty of Science, 625 00 Brno-Bohunice, Czech Republic.
| | - Philip Toukach
- Zelinsky Institute of Organic Chemistry, Russian Academy of Sciences, Laboratory of Carbohydrate Chemistry, 119991 Moscow, Russia.
| | - Frédérique Lisacek
- Proteome Informatics Group, SIB Swiss Institute of Bioinformatics, 1211 Geneva, Switzerland.
- Computer Science Department, University of Geneva, 1211 Geneva, Switzerland.
- Section of Biology, University of Geneva, 1211 Geneva, Switzerland.
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13
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Alocci D, Ghraichy M, Barletta E, Gastaldello A, Mariethoz J, Lisacek F. Understanding the glycome: an interactive view of glycosylation from glycocompositions to glycoepitopes. Glycobiology 2018. [PMID: 29518231 DOI: 10.1093/glycob/cwy019] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023] Open
Abstract
Nowadays, due to the advance of experimental techniques in glycomics, large collections of glycan profiles are regularly published. The rapid growth of available glycan data accentuates the lack of innovative tools for visualizing and exploring large amount of information. Scientists resort to using general-purpose spreadsheet applications to create ad hoc data visualization. Thus, results end up being encoded in publication images and text, while valuable curated data is stored in files as supplementary information. To tackle this problem, we have built an interactive pipeline composed with three tools: Glynsight, EpitopeXtractor and Glydin'. Glycan profile data can be imported in Glynsight, which generates a custom interactive glycan profile. Several profiles can be compared and glycan composition is integrated with structural data stored in databases. Glycan structures of interest can then be sent to EpitopeXtractor to perform a glycoepitope extraction. EpitopeXtractor results can be superimposed on the Glydin' glycoepitope network. The network visualization allows fast detection of clusters of glycoepitopes and discovery of potential new targets. Each of these tools is standalone or can be used in conjunction with the others, depending on the data and the specific interest of the user. All the tools composing this pipeline are part of the Glycomics@ExPASy initiative and are available at https://www.expasy.org/glycomics.
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Affiliation(s)
- Davide Alocci
- Proteome Informatics Group, SIB Swiss Institute of Bioinformatics, 7 Route de Drize, 1227 Geneva, Switzerland.,Computer Science Department CUI, University of Geneva, 7 Route de Drize, 1227 Geneva, Switzerland
| | - Marie Ghraichy
- Proteome Informatics Group, SIB Swiss Institute of Bioinformatics, 7 Route de Drize, 1227 Geneva, Switzerland.,Division of Immunology, University Children's Hospital Zurich, Zurich, Switzerland
| | - Elena Barletta
- Proteome Informatics Group, SIB Swiss Institute of Bioinformatics, 7 Route de Drize, 1227 Geneva, Switzerland.,Computer Science Department CUI, University of Geneva, 7 Route de Drize, 1227 Geneva, Switzerland
| | - Alessandra Gastaldello
- Proteome Informatics Group, SIB Swiss Institute of Bioinformatics, 7 Route de Drize, 1227 Geneva, Switzerland.,Computer Science Department CUI, University of Geneva, 7 Route de Drize, 1227 Geneva, Switzerland
| | - Julien Mariethoz
- Proteome Informatics Group, SIB Swiss Institute of Bioinformatics, 7 Route de Drize, 1227 Geneva, Switzerland.,Computer Science Department CUI, University of Geneva, 7 Route de Drize, 1227 Geneva, Switzerland
| | - Frederique Lisacek
- Proteome Informatics Group, SIB Swiss Institute of Bioinformatics, 7 Route de Drize, 1227 Geneva, Switzerland.,Computer Science Department CUI, University of Geneva, 7 Route de Drize, 1227 Geneva, Switzerland.,Section of Biology, University of Geneva, Geneva, Switzerland
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14
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Egorova KS, Toukach PV. Glykoinformatik: Brücken zwischen isolierten Inseln im Datenmeer. Angew Chem Int Ed Engl 2018. [DOI: 10.1002/ange.201803576] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Affiliation(s)
- Ksenia S. Egorova
- Zelinsky Institute of Organic ChemistryRussian Academy of Sciences Leninsky Prospect 47 Moscow 119991 Russland
| | - Philip V. Toukach
- Zelinsky Institute of Organic ChemistryRussian Academy of Sciences Leninsky Prospect 47 Moscow 119991 Russland
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15
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Egorova KS, Toukach PV. Glycoinformatics: Bridging Isolated Islands in the Sea of Data. Angew Chem Int Ed Engl 2018; 57:14986-14990. [DOI: 10.1002/anie.201803576] [Citation(s) in RCA: 25] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2018] [Indexed: 11/07/2022]
Affiliation(s)
- Ksenia S. Egorova
- Zelinsky Institute of Organic ChemistryRussian Academy of Sciences Leninsky Prospect 47 Moscow 119991 Russia
| | - Philip V. Toukach
- Zelinsky Institute of Organic ChemistryRussian Academy of Sciences Leninsky Prospect 47 Moscow 119991 Russia
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16
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Mariethoz J, Alocci D, Gastaldello A, Horlacher O, Gasteiger E, Rojas-Macias M, Karlsson NG, Packer NH, Lisacek F. Glycomics@ExPASy: Bridging the Gap. Mol Cell Proteomics 2018; 17:2164-2176. [PMID: 30097532 PMCID: PMC6210229 DOI: 10.1074/mcp.ra118.000799] [Citation(s) in RCA: 39] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2018] [Revised: 07/15/2018] [Indexed: 12/28/2022] Open
Abstract
Glycomics@ExPASy (https://www.expasy.org/glycomics) is the glycomics tab of ExPASy, the server of SIB Swiss Institute of Bioinformatics. It was created in 2016 to centralize web-based glycoinformatics resources developed within an international network of glycoscientists. The hosted collection currently includes mainly databases and tools created and maintained at SIB but also links to a range of reference resources popular in the glycomics community. The philosophy of our toolbox is that it should be {glycoscientist AND protein scientist}-friendly with the aim of (1) popularizing the use of bioinformatics in glycobiology and (2) emphasizing the relationship between glycobiology and protein-oriented bioinformatics resources. The scarcity of data bridging these two disciplines led us to design tools as interactive as possible based on database connectivity to facilitate data exploration and support hypothesis building. Glycomics@ExPASy was designed, and is developed, with a long-term vision in close collaboration with glycoscientists to meet as closely as possible the growing needs of the community for glycoinformatics.
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Affiliation(s)
- Julien Mariethoz
- From the ‡Proteome Informatics Group, SIB Swiss Institute of Bioinformatics, Geneva, Switzerland
- §Computer Science Department, University of Geneva, Geneva, Switzerland
| | - Davide Alocci
- From the ‡Proteome Informatics Group, SIB Swiss Institute of Bioinformatics, Geneva, Switzerland
- §Computer Science Department, University of Geneva, Geneva, Switzerland
| | - Alessandra Gastaldello
- From the ‡Proteome Informatics Group, SIB Swiss Institute of Bioinformatics, Geneva, Switzerland
- §Computer Science Department, University of Geneva, Geneva, Switzerland
| | - Oliver Horlacher
- From the ‡Proteome Informatics Group, SIB Swiss Institute of Bioinformatics, Geneva, Switzerland
| | - Elisabeth Gasteiger
- ¶Swiss-Prot Group, SIB Swiss Institute of Bioinformatics, Geneva, Switzerland
| | - Miguel Rojas-Macias
- ‖Glyco Inflammatory Group, Department of Medical Biochemistry and Cell Biology, Institute of Biomedicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Niclas G Karlsson
- ‖Glyco Inflammatory Group, Department of Medical Biochemistry and Cell Biology, Institute of Biomedicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Nicolle H Packer
- **Institute for Glycomics, Gold Coast Campus, Griffith University, Southport, QLD, Australia
- ‡‡Biomolecular Discovery & Design Research Centre, Macquarie University, North Ryde, NSW, Australia
| | - Frédérique Lisacek
- From the ‡Proteome Informatics Group, SIB Swiss Institute of Bioinformatics, Geneva, Switzerland;
- §Computer Science Department, University of Geneva, Geneva, Switzerland
- §§Section of Biology, University of Geneva, Geneva, Switzerland
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17
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Smith J, Mittermayr S, Váradi C, Bones J. Quantitative glycomics using liquid phase separations coupled to mass spectrometry. Analyst 2018; 142:700-720. [PMID: 28170017 DOI: 10.1039/c6an02715f] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
Post-translational modification of proteins by the attachment of glycans is governed by a variety of highly specific enzymes and is associated with fundamental impacts on the parent protein's physical, chemical and biological properties. The inherent connection between cellular physiology and specific glycosylation patterns has been shown to offer potential for diagnostic and prognostic monitoring of altered glycosylation in the disease state. Conversely, glycoprotein based biopharmaceuticals have emerged as dominant therapeutic strategies in the treatment of intricate diseases. Glycosylation present on these biopharmaceuticals represents a major critical quality attribute with impacts on both pharmacokinetics and pharmacodynamics. The structural variety of glycans, based upon their non-template driven assembly, poses a significant analytical challenge for both qualitative and quantitative analysis. Labile monosaccharide constituents, isomeric species and often low sample availability from biological sources necessitates meticulous sample handling, ultra-high-resolution analytical separation and sensitive detection techniques, respectively. In this article a critical review of analytical quantitation approaches using liquid phase separations coupled to mass spectrometry for released glycans of biopharmaceutical and biomedical significance is presented. Considerations associated with sample derivatisation strategies, ionisation, relative quantitation through isotopic as well as isobaric labelling, metabolic/enzymatic incorporation and targeted analysis are all thoroughly discussed.
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Affiliation(s)
- Josh Smith
- National Institute for Bioprocessing Research and Training, Fosters Avenue, Mount Merrion, Blackrock, Dublin, A94 X099, Ireland. and School of Biochemistry and Immunology, Trinity Biomedical Sciences Institute, Trinity College Dublin, 152-160 Pearse Street, Dublin 2, D02 R590, Ireland
| | - Stefan Mittermayr
- National Institute for Bioprocessing Research and Training, Fosters Avenue, Mount Merrion, Blackrock, Dublin, A94 X099, Ireland.
| | - Csaba Váradi
- National Institute for Bioprocessing Research and Training, Fosters Avenue, Mount Merrion, Blackrock, Dublin, A94 X099, Ireland.
| | - Jonathan Bones
- National Institute for Bioprocessing Research and Training, Fosters Avenue, Mount Merrion, Blackrock, Dublin, A94 X099, Ireland. and School of Chemical and Bioprocess Engineering, University College Dublin, Belfield, Dublin 4, D04 V1 W8, Ireland
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18
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Minkiewicz P, Iwaniak A, Darewicz M. Annotation of Peptide Structures Using SMILES and Other Chemical Codes-Practical Solutions. Molecules 2017; 22:molecules22122075. [PMID: 29186902 PMCID: PMC6149970 DOI: 10.3390/molecules22122075] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2017] [Revised: 11/15/2017] [Accepted: 11/25/2017] [Indexed: 12/20/2022] Open
Abstract
Contemporary peptide science exploits methods and tools of bioinformatics, and cheminformatics. These approaches use different languages to describe peptide structures—amino acid sequences and chemical codes (especially SMILES), respectively. The latter may be applied, e.g., in comparative studies involving structures and properties of peptides and peptidomimetics. Progress in peptide science “in silico” may be achieved via better communication between biologists and chemists, involving the translation of peptide representation from amino acid sequence into SMILES code. Recent recommendations concerning good practice in chemical information include careful verification of data and their annotation. This publication discusses the generation of SMILES representations of peptides using existing software. Construction of peptide structures containing unnatural and modified amino acids (with special attention paid on glycosylated peptides) is also included. Special attention is paid to the detection and correction of typical errors occurring in SMILES representations of peptides and their correction using molecular editors. Brief recommendations for training of staff working on peptide annotations, are discussed as well.
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Affiliation(s)
- Piotr Minkiewicz
- Chair of Food Biochemistry, Faculty of Food Science, University of Warmia and Mazury in Olsztyn, Plac Cieszyński 1, 10-726 Olsztyn-Kortowo, Poland.
| | - Anna Iwaniak
- Chair of Food Biochemistry, Faculty of Food Science, University of Warmia and Mazury in Olsztyn, Plac Cieszyński 1, 10-726 Olsztyn-Kortowo, Poland.
| | - Małgorzata Darewicz
- Chair of Food Biochemistry, Faculty of Food Science, University of Warmia and Mazury in Olsztyn, Plac Cieszyński 1, 10-726 Olsztyn-Kortowo, Poland.
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19
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Abrahams JL, Campbell MP, Packer NH. Building a PGC-LC-MS N-glycan retention library and elution mapping resource. Glycoconj J 2017; 35:15-29. [PMID: 28905148 DOI: 10.1007/s10719-017-9793-4] [Citation(s) in RCA: 78] [Impact Index Per Article: 11.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2017] [Revised: 08/10/2017] [Accepted: 08/18/2017] [Indexed: 11/27/2022]
Abstract
Porous graphitised carbon-liquid chromatography (PGC-LC) has been proven to be a powerful technique for the analysis and characterisation of complex mixtures of isomeric and isobaric glycan structures. Here we evaluate the elution behaviour of N-glycans on PGC-LC and thereby provide the potential of using chromatographic separation properties, together with mass spectrometry (MS) fragmentation, to determine glycan structure assignments more easily. We used previously reported N-glycan structures released from the purified glycoproteins Immunoglobulin G (IgG), Immunoglobulin A (IgA), lactoferrin, α1-acid glycoprotein, Ribonuclease B (RNase B), fetuin and ovalbumin to profile their behaviour on capillary PGC-LC-MS. Over 100 glycan structures were determined by MS/MS, and together with targeted exoglycosidase digestions, created a N-glycan PGC retention library covering a full spectrum of biologically significant N-glycans from pauci mannose to sialylated tetra-antennary classes. The resultant PGC retention library ( http://www.glycostore.org/showPgc ) incorporates retention times and supporting fragmentation spectra including exoglycosidase digestion products, and provides detailed knowledge on the elution properties of N-glycans by PGC-LC. Consequently, this platform should serve as a valuable resource for facilitating the detailed analysis of the glycosylation of both purified recombinant, and complex mixtures of, glycoproteins using established workflows.
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Affiliation(s)
- Jodie L Abrahams
- Department of Chemistry and Biomolecular Sciences, Faculty of Science & Engineering, Macquarie University, North Ryde, Sydney, NSW, 2109, Australia
- Institute for Glycomics, Griffith University, QLD, Gold Coast, 4222, Australia
| | - Matthew P Campbell
- Department of Chemistry and Biomolecular Sciences, Faculty of Science & Engineering, Macquarie University, North Ryde, Sydney, NSW, 2109, Australia
- Institute for Glycomics, Griffith University, QLD, Gold Coast, 4222, Australia
| | - Nicolle H Packer
- Department of Chemistry and Biomolecular Sciences, Faculty of Science & Engineering, Macquarie University, North Ryde, Sydney, NSW, 2109, Australia.
- Institute for Glycomics, Griffith University, QLD, Gold Coast, 4222, Australia.
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20
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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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21
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Affiliation(s)
- Stefan Gaunitz
- Department of Chemistry, Indiana University, Bloomington, Indiana 47405, United States
| | - Gabe Nagy
- Department of Chemistry, Indiana University, Bloomington, Indiana 47405, United States
| | - Nicola L. B. Pohl
- Department of Chemistry, Indiana University, Bloomington, Indiana 47405, United States
| | - Milos V. Novotny
- Department of Chemistry, Indiana University, Bloomington, Indiana 47405, United States
- Regional Center for Applied Molecular Oncology, Masaryk Memorial Oncological Institute, 656 53 Brno, Czech Republic
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22
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Campbell MP. A Review of Software Applications and Databases for the Interpretation of Glycopeptide Data. TRENDS GLYCOSCI GLYC 2017. [DOI: 10.4052/tigg.1601.1e] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
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23
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Campbell MP, Peterson RA, Gasteiger E, Mariethoz J, Lisacek F, Packer NH. Navigating the Glycome Space and Connecting the Glycoproteome. Methods Mol Biol 2017; 1558:139-158. [PMID: 28150237 DOI: 10.1007/978-1-4939-6783-4_7] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Abstract
UniCarbKB ( http://unicarbkb.org ) is a comprehensive resource for mammalian glycoprotein and annotation data. In particular, the database provides information on the oligosaccharides characterized from a glycoprotein at either the global or site-specific level. This evidence is accumulated from a peer-reviewed and manually curated collection of information on oligosaccharides derived from membrane and secreted glycoproteins purified from biological fluids and/or tissues. This information is further supplemented with experimental method descriptions that summarize important sample preparation and analytical strategies. A new release of UniCarbKB is published every three months, each includes a collection of curated data and improvements to database functionality. In this Chapter, we outline the objectives of UniCarbKB, and describe a selection of step-by-step workflows for navigating the information available. We also provide a short description of web services available and future plans for improving data access. The information presented in this Chapter supplements content available in our knowledgebase including regular updates on interface improvements, new features, and revisions to the database content ( http://confluence.unicarbkb.org ).
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Affiliation(s)
- Matthew P Campbell
- Department of Chemistry and Biomolecular Sciences, Research Drive, Building E8C, Macquarie University, North Ryde, Sydney, 2109, NSW, Australia
| | - Robyn A Peterson
- Department of Chemistry and Biomolecular Sciences, Research Drive, Building E8C, Macquarie University, North Ryde, Sydney, 2109, NSW, Australia
| | - Elisabeth Gasteiger
- Swiss-Prot group, SIB Swiss Institute of Bioinformatics, Battelle - Building A7, Route de Drize, 1227 Carouge, Switzerland
| | - Julien Mariethoz
- Proteome Informatics Group, SIB Swiss Institute of Bioinformatics, Battelle - Building A7, Route de Drize, 1227 Carouge, Switzerland
| | - Frederique Lisacek
- Proteome Informatics Group, SIB Swiss Institute of Bioinformatics, Battelle - Building A7, Route de Drize, 1227 Carouge, Switzerland
- Computer Science Department, University of Geneva, Battelle - Building A7, Route de Drize, 1227 Carouge, Switzerland
| | - Nicolle H Packer
- Department of Chemistry and Biomolecular Sciences, Research Drive, Building E8C, Macquarie University, North Ryde, Sydney, 2109, NSW, Australia.
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24
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Ricard-Blum S, Lisacek F. Glycosaminoglycanomics: where we are. Glycoconj J 2016; 34:339-349. [PMID: 27900575 DOI: 10.1007/s10719-016-9747-2] [Citation(s) in RCA: 33] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2016] [Revised: 10/28/2016] [Accepted: 11/01/2016] [Indexed: 01/21/2023]
Abstract
Glycosaminoglycans regulate numerous physiopathological processes such as development, angiogenesis, innate immunity, cancer and neurodegenerative diseases. Cell surface GAGs are involved in cell-cell and cell-matrix interactions, cell adhesion and signaling, and host-pathogen interactions. GAGs contribute to the assembly of the extracellular matrix and heparan sulfate chains are able to sequester growth factors in the ECM. Their biological activities are regulated by their interactions with proteins. The structural heterogeneity of GAGs, mostly due to chemical modifications occurring during and after their synthesis, makes the development of analytical techniques for their profiling in cells, tissues, and biological fluids, and of computational tools for mining GAG-protein interaction data very challenging. We give here an overview of the experimental approaches used in glycosaminoglycomics, of the major GAG-protein interactomes characterized so far, and of the computational tools and databases available to analyze and store GAG structures and interactions.
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Affiliation(s)
- Sylvie Ricard-Blum
- Institut de Chimie et Biochimie Moléculaires et Supramoléculaires, UMR 5246 CNRS - Université Lyon 1, INSA Lyon, CPE Lyon, 69622, Villeurbanne Cedex, France.
| | - Frédérique Lisacek
- SIB Swiss Institute of Bioinformatics, 1 Rue Michel-Servet, 1211, Geneva, Switzerland.,Computer Science Department, University of Geneva, Geneva, Switzerland
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25
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Arroyuelo A, Vila JA, Martin OA. Azahar: a PyMOL plugin for construction, visualization and analysis of glycan molecules. J Comput Aided Mol Des 2016; 30:619-24. [DOI: 10.1007/s10822-016-9944-x] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2016] [Accepted: 08/17/2016] [Indexed: 10/21/2022]
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26
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Walsh I, Zhao S, Campbell M, Taron CH, Rudd PM. Quantitative profiling of glycans and glycopeptides: an informatics' perspective. Curr Opin Struct Biol 2016; 40:70-80. [PMID: 27522273 DOI: 10.1016/j.sbi.2016.07.022] [Citation(s) in RCA: 28] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2016] [Revised: 07/25/2016] [Accepted: 07/30/2016] [Indexed: 12/16/2022]
Abstract
Experimental techniques to identify and quantify glycan structures in a given sample are continuously improving. However, as they advance data analysis and annotation seems to become more complex. To address this issue, much progress has been made in developing software for interpretation of quantitative glycan profiles. Here, we focus on these informatics tools for high/ultra performance liquid chromatography (H/UPLC), mass spectrometry (MS), tandem mass spectrometry (MSn) and combinations thereof. Software for biomarker discovery, pathway, genomic and disease analysis and a final note on some future prospects for glycoinformatics are also mentioned.
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Affiliation(s)
- Ian Walsh
- Bioprocessing Technology Institute, Agency for Science, Technology and Research (A*STAR), 20 Biopolis Way, #06-01 Centros, Singapore 138668, Singapore; New England Biolabs, Ipswich, MA, United States
| | - Sophie Zhao
- Bioprocessing Technology Institute, Agency for Science, Technology and Research (A*STAR), 20 Biopolis Way, #06-01 Centros, Singapore 138668, Singapore
| | - Matthew Campbell
- Department of Chemistry and Biomolecular Sciences, Macquarie University, Sydney, NSW 2109, Australia
| | | | - Pauline M Rudd
- Bioprocessing Technology Institute, Agency for Science, Technology and Research (A*STAR), 20 Biopolis Way, #06-01 Centros, Singapore 138668, Singapore; National Institute for Bioprocessing Research & Training, Dublin, Ireland.
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27
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Barnett CB, Aoki-Kinoshita KF, Naidoo KJ. The Glycome Analytics Platform: an integrative framework for glycobioinformatics. Bioinformatics 2016; 32:3005-11. [PMID: 27288496 DOI: 10.1093/bioinformatics/btw341] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2015] [Accepted: 05/26/2016] [Indexed: 11/13/2022] Open
Abstract
MOTIVATION Complex carbohydrates play a central role in cellular communication and in disease development. O- and N-glycans, which are post-translationally attached to proteins and lipids, are sugar chains that are rooted, tree structures. Independent efforts to develop computational tools for analyzing complex carbohydrate structures have been designed to exploit specific databases requiring unique formatting and limited transferability. Attempts have been made at integrating these resources, yet it remains difficult to communicate and share data across several online resources. A disadvantage of the lack of coordination between development efforts is the inability of the user community to create reproducible analyses (workflows). The latter results in the more serious unreliability of glycomics metadata. RESULTS In this paper, we realize the significance of connecting multiple online glycan resources that can be used to design reproducible experiments for obtaining, generating and analyzing cell glycomes. To address this, a suite of tools and utilities, have been integrated into the analytic functionality of the Galaxy bioinformatics platform to provide a Glycome Analytics Platform (GAP).Using this platform, users can design in silico workflows to manipulate various formats of glycan sequences and analyze glycomes through access to web data and services. We illustrate the central functionality and features of the GAP by way of example; we analyze and compare the features of the N-glycan glycome of monocytic cells sourced from two separate data depositions.This paper highlights the use of reproducible research methods for glycomics analysis and the GAP presents an opportunity for integrating tools in glycobioinformatics. AVAILABILITY AND IMPLEMENTATION This software is open-source and available online at https://bitbucket.org/scientificomputing/glycome-analytics-platform CONTACTS chris.barnett@uct.ac.za or kevin.naidoo@uct.ac.za SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Christopher B Barnett
- Scientific Computing Research Unit and Department of Chemistry, University of Cape Town, Rondebosch 7701, South Africa
| | - Kiyoko F Aoki-Kinoshita
- Department of Bioinformatics, Faculty of Engineering, Soka University, Hachioji, Tokyo 192-8577, Japan
| | - Kevin J Naidoo
- Scientific Computing Research Unit and Department of Chemistry, University of Cape Town, Rondebosch 7701, South Africa
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28
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Akune Y, Lin CH, Abrahams JL, Zhang J, Packer NH, Aoki-Kinoshita KF, Campbell MP. Comprehensive analysis of the N-glycan biosynthetic pathway using bioinformatics to generate UniCorn: A theoretical N-glycan structure database. Carbohydr Res 2016; 431:56-63. [PMID: 27318307 DOI: 10.1016/j.carres.2016.05.012] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2015] [Revised: 05/23/2016] [Accepted: 05/29/2016] [Indexed: 02/06/2023]
Abstract
Glycan structures attached to proteins are comprised of diverse monosaccharide sequences and linkages that are produced from precursor nucleotide-sugars by a series of glycosyltransferases. Databases of these structures are an essential resource for the interpretation of analytical data and the development of bioinformatics tools. However, with no template to predict what structures are possible the human glycan structure databases are incomplete and rely heavily on the curation of published, experimentally determined, glycan structure data. In this work, a library of 45 human glycosyltransferases was used to generate a theoretical database of N-glycan structures comprised of 15 or less monosaccharide residues. Enzyme specificities were sourced from major online databases including Kyoto Encyclopedia of Genes and Genomes (KEGG) Glycan, Consortium for Functional Glycomics (CFG), Carbohydrate-Active enZymes (CAZy), GlycoGene DataBase (GGDB) and BRENDA. Based on the known activities, more than 1.1 million theoretical structures and 4.7 million synthetic reactions were generated and stored in our database called UniCorn. Furthermore, we analyzed the differences between the predicted glycan structures in UniCorn and those contained in UniCarbKB (www.unicarbkb.org), a database which stores experimentally described glycan structures reported in the literature, and demonstrate that UniCorn can be used to aid in the assignment of ambiguous structures whilst also serving as a discovery database.
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Affiliation(s)
- Yukie Akune
- Department of Chemistry and Biomolecular Sciences, Faculty of Science & Engineering, Macquarie University, Balaclava Road, North Ryde, NSW, 2109, Australia; Department of Bioinformatics, Graduate School of Engineering, Soka University, 1-236, Tangi, Hachioji, 192-8577, Tokyo, Japan
| | - Chi-Hung Lin
- Department of Chemistry and Biomolecular Sciences, Faculty of Science & Engineering, Macquarie University, Balaclava Road, North Ryde, NSW, 2109, Australia
| | - Jodie L Abrahams
- Department of Chemistry and Biomolecular Sciences, Faculty of Science & Engineering, Macquarie University, Balaclava Road, North Ryde, NSW, 2109, Australia
| | - Jingyu Zhang
- Department of Chemistry and Biomolecular Sciences, Faculty of Science & Engineering, Macquarie University, Balaclava Road, North Ryde, NSW, 2109, Australia
| | - Nicolle H Packer
- Department of Chemistry and Biomolecular Sciences, Faculty of Science & Engineering, Macquarie University, Balaclava Road, North Ryde, NSW, 2109, Australia
| | - Kiyoko F Aoki-Kinoshita
- Department of Bioinformatics, Graduate School of Engineering, Soka University, 1-236, Tangi, Hachioji, 192-8577, Tokyo, Japan
| | - Matthew P Campbell
- Department of Chemistry and Biomolecular Sciences, Faculty of Science & Engineering, Macquarie University, Balaclava Road, North Ryde, NSW, 2109, Australia.
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Zhang P, Woen S, Wang T, Liau B, Zhao S, Chen C, Yang Y, Song Z, Wormald MR, Yu C, Rudd PM. Challenges of glycosylation analysis and control: an integrated approach to producing optimal and consistent therapeutic drugs. Drug Discov Today 2016; 21:740-65. [DOI: 10.1016/j.drudis.2016.01.006] [Citation(s) in RCA: 136] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2015] [Revised: 12/22/2015] [Accepted: 01/14/2016] [Indexed: 12/18/2022]
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Planinc A, Bones J, Dejaegher B, Van Antwerpen P, Delporte C. Glycan characterization of biopharmaceuticals: Updates and perspectives. Anal Chim Acta 2016; 921:13-27. [PMID: 27126786 DOI: 10.1016/j.aca.2016.03.049] [Citation(s) in RCA: 56] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2015] [Revised: 03/21/2016] [Accepted: 03/29/2016] [Indexed: 02/01/2023]
Abstract
Therapeutic proteins are rapidly becoming the most promising class of pharmaceuticals on the market due to their successful treatment of a vast array of serious diseases, such as cancers and immune disorders. Therapeutic proteins are produced using recombinant DNA technology. More than 60% of therapeutic proteins are posttranslationally modified following biosynthesis by the addition of N- or O-linked glycans. Glycosylation is the most common posttranslational modifications of proteins. However, it is also the most demanding and complex posttranslational modification from the analytical point of view. Moreover, research has shown that glycosylation significantly impacts stability, half-life, mechanism of action and safety of a therapeutic protein. Considering the exponential growth of biotherapeutics, this present review of the literature (2009-2015) focuses on the characterization of protein glycosylation, which has witnessed an improvement in methodology. Furthermore, it discusses current issues in the fields of production and characterization of therapeutic proteins. This review also highlights the problem of non-standard requirements for the approval of biosimilars with regard to their glycosylation and discusses recent developments and perspectives for improved glycan characterization.
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Affiliation(s)
- Ana Planinc
- Analytical Platform of the Faculty of Pharmacy and Laboratory of Pharmaceutical Chemistry, Faculty of Pharmacy, Universite Libre de Bruxelles (ULB), Brussels, Belgium
| | - Jonathan Bones
- Characterisation and Comparability Laboratory, NIBRT - The National Institute for Bioprocessing Research and Training, Foster Avenue, Mount Merrion, Blackrock, Co. Dublin, Ireland
| | - Bieke Dejaegher
- Laboratory of Instrumental Analysis and Bioelectrochemistry, Faculty of Pharmacy, Université Libre de Bruxelles (ULB), Boulevard du Triomphe, B-1050 Brussels, Belgium; Department of Analytical Chemistry and Pharmaceutical Technology (FABI), Center for Pharmaceutical Research (CePhaR), Faculty of Medicines and Pharmacy, Vrije Universiteit Brussel (VUB), Laarbeeklaan 103, B-1090 Brussels, Belgium
| | - Pierre Van Antwerpen
- Analytical Platform of the Faculty of Pharmacy and Laboratory of Pharmaceutical Chemistry, Faculty of Pharmacy, Universite Libre de Bruxelles (ULB), Brussels, Belgium
| | - Cédric Delporte
- Analytical Platform of the Faculty of Pharmacy and Laboratory of Pharmaceutical Chemistry, Faculty of Pharmacy, Universite Libre de Bruxelles (ULB), Brussels, Belgium.
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Campbell MP, Packer NH. UniCarbKB: New database features for integrating glycan structure abundance, compositional glycoproteomics data, and disease associations. Biochim Biophys Acta Gen Subj 2016; 1860:1669-75. [PMID: 26940363 DOI: 10.1016/j.bbagen.2016.02.016] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2016] [Revised: 02/23/2016] [Accepted: 02/24/2016] [Indexed: 10/22/2022]
Abstract
BACKGROUND UniCarbKB aims to provide a resource for the representation of mammalian glycobiology knowledge by providing a curated database of structural and experimental data, supported by a web application that allows users to easily find and view richly annotated information. The database comprises two levels of annotation (i) global-specific data of oligosaccharides released and characterised from single purified glycoproteins and (ii) information pertaining to site-specific glycan heterogeneity. Additional, contextual information is provided including structural, bibliographic, and taxonomic information for each entry. METHODS Since the launch of UniCarbKB in 2012, we have continued to improve the organisation of our data model. Recently, we have extended our pipeline to collate structural and abundance changes of oligosaccharides in different human disease states and experimental models to extend our coverage of the human glycome. RESULTS In this manuscript, we demonstrate the capability of UniCarbKB to store and query relative glycan abundance data using a set of published colorectal and prostate cancer cell lines as examples. Furthermore, we outline our strategy for managing large-scale glycoproteomics data, site-specific and glycan compositional data, and how this information is adding value to UniCarbKB. Finally, we summarise our efforts to improve the efficient representation of disease terms and associated changes in glycan heterogeneity by integrating the Disease Ontology. CONCLUSIONS Updates and improvements to UniCarbKB have introduced unique features for storing and displaying glycosylation features of mammalian glycoproteins. The integration of site-specific glycosylation data obtained from large-scale glycoproteomics and introduction of cell line studies will improve the analysis of glycoproteins and entire glycomes. GENERAL SIGNIFICANCE Continuing advancements in analytical technologies and new data types are advancing disease-related glycomics. It is increasingly necessary to ensure all the data are comprehensively annotated. UniCarbKB was established with the mission of providing a resource for human glycobiology by capturing a wide range of data with corresponding annotations. This article is part of a Special Issue entitled "Glycans in personalised medicine" Guest Editor: Professor Gordan Lauc.
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Affiliation(s)
- Matthew P Campbell
- Department of Chemistry and Biomolecular Sciences, Biomolecular Frontiers Research Centre, Macquarie University, Sydney 2109, Australia.
| | - Nicolle H Packer
- Department of Chemistry and Biomolecular Sciences, Biomolecular Frontiers Research Centre, Macquarie University, Sydney 2109, Australia
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Mariethoz J, Khatib K, Alocci D, Campbell MP, Karlsson NG, Packer NH, Mullen EH, Lisacek F. SugarBindDB, a resource of glycan-mediated host-pathogen interactions. Nucleic Acids Res 2016; 44:D1243-50. [PMID: 26578555 PMCID: PMC4702881 DOI: 10.1093/nar/gkv1247] [Citation(s) in RCA: 34] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2015] [Revised: 10/22/2015] [Accepted: 10/31/2015] [Indexed: 12/16/2022] Open
Abstract
The SugarBind Database (SugarBindDB) covers knowledge of glycan binding of human pathogen lectins and adhesins. It is a curated database; each glycan-protein binding pair is associated with at least one published reference. The core data element of SugarBindDB is a set of three inseparable components: the pathogenic agent, a lectin/adhesin and a glycan ligand. Each entity (agent, lectin or ligand) is described by a range of properties that are summarized in an entity-dedicated page. Several search, navigation and visualisation tools are implemented to investigate the functional role of glycans in pathogen binding. The database is cross-linked to protein and glycan-relaled resources such as UniProtKB and UniCarbKB. It is tightly bound to the latter via a substructure search tool that maps each ligand to full structures where it occurs. Thus, a glycan-lectin binding pair of SugarBindDB can lead to the identification of a glycan-mediated protein-protein interaction, that is, a lectin-glycoprotein interaction, via substructure search and the knowledge of site-specific glycosylation stored in UniCarbKB. SugarBindDB is accessible at: http://sugarbind.expasy.org.
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Affiliation(s)
- Julien Mariethoz
- Proteome Informatics Group, SIB Swiss Institute of Bioinformatics, Geneva, Switzerland
| | | | - Davide Alocci
- Proteome Informatics Group, SIB Swiss Institute of Bioinformatics, Geneva, Switzerland Department of Computer Science, University of Geneva, Geneva, Switzerland
| | - Matthew P Campbell
- Biomolecular Frontiers Research Centre, Macquarie University, North Ryde, NSW, Australia
| | - Niclas G Karlsson
- University of Gothenburg, Sahlgrenska Academy, Institute of Biomedicine, Department of Medical Biochemistry and Cell Biology, Gothenburg, Sweden
| | - Nicolle H Packer
- Biomolecular Frontiers Research Centre, Macquarie University, North Ryde, NSW, Australia
| | | | - Frederique Lisacek
- Proteome Informatics Group, SIB Swiss Institute of Bioinformatics, Geneva, Switzerland Department of Computer Science, University of Geneva, Geneva, Switzerland
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Alocci D, Mariethoz J, Horlacher O, Bolleman JT, Campbell MP, Lisacek F. Property Graph vs RDF Triple Store: A Comparison on Glycan Substructure Search. PLoS One 2015; 10:e0144578. [PMID: 26656740 PMCID: PMC4684231 DOI: 10.1371/journal.pone.0144578] [Citation(s) in RCA: 32] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2015] [Accepted: 11/22/2015] [Indexed: 11/18/2022] Open
Abstract
Resource description framework (RDF) and Property Graph databases are emerging technologies that are used for storing graph-structured data. We compare these technologies through a molecular biology use case: glycan substructure search. Glycans are branched tree-like molecules composed of building blocks linked together by chemical bonds. The molecular structure of a glycan can be encoded into a direct acyclic graph where each node represents a building block and each edge serves as a chemical linkage between two building blocks. In this context, Graph databases are possible software solutions for storing glycan structures and Graph query languages, such as SPARQL and Cypher, can be used to perform a substructure search. Glycan substructure searching is an important feature for querying structure and experimental glycan databases and retrieving biologically meaningful data. This applies for example to identifying a region of the glycan recognised by a glycan binding protein (GBP). In this study, 19,404 glycan structures were selected from GlycomeDB (www.glycome-db.org) and modelled for being stored into a RDF triple store and a Property Graph. We then performed two different sets of searches and compared the query response times and the results from both technologies to assess performance and accuracy. The two implementations produced the same results, but interestingly we noted a difference in the query response times. Qualitative measures such as portability were also used to define further criteria for choosing the technology adapted to solving glycan substructure search and other comparable issues.
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Affiliation(s)
- Davide Alocci
- Proteome Informatics Group, SIB Swiss Institute of Bioinformatics, Geneva, 1211, Switzerland
- Computer Science Department, University of Geneva, Geneva, 1227, Switzerland
| | - Julien Mariethoz
- Proteome Informatics Group, SIB Swiss Institute of Bioinformatics, Geneva, 1211, Switzerland
| | - Oliver Horlacher
- Proteome Informatics Group, SIB Swiss Institute of Bioinformatics, Geneva, 1211, Switzerland
- Computer Science Department, University of Geneva, Geneva, 1227, Switzerland
| | - Jerven T. Bolleman
- Swiss-Prot Group, SIB Swiss Institute of Bioinformatics, Geneva, 1211, Switzerland
| | - Matthew P. Campbell
- Department of Chemistry and Biomolecular Sciences, Macquarie University, Sydney, Australia
| | - Frederique Lisacek
- Proteome Informatics Group, SIB Swiss Institute of Bioinformatics, Geneva, 1211, Switzerland
- Computer Science Department, University of Geneva, Geneva, 1227, Switzerland
- * E-mail:
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Shu L, Suter MJF, Räsänen K. Evolution of egg coats: linking molecular biology and ecology. Mol Ecol 2015; 24:4052-73. [DOI: 10.1111/mec.13283] [Citation(s) in RCA: 37] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2014] [Revised: 06/12/2015] [Accepted: 06/17/2015] [Indexed: 12/18/2022]
Affiliation(s)
- Longfei Shu
- Department of Aquatic Ecology; Swiss Federal Institute of Aquatic Science and Technology; Eawag; 8600 Duebendorf Switzerland
- Institute of Integrative Biology; ETH Zurich; 8092 Zurich Switzerland
| | - Marc J.-F. Suter
- Department of Environmental Toxicology; Swiss Federal Institute of Aquatic Science and Technology; Eawag; 8600 Duebendorf Switzerland
- Department of Environmental Systems Science; Swiss Federal Institute of Technology; ETH Zurich; 8092 Zurich Switzerland
| | - Katja Räsänen
- Department of Aquatic Ecology; Swiss Federal Institute of Aquatic Science and Technology; Eawag; 8600 Duebendorf Switzerland
- Institute of Integrative Biology; ETH Zurich; 8092 Zurich Switzerland
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36
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Horlacher O, Nikitin F, Alocci D, Mariethoz J, Müller M, Lisacek F. MzJava: An open source library for mass spectrometry data processing. J Proteomics 2015; 129:63-70. [PMID: 26141507 DOI: 10.1016/j.jprot.2015.06.013] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2015] [Revised: 06/17/2015] [Accepted: 06/22/2015] [Indexed: 10/23/2022]
Abstract
Mass spectrometry (MS) is a widely used and evolving technique for the high-throughput identification of molecules in biological samples. The need for sharing and reuse of code among bioinformaticians working with MS data prompted the design and implementation of MzJava, an open-source Java Application Programming Interface (API) for MS related data processing. MzJava provides data structures and algorithms for representing and processing mass spectra and their associated biological molecules, such as metabolites, glycans and peptides. MzJava includes functionality to perform mass calculation, peak processing (e.g. centroiding, filtering, transforming), spectrum alignment and clustering, protein digestion, fragmentation of peptides and glycans as well as scoring functions for spectrum-spectrum and peptide/glycan-spectrum matches. For data import and export MzJava implements readers and writers for commonly used data formats. For many classes support for the Hadoop MapReduce (hadoop.apache.org) and Apache Spark (spark.apache.org) frameworks for cluster computing was implemented. The library has been developed applying best practices of software engineering. To ensure that MzJava contains code that is correct and easy to use the library's API was carefully designed and thoroughly tested. MzJava is an open-source project distributed under the AGPL v3.0 licence. MzJava requires Java 1.7 or higher. Binaries, source code and documentation can be downloaded from http://mzjava.expasy.org and https://bitbucket.org/sib-pig/mzjava. This article is part of a Special Issue entitled: Computational Proteomics.
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Affiliation(s)
- Oliver Horlacher
- Proteome Informatics Group, SIB Swiss Institute of Bioinformatics, Geneva 1211, Switzerland; Centre Universitaire de Bioinformatique, University of Geneva, Geneva 1211, Switzerland
| | - Frederic Nikitin
- Proteome Informatics Group, SIB Swiss Institute of Bioinformatics, Geneva 1211, Switzerland
| | - Davide Alocci
- Proteome Informatics Group, SIB Swiss Institute of Bioinformatics, Geneva 1211, Switzerland; Centre Universitaire de Bioinformatique, University of Geneva, Geneva 1211, Switzerland
| | - Julien Mariethoz
- Proteome Informatics Group, SIB Swiss Institute of Bioinformatics, Geneva 1211, Switzerland
| | - Markus Müller
- Proteome Informatics Group, SIB Swiss Institute of Bioinformatics, Geneva 1211, Switzerland; Centre Universitaire de Bioinformatique, University of Geneva, Geneva 1211, Switzerland.
| | - Frederique Lisacek
- Proteome Informatics Group, SIB Swiss Institute of Bioinformatics, Geneva 1211, Switzerland; Centre Universitaire de Bioinformatique, University of Geneva, Geneva 1211, Switzerland.
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Song X, Heimburg-Molinaro J, Smith DF, Cummings RD. Glycan microarrays of fluorescently-tagged natural glycans. Glycoconj J 2015; 32:465-73. [PMID: 25877830 DOI: 10.1007/s10719-015-9584-8] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2015] [Revised: 03/02/2015] [Accepted: 03/19/2015] [Indexed: 01/22/2023]
Abstract
This review discusses the challenges facing research in 'functional glycomics' and the novel technologies that are being developed to advance the field. The structural complexity of glycans and glycoconjugates makes studies of both their structures and recognition difficult. However, these intricate structures can be captured from their natural sources, isolated and fluorescently-tagged for detailed structural analysis and for presentation on glycan microarrays for functional recognition by glycan-binding proteins. These advances in glycan preparation and manipulation enable the streamlining of functional glycomics studies and will help to propel the field forward in studying natural, biologically relevant glycans.
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Affiliation(s)
- Xuezheng Song
- Department of Biochemistry, The National Center for Functional Glycomics, Emory University School of Medicine, Atlanta, GA, 30322, USA. .,Department of Biochemistry, O. Wayne Rollins Research Center, Emory University School of Medicine, 1510 Clifton Road, Suite 4025, Atlanta, GA, 30322, USA.
| | - Jamie Heimburg-Molinaro
- Department of Biochemistry, The National Center for Functional Glycomics, Emory University School of Medicine, Atlanta, GA, 30322, USA.,Department of Biochemistry, O. Wayne Rollins Research Center, Emory University School of Medicine, 1510 Clifton Road, Suite 4025, Atlanta, GA, 30322, USA
| | - David F Smith
- Department of Biochemistry, The National Center for Functional Glycomics, Emory University School of Medicine, Atlanta, GA, 30322, USA.,Department of Biochemistry, O. Wayne Rollins Research Center, Emory University School of Medicine, 1510 Clifton Road, Suite 4025, Atlanta, GA, 30322, USA
| | - Richard D Cummings
- Department of Biochemistry, The National Center for Functional Glycomics, Emory University School of Medicine, Atlanta, GA, 30322, USA.,Department of Biochemistry, O. Wayne Rollins Research Center, Emory University School of Medicine, 1510 Clifton Road, Suite 4025, Atlanta, GA, 30322, USA
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Ranzinger R, Aoki-Kinoshita KF, Campbell MP, Kawano S, Lütteke T, Okuda S, Shinmachi D, Shikanai T, Sawaki H, Toukach P, Matsubara M, Yamada I, Narimatsu H. GlycoRDF: an ontology to standardize glycomics data in RDF. Bioinformatics 2015; 31:919-25. [PMID: 25388145 PMCID: PMC4380026 DOI: 10.1093/bioinformatics/btu732] [Citation(s) in RCA: 32] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2014] [Revised: 10/12/2014] [Accepted: 10/28/2014] [Indexed: 11/13/2022] Open
Abstract
MOTIVATION Over the last decades several glycomics-based bioinformatics resources and databases have been created and released to the public. Unfortunately, there is no common standard in the representation of the stored information or a common machine-readable interface allowing bioinformatics groups to easily extract and cross-reference the stored information. RESULTS An international group of bioinformatics experts in the field of glycomics have worked together to create a standard Resource Description Framework (RDF) representation for glycomics data, focused on glycan sequences and related biological source, publications and experimental data. This RDF standard is defined by the GlycoRDF ontology and will be used by database providers to generate common machine-readable exports of the data stored in their databases. AVAILABILITY AND IMPLEMENTATION The ontology, supporting documentation and source code used by database providers to generate standardized RDF are available online (http://www.glycoinfo.org/GlycoRDF/).
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Affiliation(s)
- Rene Ranzinger
- Complex Carbohydrate Research Center, University of Georgia, Athens, GA, USA, Research Center for Medical Glycoscience, National Institute of Advanced Industrial Science and Technology, Tsukuba, Japan, Faculty of Engineering, Soka University, Tokyo, Japan, Biomolecular Frontiers Research Centre, Macquarie University, Sydney, Australia, Database Center for Life Science, Research Organization of Information and Systems, Chiba, Japan, Institute of Veterinary Physiology and Biochemistry, Justus-Liebig-University Giessen, Giessen, Germany, Graduate School of Medical and Dental Sciences, Niigata University, Niigata, Japan, N.D. Zelinsky Institute of Organic Chemistry, Russian Academy of Sciences, Moscow, Russia and Laboratory of Glyco-organic Chemistry, The Noguchi Institute, Tokyo, Japan
| | - Kiyoko F Aoki-Kinoshita
- Complex Carbohydrate Research Center, University of Georgia, Athens, GA, USA, Research Center for Medical Glycoscience, National Institute of Advanced Industrial Science and Technology, Tsukuba, Japan, Faculty of Engineering, Soka University, Tokyo, Japan, Biomolecular Frontiers Research Centre, Macquarie University, Sydney, Australia, Database Center for Life Science, Research Organization of Information and Systems, Chiba, Japan, Institute of Veterinary Physiology and Biochemistry, Justus-Liebig-University Giessen, Giessen, Germany, Graduate School of Medical and Dental Sciences, Niigata University, Niigata, Japan, N.D. Zelinsky Institute of Organic Chemistry, Russian Academy of Sciences, Moscow, Russia and Laboratory of Glyco-organic Chemistry, The Noguchi Institute, Tokyo, Japan Complex Carbohydrate Research Center, University of Georgia, Athens, GA, USA, Research Center for Medical Glycoscience, National Institute of Advanced Industrial Science and Technology, Tsukuba, Japan, Faculty of Engineering, Soka University, Tokyo, Japan, Biomolecular Frontiers Research Centre, Macquarie University, Sydney, Australia, Database Center for Life Science, Research Organization of Information and Systems, Chiba, Japan, Institute of Veterinary Physiology and Biochemistry, Justus-Liebig-University Giessen, Giessen, Germany, Graduate School of Medical and Dental Sciences, Niigata University, Niigata, Japan, N.D. Zelinsky Institute of Organic Chemistry, Russian Academy of Sciences, Moscow, Russia and Laboratory of Glyco-organic Chemistry, The Noguchi Institute, Tokyo, Japan
| | - Matthew P Campbell
- Complex Carbohydrate Research Center, University of Georgia, Athens, GA, USA, Research Center for Medical Glycoscience, National Institute of Advanced Industrial Science and Technology, Tsukuba, Japan, Faculty of Engineering, Soka University, Tokyo, Japan, Biomolecular Frontiers Research Centre, Macquarie University, Sydney, Australia, Database Center for Life Science, Research Organization of Information and Systems, Chiba, Japan, Institute of Veterinary Physiology and Biochemistry, Justus-Liebig-University Giessen, Giessen, Germany, Graduate School of Medical and Dental Sciences, Niigata University, Niigata, Japan, N.D. Zelinsky Institute of Organic Chemistry, Russian Academy of Sciences, Moscow, Russia and Laboratory of Glyco-organic Chemistry, The Noguchi Institute, Tokyo, Japan
| | - Shin Kawano
- Complex Carbohydrate Research Center, University of Georgia, Athens, GA, USA, Research Center for Medical Glycoscience, National Institute of Advanced Industrial Science and Technology, Tsukuba, Japan, Faculty of Engineering, Soka University, Tokyo, Japan, Biomolecular Frontiers Research Centre, Macquarie University, Sydney, Australia, Database Center for Life Science, Research Organization of Information and Systems, Chiba, Japan, Institute of Veterinary Physiology and Biochemistry, Justus-Liebig-University Giessen, Giessen, Germany, Graduate School of Medical and Dental Sciences, Niigata University, Niigata, Japan, N.D. Zelinsky Institute of Organic Chemistry, Russian Academy of Sciences, Moscow, Russia and Laboratory of Glyco-organic Chemistry, The Noguchi Institute, Tokyo, Japan
| | - Thomas Lütteke
- Complex Carbohydrate Research Center, University of Georgia, Athens, GA, USA, Research Center for Medical Glycoscience, National Institute of Advanced Industrial Science and Technology, Tsukuba, Japan, Faculty of Engineering, Soka University, Tokyo, Japan, Biomolecular Frontiers Research Centre, Macquarie University, Sydney, Australia, Database Center for Life Science, Research Organization of Information and Systems, Chiba, Japan, Institute of Veterinary Physiology and Biochemistry, Justus-Liebig-University Giessen, Giessen, Germany, Graduate School of Medical and Dental Sciences, Niigata University, Niigata, Japan, N.D. Zelinsky Institute of Organic Chemistry, Russian Academy of Sciences, Moscow, Russia and Laboratory of Glyco-organic Chemistry, The Noguchi Institute, Tokyo, Japan
| | - Shujiro Okuda
- Complex Carbohydrate Research Center, University of Georgia, Athens, GA, USA, Research Center for Medical Glycoscience, National Institute of Advanced Industrial Science and Technology, Tsukuba, Japan, Faculty of Engineering, Soka University, Tokyo, Japan, Biomolecular Frontiers Research Centre, Macquarie University, Sydney, Australia, Database Center for Life Science, Research Organization of Information and Systems, Chiba, Japan, Institute of Veterinary Physiology and Biochemistry, Justus-Liebig-University Giessen, Giessen, Germany, Graduate School of Medical and Dental Sciences, Niigata University, Niigata, Japan, N.D. Zelinsky Institute of Organic Chemistry, Russian Academy of Sciences, Moscow, Russia and Laboratory of Glyco-organic Chemistry, The Noguchi Institute, Tokyo, Japan
| | - Daisuke Shinmachi
- Complex Carbohydrate Research Center, University of Georgia, Athens, GA, USA, Research Center for Medical Glycoscience, National Institute of Advanced Industrial Science and Technology, Tsukuba, Japan, Faculty of Engineering, Soka University, Tokyo, Japan, Biomolecular Frontiers Research Centre, Macquarie University, Sydney, Australia, Database Center for Life Science, Research Organization of Information and Systems, Chiba, Japan, Institute of Veterinary Physiology and Biochemistry, Justus-Liebig-University Giessen, Giessen, Germany, Graduate School of Medical and Dental Sciences, Niigata University, Niigata, Japan, N.D. Zelinsky Institute of Organic Chemistry, Russian Academy of Sciences, Moscow, Russia and Laboratory of Glyco-organic Chemistry, The Noguchi Institute, Tokyo, Japan
| | - Toshihide Shikanai
- Complex Carbohydrate Research Center, University of Georgia, Athens, GA, USA, Research Center for Medical Glycoscience, National Institute of Advanced Industrial Science and Technology, Tsukuba, Japan, Faculty of Engineering, Soka University, Tokyo, Japan, Biomolecular Frontiers Research Centre, Macquarie University, Sydney, Australia, Database Center for Life Science, Research Organization of Information and Systems, Chiba, Japan, Institute of Veterinary Physiology and Biochemistry, Justus-Liebig-University Giessen, Giessen, Germany, Graduate School of Medical and Dental Sciences, Niigata University, Niigata, Japan, N.D. Zelinsky Institute of Organic Chemistry, Russian Academy of Sciences, Moscow, Russia and Laboratory of Glyco-organic Chemistry, The Noguchi Institute, Tokyo, Japan
| | - Hiromichi Sawaki
- Complex Carbohydrate Research Center, University of Georgia, Athens, GA, USA, Research Center for Medical Glycoscience, National Institute of Advanced Industrial Science and Technology, Tsukuba, Japan, Faculty of Engineering, Soka University, Tokyo, Japan, Biomolecular Frontiers Research Centre, Macquarie University, Sydney, Australia, Database Center for Life Science, Research Organization of Information and Systems, Chiba, Japan, Institute of Veterinary Physiology and Biochemistry, Justus-Liebig-University Giessen, Giessen, Germany, Graduate School of Medical and Dental Sciences, Niigata University, Niigata, Japan, N.D. Zelinsky Institute of Organic Chemistry, Russian Academy of Sciences, Moscow, Russia and Laboratory of Glyco-organic Chemistry, The Noguchi Institute, Tokyo, Japan
| | - Philip Toukach
- Complex Carbohydrate Research Center, University of Georgia, Athens, GA, USA, Research Center for Medical Glycoscience, National Institute of Advanced Industrial Science and Technology, Tsukuba, Japan, Faculty of Engineering, Soka University, Tokyo, Japan, Biomolecular Frontiers Research Centre, Macquarie University, Sydney, Australia, Database Center for Life Science, Research Organization of Information and Systems, Chiba, Japan, Institute of Veterinary Physiology and Biochemistry, Justus-Liebig-University Giessen, Giessen, Germany, Graduate School of Medical and Dental Sciences, Niigata University, Niigata, Japan, N.D. Zelinsky Institute of Organic Chemistry, Russian Academy of Sciences, Moscow, Russia and Laboratory of Glyco-organic Chemistry, The Noguchi Institute, Tokyo, Japan
| | - Masaaki Matsubara
- Complex Carbohydrate Research Center, University of Georgia, Athens, GA, USA, Research Center for Medical Glycoscience, National Institute of Advanced Industrial Science and Technology, Tsukuba, Japan, Faculty of Engineering, Soka University, Tokyo, Japan, Biomolecular Frontiers Research Centre, Macquarie University, Sydney, Australia, Database Center for Life Science, Research Organization of Information and Systems, Chiba, Japan, Institute of Veterinary Physiology and Biochemistry, Justus-Liebig-University Giessen, Giessen, Germany, Graduate School of Medical and Dental Sciences, Niigata University, Niigata, Japan, N.D. Zelinsky Institute of Organic Chemistry, Russian Academy of Sciences, Moscow, Russia and Laboratory of Glyco-organic Chemistry, The Noguchi Institute, Tokyo, Japan
| | - Issaku Yamada
- Complex Carbohydrate Research Center, University of Georgia, Athens, GA, USA, Research Center for Medical Glycoscience, National Institute of Advanced Industrial Science and Technology, Tsukuba, Japan, Faculty of Engineering, Soka University, Tokyo, Japan, Biomolecular Frontiers Research Centre, Macquarie University, Sydney, Australia, Database Center for Life Science, Research Organization of Information and Systems, Chiba, Japan, Institute of Veterinary Physiology and Biochemistry, Justus-Liebig-University Giessen, Giessen, Germany, Graduate School of Medical and Dental Sciences, Niigata University, Niigata, Japan, N.D. Zelinsky Institute of Organic Chemistry, Russian Academy of Sciences, Moscow, Russia and Laboratory of Glyco-organic Chemistry, The Noguchi Institute, Tokyo, Japan
| | - Hisashi Narimatsu
- Complex Carbohydrate Research Center, University of Georgia, Athens, GA, USA, Research Center for Medical Glycoscience, National Institute of Advanced Industrial Science and Technology, Tsukuba, Japan, Faculty of Engineering, Soka University, Tokyo, Japan, Biomolecular Frontiers Research Centre, Macquarie University, Sydney, Australia, Database Center for Life Science, Research Organization of Information and Systems, Chiba, Japan, Institute of Veterinary Physiology and Biochemistry, Justus-Liebig-University Giessen, Giessen, Germany, Graduate School of Medical and Dental Sciences, Niigata University, Niigata, Japan, N.D. Zelinsky Institute of Organic Chemistry, Russian Academy of Sciences, Moscow, Russia and Laboratory of Glyco-organic Chemistry, The Noguchi Institute, Tokyo, Japan
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Goldman R, Sanda M. Targeted methods for quantitative analysis of protein glycosylation. Proteomics Clin Appl 2015; 9:17-32. [PMID: 25522218 PMCID: PMC5780646 DOI: 10.1002/prca.201400152] [Citation(s) in RCA: 36] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2014] [Revised: 11/15/2014] [Accepted: 12/11/2014] [Indexed: 12/17/2022]
Abstract
Quantification of proteins by LC-MS/MS-MRM has become a standard method with broad projected clinical applicability. MRM quantification of protein modifications is, however, far less utilized, especially in the case of glycoproteins. This review summarizes current methods for quantitative analysis of protein glycosylation with a focus on MRM methods. We describe advantages of this quantitative approach, analytical parameters that need to be optimized to achieve reliable measurements, and point out the limitations. Differences between major classes of N- and O-glycopeptides are described and class-specific glycopeptide assays are demonstrated.
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Affiliation(s)
- Radoslav Goldman
- Department of Oncology, Lombardi Comprehensive Cancer Center, Washington, DC, USA
- Department of Biochemistry and Molecular and Cellular Biology, Georgetown University, Washington, DC, USA
| | - Miloslav Sanda
- Department of Oncology, Lombardi Comprehensive Cancer Center, Washington, DC, USA
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40
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Stavenhagen K, Kolarich D, Wuhrer M. Clinical Glycomics Employing Graphitized Carbon Liquid Chromatography-Mass Spectrometry. Chromatographia 2014; 78:307-320. [PMID: 25750456 PMCID: PMC4346670 DOI: 10.1007/s10337-014-2813-7] [Citation(s) in RCA: 66] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2014] [Revised: 10/25/2014] [Accepted: 11/13/2014] [Indexed: 12/25/2022]
Abstract
Glycoconjugates and free glycan are involved in a variety of biological processes such as cell-cell interaction and cell trafficking. Alterations in the complex glycosylation machinery have been correlated with various pathological processes including cancer progression and metastasis. Mass Spectrometry (MS) has evolved as one of the most powerful tools in glycomics and glycoproteomics and in combination with porous graphitized carbon-liquid chromatography (PGC-LC) it is a versatile and sensitive technique for the analysis of glycans and to some extent also glycopeptides. PGC-LC-ESI-MS analysis is characterized by a high isomer separation power enabling a specific glycan compound analysis on the level of individual structures. This allows the investigation of the biological relevance of particular glycan structures and glycan features. Consequently, this strategy is a very powerful technique suitable for clinical research, such as cancer biomarker discovery, as well as in-depth analysis of recombinant glycoproteins. In this review, we will focus on how PGC in conjunction with MS detection can deliver specific structural information for clinical research on protein-bound N-glycans and mucin-type O-glycans. In addition, we will briefly review PGC analysis approaches for glycopeptides, glycosaminoglycans (GAGs) and human milk oligosaccharides (HMOs). The presented applications cover systems that vary vastly with regard to complexity such as purified glycoproteins, cells, tissue or body fluids revealing specific glycosylation changes associated with various biological processes including cancer and inflammation.
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Affiliation(s)
- Kathrin Stavenhagen
- Division of BioAnalytical Chemistry, VU University Amsterdam, De Boelelaan 1083, 1081 HV Amsterdam, The Netherlands
| | - Daniel Kolarich
- Department of Biomolecular Systems, Max Planck Institute of Colloids and Interfaces, Wissenschaftspark Potsdam-Golm, Am Mühlenberg 1 OT Golm, 14242 Potsdam, Germany
| | - Manfred Wuhrer
- Division of BioAnalytical Chemistry, VU University Amsterdam, De Boelelaan 1083, 1081 HV Amsterdam, The Netherlands ; Department of Molecular Cell Biology and Immunology, VU University Medical Center, Amsterdam, The Netherlands ; Center for Proteomics and Metabolomics, Leiden University Medical Center, Leiden, The Netherlands
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41
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High-Throughput Analysis and Automation for Glycomics Studies. Chromatographia 2014; 78:321-333. [PMID: 25814696 PMCID: PMC4363487 DOI: 10.1007/s10337-014-2803-9] [Citation(s) in RCA: 60] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2014] [Revised: 10/17/2014] [Accepted: 10/17/2014] [Indexed: 11/12/2022]
Abstract
This review covers advances in analytical technologies for high-throughput (HTP) glycomics. Our focus is on structural studies of glycoprotein glycosylation to support biopharmaceutical realization and the discovery of glycan biomarkers for human disease. For biopharmaceuticals, there is increasing use of glycomics in Quality by Design studies to help optimize glycan profiles of drugs with a view to improving their clinical performance. Glycomics is also used in comparability studies to ensure consistency of glycosylation both throughout product development and between biosimilars and innovator drugs. In clinical studies there is as well an expanding interest in the use of glycomics—for example in Genome Wide Association Studies—to follow changes in glycosylation patterns of biological tissues and fluids with the progress of certain diseases. These include cancers, neurodegenerative disorders and inflammatory conditions. Despite rising activity in this field, there are significant challenges in performing large scale glycomics studies. The requirement is accurate identification and quantitation of individual glycan structures. However, glycoconjugate samples are often very complex and heterogeneous and contain many diverse branched glycan structures. In this article we cover HTP sample preparation and derivatization methods, sample purification, robotization, optimized glycan profiling by UHPLC, MS and multiplexed CE, as well as hyphenated techniques and automated data analysis tools. Throughout, we summarize the advantages and challenges with each of these technologies. The issues considered include reliability of the methods for glycan identification and quantitation, sample throughput, labor intensity, and affordability for large sample numbers.
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Al Jadda K, Porterfield MP, Bridger R, Heiss C, Tiemeyer M, Wells L, Miller JA, York WS, Ranzinger R. EUROCarbDB(CCRC): a EUROCarbDB node for storing glycomics standard data. ACTA ACUST UNITED AC 2014; 31:242-5. [PMID: 25217575 DOI: 10.1093/bioinformatics/btu609] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022]
Abstract
MOTIVATION In the field of glycomics research, several different techniques are used for structure elucidation. Although multiple techniques are often used to increase confidence in structure assignments, most glycomics databases allow storing of only a single type of experimental data. In addition, the methods used to prepare a sample for analysis is seldom recorded making it harder to reproduce the analytical data and results. RESULTS We have extended the freely available EUROCarbDB framework to allow the submission of experimental data and the reporting of several orthogonal experimental datasets. The features aim to increase the understandability and reproducibility of the reported data. AVAILABILITY AND IMPLEMENTATION The installation with the glycan standards is available at http://glycomics.ccrc.uga.edu/eurocarb/. The source code of the project is available at https://code.google.com/p/ucdb/. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Khalifeh Al Jadda
- Department of Computer Science and Complex Carbohydrate Research Center, University of Georgia, Athens, GA 30602, USA
| | - Melody P Porterfield
- Department of Computer Science and Complex Carbohydrate Research Center, University of Georgia, Athens, GA 30602, USA
| | - Robert Bridger
- Department of Computer Science and Complex Carbohydrate Research Center, University of Georgia, Athens, GA 30602, USA
| | - Christian Heiss
- Department of Computer Science and Complex Carbohydrate Research Center, University of Georgia, Athens, GA 30602, USA
| | - Michael Tiemeyer
- Department of Computer Science and Complex Carbohydrate Research Center, University of Georgia, Athens, GA 30602, USA
| | - Lance Wells
- Department of Computer Science and Complex Carbohydrate Research Center, University of Georgia, Athens, GA 30602, USA
| | - John A Miller
- Department of Computer Science and Complex Carbohydrate Research Center, University of Georgia, Athens, GA 30602, USA
| | - William S York
- Department of Computer Science and Complex Carbohydrate Research Center, University of Georgia, Athens, GA 30602, USA
| | - Rene Ranzinger
- Department of Computer Science and Complex Carbohydrate Research Center, University of Georgia, Athens, GA 30602, USA
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Agravat SB, Saltz JH, Cummings RD, Smith DF. GlycoPattern: a web platform for glycan array mining. Bioinformatics 2014; 30:3417-8. [PMID: 25143288 DOI: 10.1093/bioinformatics/btu559] [Citation(s) in RCA: 36] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
UNLABELLED GlycoPattern is Web-based bioinformatics resource to support the analysis of glycan array data for the Consortium for Functional Glycomics. This resource includes algorithms and tools to discover structural motifs, a heatmap visualization to compare multiple experiments, hierarchical clustering of Glycan Binding Proteins with respect to their binding motifs and a structural search feature on the experimental data. AVAILABILITY AND IMPLEMENTATION GlycoPattern is freely available on the Web at http://glycopattern.emory.edu with all major browsers supported.
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Affiliation(s)
- Sanjay B Agravat
- National Center For Functional Glycomics, Emory University School of Medicine, Atlanta, GA 30322, USA, Department of Mathematics and Computer Science, Emory University, Atlanta, GA 30322 and Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY 11794, USA National Center For Functional Glycomics, Emory University School of Medicine, Atlanta, GA 30322, USA, Department of Mathematics and Computer Science, Emory University, Atlanta, GA 30322 and Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY 11794, USA
| | - Joel H Saltz
- National Center For Functional Glycomics, Emory University School of Medicine, Atlanta, GA 30322, USA, Department of Mathematics and Computer Science, Emory University, Atlanta, GA 30322 and Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY 11794, USA National Center For Functional Glycomics, Emory University School of Medicine, Atlanta, GA 30322, USA, Department of Mathematics and Computer Science, Emory University, Atlanta, GA 30322 and Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY 11794, USA
| | - Richard D Cummings
- National Center For Functional Glycomics, Emory University School of Medicine, Atlanta, GA 30322, USA, Department of Mathematics and Computer Science, Emory University, Atlanta, GA 30322 and Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY 11794, USA
| | - David F Smith
- National Center For Functional Glycomics, Emory University School of Medicine, Atlanta, GA 30322, USA, Department of Mathematics and Computer Science, Emory University, Atlanta, GA 30322 and Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY 11794, USA
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Gotz L, Abrahams JL, Mariethoz J, Rudd PM, Karlsson NG, Packer NH, Campbell MP, Lisacek F. GlycoDigest: a tool for the targeted use of exoglycosidase digestions in glycan structure determination. Bioinformatics 2014; 30:3131-3. [PMID: 25015990 DOI: 10.1093/bioinformatics/btu425] [Citation(s) in RCA: 27] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
UNLABELLED Sequencing oligosaccharides by exoglycosidases, either sequentially or in an array format, is a powerful tool to unambiguously determine the structure of complex N- and O-link glycans. Here, we introduce GlycoDigest, a tool that simulates exoglycosidase digestion, based on controlled rules acquired from expert knowledge and experimental evidence available in GlycoBase. The tool allows the targeted design of glycosidase enzyme mixtures by allowing researchers to model the action of exoglycosidases, thereby validating and improving the efficiency and accuracy of glycan analysis. AVAILABILITY AND IMPLEMENTATION http://www.glycodigest.org.
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Affiliation(s)
- Lou Gotz
- Proteome Informatics Group, Swiss Institute of Bioinformatics, 1211 Geneva, Switzerland, Biomolecular Frontiers Research Centre, Macquarie University, North Ryde, NSW 2109, Australia, National Institute for Bioprocessing Research and Training, GlycoScience Group, Dublin, Ireland, Department of Medical Biochemistry and Cell Biology, University of Gothenburg, 40530 Gothenburg, Sweden and Section of Biology, University of Geneva, 1211 Geneva, Switzerland
| | - Jodie L Abrahams
- Proteome Informatics Group, Swiss Institute of Bioinformatics, 1211 Geneva, Switzerland, Biomolecular Frontiers Research Centre, Macquarie University, North Ryde, NSW 2109, Australia, National Institute for Bioprocessing Research and Training, GlycoScience Group, Dublin, Ireland, Department of Medical Biochemistry and Cell Biology, University of Gothenburg, 40530 Gothenburg, Sweden and Section of Biology, University of Geneva, 1211 Geneva, Switzerland
| | - Julien Mariethoz
- Proteome Informatics Group, Swiss Institute of Bioinformatics, 1211 Geneva, Switzerland, Biomolecular Frontiers Research Centre, Macquarie University, North Ryde, NSW 2109, Australia, National Institute for Bioprocessing Research and Training, GlycoScience Group, Dublin, Ireland, Department of Medical Biochemistry and Cell Biology, University of Gothenburg, 40530 Gothenburg, Sweden and Section of Biology, University of Geneva, 1211 Geneva, Switzerland
| | - Pauline M Rudd
- Proteome Informatics Group, Swiss Institute of Bioinformatics, 1211 Geneva, Switzerland, Biomolecular Frontiers Research Centre, Macquarie University, North Ryde, NSW 2109, Australia, National Institute for Bioprocessing Research and Training, GlycoScience Group, Dublin, Ireland, Department of Medical Biochemistry and Cell Biology, University of Gothenburg, 40530 Gothenburg, Sweden and Section of Biology, University of Geneva, 1211 Geneva, Switzerland
| | - Niclas G Karlsson
- Proteome Informatics Group, Swiss Institute of Bioinformatics, 1211 Geneva, Switzerland, Biomolecular Frontiers Research Centre, Macquarie University, North Ryde, NSW 2109, Australia, National Institute for Bioprocessing Research and Training, GlycoScience Group, Dublin, Ireland, Department of Medical Biochemistry and Cell Biology, University of Gothenburg, 40530 Gothenburg, Sweden and Section of Biology, University of Geneva, 1211 Geneva, Switzerland
| | - Nicolle H Packer
- Proteome Informatics Group, Swiss Institute of Bioinformatics, 1211 Geneva, Switzerland, Biomolecular Frontiers Research Centre, Macquarie University, North Ryde, NSW 2109, Australia, National Institute for Bioprocessing Research and Training, GlycoScience Group, Dublin, Ireland, Department of Medical Biochemistry and Cell Biology, University of Gothenburg, 40530 Gothenburg, Sweden and Section of Biology, University of Geneva, 1211 Geneva, Switzerland
| | - Matthew P Campbell
- Proteome Informatics Group, Swiss Institute of Bioinformatics, 1211 Geneva, Switzerland, Biomolecular Frontiers Research Centre, Macquarie University, North Ryde, NSW 2109, Australia, National Institute for Bioprocessing Research and Training, GlycoScience Group, Dublin, Ireland, Department of Medical Biochemistry and Cell Biology, University of Gothenburg, 40530 Gothenburg, Sweden and Section of Biology, University of Geneva, 1211 Geneva, Switzerland
| | - Frederique Lisacek
- Proteome Informatics Group, Swiss Institute of Bioinformatics, 1211 Geneva, Switzerland, Biomolecular Frontiers Research Centre, Macquarie University, North Ryde, NSW 2109, Australia, National Institute for Bioprocessing Research and Training, GlycoScience Group, Dublin, Ireland, Department of Medical Biochemistry and Cell Biology, University of Gothenburg, 40530 Gothenburg, Sweden and Section of Biology, University of Geneva, 1211 Geneva, Switzerland Proteome Informatics Group, Swiss Institute of Bioinformatics, 1211 Geneva, Switzerland, Biomolecular Frontiers Research Centre, Macquarie University, North Ryde, NSW 2109, Australia, National Institute for Bioprocessing Research and Training, GlycoScience Group, Dublin, Ireland, Department of Medical Biochemistry and Cell Biology, University of Gothenburg, 40530 Gothenburg, Sweden and Section of Biology, University of Geneva, 1211 Geneva, Switzerland
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Romano P, Lisacek F, Masseroli M. NETTAB 2012 on "Integrated Bio-Search". BMC Bioinformatics 2014; 15 Suppl 1:S1. [PMID: 24564635 PMCID: PMC4015131 DOI: 10.1186/1471-2105-15-s1-s1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/02/2022] Open
Abstract
The NETTAB 2012 workshop, held in Como on November 14-16, 2012, was devoted to "Integrated Bio-Search", that is to technologies, methods, architectures, systems and applications for searching, retrieving, integrating and analyzing data, information, and knowledge with the aim of answering complex bio-medical-molecular questions, i.e. some of the most challenging issues in bioinformatics today. It brought together about 80 researchers working in the field of Bioinformatics, Computational Biology, Biology, Computer Science and Engineering. More than 50 scientific contributions, including keynote and tutorial talks, oral communications, posters and software demonstrations, were presented at the workshop. This preface provides a brief overview of the workshop and shortly introduces the peer-reviewed manuscripts that were accepted for publication in this Supplement.
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Affiliation(s)
- Paolo Romano
- Bioinformatics, IRCCS AOU San Martino - IST National Cancer Research Institute, Genoa, I-16132, Italy
| | - Frédérique Lisacek
- Proteome Informatics Group, SIB Swiss Institute of Bioinformatics, 1211 Geneva 4, Switzerland
- Section of Biology, University of Geneva, 1211 Geneva 4, Switzerland
| | - Marco Masseroli
- Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Milano, I-20133, Italy
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Campbell MP, Peterson R, Mariethoz J, Gasteiger E, Akune Y, Aoki-Kinoshita KF, Lisacek F, Packer NH. UniCarbKB: building a knowledge platform for glycoproteomics. Nucleic Acids Res 2013; 42:D215-21. [PMID: 24234447 PMCID: PMC3964942 DOI: 10.1093/nar/gkt1128] [Citation(s) in RCA: 129] [Impact Index Per Article: 11.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
The UniCarb KnowledgeBase (UniCarbKB; http://unicarbkb.org) offers public access to a growing, curated database of information on the glycan structures of glycoproteins. UniCarbKB is an international effort that aims to further our understanding of structures, pathways and networks involved in glycosylation and glyco-mediated processes by integrating structural, experimental and functional glycoscience information. This initiative builds upon the success of the glycan structure database GlycoSuiteDB, together with the informatic standards introduced by EUROCarbDB, to provide a high-quality and updated resource to support glycomics and glycoproteomics research. UniCarbKB provides comprehensive information concerning glycan structures, and published glycoprotein information including global and site-specific attachment information. For the first release over 890 references, 3740 glycan structure entries and 400 glycoproteins have been curated. Further, 598 protein glycosylation sites have been annotated with experimentally confirmed glycan structures from the literature. Among these are 35 glycoproteins, 502 structures and 60 publications previously not included in GlycoSuiteDB. This article provides an update on the transformation of GlycoSuiteDB (featured in previous NAR Database issues and hosted by ExPASy since 2009) to UniCarbKB and its integration with UniProtKB and GlycoMod. Here, we introduce a refactored database, supported by substantial new curated data collections and intuitive user-interfaces that improve database searching.
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Affiliation(s)
- Matthew P Campbell
- Biomolecular Frontiers Research Centre, Macquarie University, North Ryde, NSW 2109, Australia, Proteome Informatics Group, Swiss Institute of Bioinformatics, Geneva, Switzerland, Swiss-Prot Group, Swiss Institute of Bioinformatics, Geneva, Switzerland, Department of Bioinformatics, Faculty of Engineering, Soka University, 1-236 Tangi-machi, Hachioji, Tokyo, Japan and Section of Biology, Faculty of Sciences, University of Geneva, Switzerland
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