1
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Toukach P. Carbohydrate Structure Database: current state and recent developments. Anal Bioanal Chem 2024:10.1007/s00216-024-05383-w. [PMID: 38914734 DOI: 10.1007/s00216-024-05383-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2024] [Revised: 05/18/2024] [Accepted: 05/28/2024] [Indexed: 06/26/2024]
Abstract
Carbohydrate Structure Database (CSDB) is a curated glycan data collection and a glycoinformatic platform. In this report, its database, analytical, and other components that have appeared for the recent years are reviewed. The major improvements were achieving close-to-full coverage on glycans from microorganisms, launching modules for glycosyltransferases and saccharide conformations, online glycan builder and 3D modeler, NMR simulator, NMR-based structure predictor, and other tools.
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Affiliation(s)
- Philip Toukach
- N.D. Zelinsky Institute of Organic Chemistry, Russian Academy of Sciences, Moscow, Russia.
- Faculty of Chemistry, National Research University Higher School of Economics, Moscow, Russia.
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2
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Schnider B, M’Rad Y, el Ahmadie J, de Brevern AG, Imberty A, Lisacek F. HumanLectome, an update of UniLectin for the annotation and prediction of human lectins. Nucleic Acids Res 2024; 52:D1683-D1693. [PMID: 37889052 PMCID: PMC10767822 DOI: 10.1093/nar/gkad905] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2023] [Revised: 10/03/2023] [Accepted: 10/06/2023] [Indexed: 10/28/2023] Open
Abstract
The UniLectin portal (https://unilectin.unige.ch/) was designed in 2019 with the goal of centralising curated and predicted data on carbohydrate-binding proteins known as lectins. UniLectin is also intended as a support for the study of lectomes (full lectin set) of organisms or tissues. The present update describes the inclusion of several new modules and details the latest (https://unilectin.unige.ch/humanLectome/), covering our knowledge of the human lectome and comprising 215 unevenly characterised lectins, particularly in terms of structural information. Each HumanLectome entry is protein-centric and compiles evidence of carbohydrate recognition domain(s), specificity, 3D-structure, tissue-based expression and related genomic data. Other recent improvements regarding interoperability and accessibility are outlined.
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Affiliation(s)
- Boris Schnider
- Proteome Informatics Group, SIB Swiss Institute of Bioinformatics, CH-1211 Geneva, Switzerland
- Computer Science Department, University of Geneva, CH-1227 Geneva, Switzerland
| | - Yacine M’Rad
- Proteome Informatics Group, SIB Swiss Institute of Bioinformatics, CH-1211 Geneva, Switzerland
- Computer Science Department, University of Geneva, CH-1227 Geneva, Switzerland
| | - Jalaa el Ahmadie
- Proteome Informatics Group, SIB Swiss Institute of Bioinformatics, CH-1211 Geneva, Switzerland
- Computer Science Department, University of Geneva, CH-1227 Geneva, Switzerland
- University Grenoble Alpes, CNRS, CERMAV, F-38000 Grenoble, France
| | - Alexandre G de Brevern
- Université Paris Cité and Université de la Réunion and Université des Antilles, INSERM, BIGR, DSIMB Bioinformatics Team, F-75014 Paris, France
| | - Anne Imberty
- University Grenoble Alpes, CNRS, CERMAV, F-38000 Grenoble, France
| | - Frederique Lisacek
- Proteome Informatics Group, SIB Swiss Institute of Bioinformatics, CH-1211 Geneva, Switzerland
- Computer Science Department, University of Geneva, CH-1227 Geneva, Switzerland
- Section of Biology, University of Geneva, CH-1205 Geneva, Switzerland
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3
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Jin C, Venkatakrishnan V, Thomsson KA, Aoki NP, Shinmachi D, Aoki-Kinoshita KF, Hayes CA, Lisacek F, Karlsson NG. UniCarb-DB: An MS/MS Experimental Glycomic Fragmentation Database. Methods Mol Biol 2024; 2836:77-96. [PMID: 38995537 DOI: 10.1007/978-1-0716-4007-4_6] [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: 07/13/2024]
Abstract
Glycosylation is a unique posttranslational modification that dynamically shapes the surface of cells. Glycans attached to proteins or lipids in a cell or tissue are studied as a whole and collectively designated as a glycome. UniCarb-DB is a glycomic spectral library of tandem mass spectrometry (MS/MS) fragment data. The current version of the database consists of over 1500 entries and over 1000 unique structures. Each entry contains parent ion information with associated MS/MS spectra, metadata about the original publication, experimental conditions, and biological origin. Each structure is also associated with the GlyTouCan glycan structure repository allowing easy access to other glycomic resources. The database can be directly utilized by mass spectrometry (MS) experimentalists through the conversion of data generated by MS into structural information. Flexible online search tools along with a downloadable version of the database are easily incorporated in either commercial or open-access MS software. This chapter highlights UniCarb-DB online search tool to browse differences of isomeric structures between spectra, a peak matching search between user-generated MS/MS spectra and spectra stored in UniCarb-DB and more advanced MS tools for combined quantitative and qualitative glycomics.
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Affiliation(s)
- Chunsheng Jin
- Proteomics Core Facility at Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | | | - Kristina A Thomsson
- Proteomics Core Facility at Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Nobuyuki P Aoki
- Soka University, Hachioji, Tokyo, Japan
- SparqLite.com, Hachioji, Tokyo, Japan
| | | | | | - Catherine A Hayes
- 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
| | - Frédérique Lisacek
- Proteome Informatics Group, SIB Swiss Institute of Bioinformatics, Geneva, Switzerland
| | - Niclas G Karlsson
- Proteomics Core Facility at Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.
- Division of Pharmacy, Department of Life Science and Health, Faculty of Health Science, Oslo Metropolitan University, Oslo, Norway.
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4
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Ritsch M, Cassman NA, Saghaei S, Marz M. Navigating the Landscape: A Comprehensive Review of Current Virus Databases. Viruses 2023; 15:1834. [PMID: 37766241 PMCID: PMC10537806 DOI: 10.3390/v15091834] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2023] [Revised: 08/18/2023] [Accepted: 08/21/2023] [Indexed: 09/29/2023] Open
Abstract
Viruses are abundant and diverse entities that have important roles in public health, ecology, and agriculture. The identification and surveillance of viruses rely on an understanding of their genome organization, sequences, and replication strategy. Despite technological advancements in sequencing methods, our current understanding of virus diversity remains incomplete, highlighting the need to explore undiscovered viruses. Virus databases play a crucial role in providing access to sequences, annotations and other metadata, and analysis tools for studying viruses. However, there has not been a comprehensive review of virus databases in the last five years. This study aimed to fill this gap by identifying 24 active virus databases and included an extensive evaluation of their content, functionality and compliance with the FAIR principles. In this study, we thoroughly assessed the search capabilities of five database catalogs, which serve as comprehensive repositories housing a diverse array of databases and offering essential metadata. Moreover, we conducted a comprehensive review of different types of errors, encompassing taxonomy, names, missing information, sequences, sequence orientation, and chimeric sequences, with the intention of empowering users to effectively tackle these challenges. We expect this review to aid users in selecting suitable virus databases and other resources, and to help databases in error management and improve their adherence to the FAIR principles. The databases listed here represent the current knowledge of viruses and will help aid users find databases of interest based on content, functionality, and scope. The use of virus databases is integral to gaining new insights into the biology, evolution, and transmission of viruses, and developing new strategies to manage virus outbreaks and preserve global health.
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Affiliation(s)
- Muriel Ritsch
- RNA Bioinformatics and High-Throughput Analysis, Friedrich Schiller University Jena, 07743 Jena, Germany;
- European Virus Bioinformatics Center, 07743 Jena, Germany
| | - Noriko A. Cassman
- RNA Bioinformatics and High-Throughput Analysis, Friedrich Schiller University Jena, 07743 Jena, Germany;
- European Virus Bioinformatics Center, 07743 Jena, Germany
| | - Shahram Saghaei
- RNA Bioinformatics and High-Throughput Analysis, Friedrich Schiller University Jena, 07743 Jena, Germany;
- European Virus Bioinformatics Center, 07743 Jena, Germany
| | - Manja Marz
- RNA Bioinformatics and High-Throughput Analysis, Friedrich Schiller University Jena, 07743 Jena, Germany;
- European Virus Bioinformatics Center, 07743 Jena, Germany
- German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, 04103 Leipzig, Germany
- FLI Leibniz Institute for Age Research, 07745 Jena, Germany
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5
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Perez S, Makshakova O, Angulo J, Bedini E, Bisio A, de Paz JL, Fadda E, Guerrini M, Hricovini M, Hricovini M, Lisacek F, Nieto PM, Pagel K, Paiardi G, Richter R, Samsonov SA, Vivès RR, Nikitovic D, Ricard Blum S. Glycosaminoglycans: What Remains To Be Deciphered? JACS AU 2023; 3:628-656. [PMID: 37006755 PMCID: PMC10052243 DOI: 10.1021/jacsau.2c00569] [Citation(s) in RCA: 22] [Impact Index Per Article: 22.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/15/2022] [Revised: 12/05/2022] [Accepted: 12/07/2022] [Indexed: 06/19/2023]
Abstract
Glycosaminoglycans (GAGs) are complex polysaccharides exhibiting a vast structural diversity and fulfilling various functions mediated by thousands of interactions in the extracellular matrix, at the cell surface, and within the cells where they have been detected in the nucleus. It is known that the chemical groups attached to GAGs and GAG conformations comprise "glycocodes" that are not yet fully deciphered. The molecular context also matters for GAG structures and functions, and the influence of the structure and functions of the proteoglycan core proteins on sulfated GAGs and vice versa warrants further investigation. The lack of dedicated bioinformatic tools for mining GAG data sets contributes to a partial characterization of the structural and functional landscape and interactions of GAGs. These pending issues will benefit from the development of new approaches reviewed here, namely (i) the synthesis of GAG oligosaccharides to build large and diverse GAG libraries, (ii) GAG analysis and sequencing by mass spectrometry (e.g., ion mobility-mass spectrometry), gas-phase infrared spectroscopy, recognition tunnelling nanopores, and molecular modeling to identify bioactive GAG sequences, biophysical methods to investigate binding interfaces, and to expand our knowledge and understanding of glycocodes governing GAG molecular recognition, and (iii) artificial intelligence for in-depth investigation of GAGomic data sets and their integration with proteomics.
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Affiliation(s)
- Serge Perez
- Centre
de Recherche sur les Macromolecules, Vegetales,
University of Grenoble-Alpes, Centre National de la Recherche Scientifique, Grenoble F-38041 France
| | - Olga Makshakova
- FRC
Kazan Scientific Center of Russian Academy of Sciences, Kazan Institute of Biochemistry and Biophysics, Kazan 420111, Russia
| | - Jesus Angulo
- Insituto
de Investigaciones Quimicas, CIC Cartuja, CSIC and Universidad de Sevilla, Sevilla, SP 41092, Spain
| | - Emiliano Bedini
- Department
of Chemical Sciences, University of Naples
Federico II, Naples,I-80126, Italy
| | - Antonella Bisio
- Istituto
di Richerche Chimiche e Biochimiche, G. Ronzoni, Milan I-20133, Italy
| | - Jose Luis de Paz
- Insituto
de Investigaciones Quimicas, CIC Cartuja, CSIC and Universidad de Sevilla, Sevilla, SP 41092, Spain
| | - Elisa Fadda
- Department
of Chemistry and Hamilton Institute, Maynooth
University, Maynooth W23 F2H6, Ireland
| | - Marco Guerrini
- Istituto
di Richerche Chimiche e Biochimiche, G. Ronzoni, Milan I-20133, Italy
| | - Michal Hricovini
- Institute
of Chemistry, Slovak Academy of Sciences, Bratislava SK-845 38, Slovakia
| | - Milos Hricovini
- Institute
of Chemistry, Slovak Academy of Sciences, Bratislava SK-845 38, Slovakia
| | - Frederique Lisacek
- Computer
Science Department & Section of Biology, University of Geneva & Swiss Institue of Bioinformatics, Geneva CH-1227, Switzerland
| | - Pedro M. Nieto
- Insituto
de Investigaciones Quimicas, CIC Cartuja, CSIC and Universidad de Sevilla, Sevilla, SP 41092, Spain
| | - Kevin Pagel
- Institut
für Chemie und Biochemie Organische Chemie, Freie Universität Berlin, Berlin 14195, Germany
| | - Giulia Paiardi
- Molecular
and Cellular Modeling Group, Heidelberg Institute for Theoretical
Studies, Heidelberg University, Heidelberg 69118, Germany
| | - Ralf Richter
- School
of Biomedical Sciences, Faculty of Biological Sciences, School of
Physics and Astronomy, Faculty of Engineering and Physical Sciences,
Astbury Centre for Structural Molecular Biology and Bragg Centre for
Materials Research, University of Leeds, Leeds LS2 9JT, United Kingdom
| | - Sergey A. Samsonov
- Department
of Theoretical Chemistry, Faculty of Chemistry, University of Gdansk, Gdsank 80-309, Poland
| | - Romain R. Vivès
- Univ.
Grenoble Alpes, CNRS, CEA, IBS, Grenoble F-38044, France
| | - Dragana Nikitovic
- School
of Histology-Embriology, Medical School, University of Crete, Heraklion 71003, Greece
| | - Sylvie Ricard Blum
- University
Claude Bernard Lyon 1, CNRS, INSA Lyon, CPE, Institute of Molecular and Supramolecular Chemistry and Biochemistry,
UMR 5246, Villeurbanne F 69622 Cedex, France
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6
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Effect of Dexamethasone on the Expression of the α2,3 and α2,6 Sialic Acids in Epithelial Cell Lines. Pathogens 2022; 11:pathogens11121518. [PMID: 36558852 PMCID: PMC9788320 DOI: 10.3390/pathogens11121518] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2022] [Revised: 11/22/2022] [Accepted: 11/25/2022] [Indexed: 12/14/2022] Open
Abstract
N-acetylneuraminic acid linked to galactose by α2,6 and α2,3 linkages (Siaα2,6 and Siaα2,3) is expressed on glycoconjugates of animal tissues, where it performs multiple biological functions. In addition, these types of sialic acid residues are the main targets for the binding and entry of influenza viruses. Here we used fluorochrome-conjugated Sambuccus nigra, Maackia amurensis, and peanut lectins for the simultaneous detection of Siaα2,3 and Siaα2,6 and galactosyl residues by two-color flow cytometry on A549 cells, a human pneumocyte cell line used for in vitro studies of the infection by influenza viruses, as well as on Vero and MDCK cell lines. The dexamethasone (DEX) glucocorticoid (GC), a widely used anti-inflammatory compound, completely abrogated the expression of Siaα2,3 in A549 cells and decreased its expression in Vero and MDCK cells; in contrast, the expression of Siaα2,6 was increased in the three cell lines. These observations indicate that DEX can be used for the study of the mechanism of sialylation of cell membrane molecules. Importantly, DEX may change the tropism of avian and human/pig influenza viruses and other infectious agents to animal and human epithelial cells.
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7
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Li H, Chiang AWT, Lewis NE. Artificial intelligence in the analysis of glycosylation data. Biotechnol Adv 2022; 60:108008. [PMID: 35738510 PMCID: PMC11157671 DOI: 10.1016/j.biotechadv.2022.108008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2022] [Revised: 06/15/2022] [Accepted: 06/16/2022] [Indexed: 11/18/2022]
Abstract
Glycans are complex, yet ubiquitous across biological systems. They are involved in diverse essential organismal functions. Aberrant glycosylation may lead to disease development, such as cancer, autoimmune diseases, and inflammatory diseases. Glycans, both normal and aberrant, are synthesized using extensive glycosylation machinery, and understanding this machinery can provide invaluable insights for diagnosis, prognosis, and treatment of various diseases. Increasing amounts of glycomics data are being generated thanks to advances in glycoanalytics technologies, but to maximize the value of such data, innovations are needed for analyzing and interpreting large-scale glycomics data. Artificial intelligence (AI) provides a powerful analysis toolbox in many scientific fields, and here we review state-of-the-art AI approaches on glycosylation analysis. We further discuss how models can be analyzed to gain mechanistic insights into glycosylation machinery and how the machinery shapes glycans under different scenarios. Finally, we propose how to leverage the gained knowledge for developing predictive AI-based models of glycosylation. Thus, guiding future research of AI-based glycosylation model development will provide valuable insights into glycosylation and glycan machinery.
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Affiliation(s)
- Haining Li
- Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093, USA
| | - Austin W T Chiang
- Department of Pediatrics, University of California, San Diego, La Jolla, CA 92093, USA.
| | - Nathan E Lewis
- Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093, USA; Department of Pediatrics, University of California, San Diego, La Jolla, CA 92093, USA.
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8
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Park JY, Kim CH, Cho SH. Glycan-Adhering Lectins and Experimental Evaluation of a Lectin FimH Inhibitor in Enterohemorrhagic Escherichia coli (EHEC) O157:H7 Strain EDL933. Int J Mol Sci 2022; 23:ijms23179931. [PMID: 36077327 PMCID: PMC9455959 DOI: 10.3390/ijms23179931] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2022] [Revised: 08/30/2022] [Accepted: 08/30/2022] [Indexed: 11/16/2022] Open
Abstract
In this study, we tried to develop a FimH inhibitor that inhibits adhesion of enterohemorrhagic Escherichia coli (EHEC) on the epithelium of human intestine during the initial stage of infections. Using a T7 phage display method with a reference strain, EHEC EDL933, FimH was selected as an adherent lectin to GM1a and Gb3 glycans. In order to detect the ligand binding domain (LBD) of FimH, we used a docking simulation and found three binding site sequences of FimH, i.e., P1, P2, and P3. Among Gb3 mimic peptides, P2 was found to have the strongest binding strength. Moreover, in vitro treatment with peptide P2 inhibited binding activity in a concentration-dependent manner. Furthermore, we conducted confirmation experiments through several strains isolated from patients in Korea, EHEC NCCP15736, NCCP15737, and NCCP15739. In addition, we analyzed the evolutionary characteristics of the predicted FimH lectin-like adhesins to construct a lectin-glycan interaction (LGI). We selected 70 recently differentiated strains from the phylogenetic tree of 2240 strains with Shiga toxin in their genome. We can infer EHEC strains dynamically evolved but FimH was conserved during the evolution time according to the phylogenetic tree. Furthermore, FimH could be a reliable candidate of drug target in terms of evolution. We examined how pathogen lectins interact with host glycans early in infection in EDL933 as well as several field strains and confirmed that glycan-like peptides worked as an initial infection inhibitor.
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Affiliation(s)
- Jun-Young Park
- Division of Zoonotic and Vector Borne Disease Research, Center for Infectious Disease Research, Korea National Institute of Health, Cheongju 28159, Korea
| | - Cheorl-Ho Kim
- Glycobiology Unit, Department of Biological Science, Sung Kyunkwan University and Samsung Advanced Institute for Health Science and Technology (SAIHST), Suwon 16419, Korea
| | - Seung-Hak Cho
- Division of Zoonotic and Vector Borne Disease Research, Center for Infectious Disease Research, Korea National Institute of Health, Cheongju 28159, Korea
- Correspondence: ; Tel.: +82-43-913-4899
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9
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Ramos-Martínez IE, Ramos-Martínez E, Segura-Velázquez RÁ, Saavedra-Montañez M, Cervantes-Torres JB, Cerbón M, Papy-Garcia D, Zenteno E, Sánchez-Betancourt JI. Heparan Sulfate and Sialic Acid in Viral Attachment: Two Sides of the Same Coin? Int J Mol Sci 2022; 23:ijms23179842. [PMID: 36077240 PMCID: PMC9456526 DOI: 10.3390/ijms23179842] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2022] [Revised: 08/26/2022] [Accepted: 08/27/2022] [Indexed: 12/11/2022] Open
Abstract
Sialic acids and heparan sulfates make up the outermost part of the cell membrane and the extracellular matrix. Both structures are characterized by being negatively charged, serving as receptors for various pathogens, and are highly expressed in the respiratory and digestive tracts. Numerous viruses use heparan sulfates as receptors to infect cells; in this group are HSV, HPV, and SARS-CoV-2. Other viruses require the cell to express sialic acids, as is the case in influenza A viruses and adenoviruses. This review aims to present, in a general way, the participation of glycoconjugates in viral entry, and therapeutic strategies focused on inhibiting the interaction between the virus and the glycoconjugates. Interestingly, there are few studies that suggest the participation of both glycoconjugates in the viruses addressed here. Considering the biological redundancy that exists between heparan sulfates and sialic acids, we propose that it is important to jointly evaluate and design strategies that contemplate inhibiting the interactions of both glycoconjugates. This approach will allow identifying new receptors and lead to a deeper understanding of interspecies transmission.
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Affiliation(s)
- Ivan Emmanuel Ramos-Martínez
- Departamento de Medicina y Zootecnia de Cerdos, Facultad de Medicina Veterinaria y Zootecnia, Universidad Nacional Autónoma de México, Ciudad de México 04510, Mexico
| | - Edgar Ramos-Martínez
- Facultad de Química, Universidad Nacional Autónoma de México, Ciudad de México 04510, Mexico
| | - René Álvaro Segura-Velázquez
- Unidad de Investigación, Facultad de Medicina Veterinaria y Zootecnia, Universidad Nacional Autónoma de México, Ciudad de México 04510, Mexico
| | - Manuel Saavedra-Montañez
- Departamento de Microbiología e Inmunología, Facultad de Medicina Veterinaria y Zootecnia, Universidad Nacional Autónoma de México, Ciudad de México 04510, Mexico
| | - Jacquelynne Brenda Cervantes-Torres
- Departamento de Microbiología e Inmunología, Facultad de Medicina Veterinaria y Zootecnia, Universidad Nacional Autónoma de México, Ciudad de México 04510, Mexico
| | - Marco Cerbón
- Unidad de Investigación en Reproducción Humana, Instituto Nacional de Perinatología-Facultad de Química, Universidad Nacional Autónoma de México, Ciudad de México 04510, Mexico
| | - Dulce Papy-Garcia
- Glycobiology, Cell Growth ant Tissue Repair Research Unit (Gly-CRRET), Université Paris Est Créteil (UPEC), F-94010 Créteil, France
| | - Edgar Zenteno
- Departamento de Bioquímica, Facultad de Medicina, Universidad Nacional Autónoma de México, Ciudad de México 04510, Mexico
| | - José Ivan Sánchez-Betancourt
- Departamento de Medicina y Zootecnia de Cerdos, Facultad de Medicina Veterinaria y Zootecnia, Universidad Nacional Autónoma de México, Ciudad de México 04510, Mexico
- Correspondence:
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10
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Systemic Lectin-Glycan Interaction of Pathogenic Enteric Bacteria in the Gastrointestinal Tract. Int J Mol Sci 2022; 23:ijms23031451. [PMID: 35163392 PMCID: PMC8835900 DOI: 10.3390/ijms23031451] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2021] [Revised: 01/20/2022] [Accepted: 01/25/2022] [Indexed: 01/27/2023] Open
Abstract
Microorganisms, such as bacteria, viruses, and fungi, and host cells, such as plants and animals, have carbohydrate chains and lectins that reciprocally recognize one another. In hosts, the defense system is activated upon non-self-pattern recognition of microbial pathogen-associated molecular patterns. These are present in Gram-negative and Gram-positive bacteria and fungi. Glycan-based PAMPs are bound to a class of lectins that are widely distributed among eukaryotes. The first step of bacterial infection in humans is the adhesion of the pathogen's lectin-like proteins to the outer membrane surfaces of host cells, which are composed of glycans. Microbes and hosts binding to each other specifically is of critical importance. The adhesion factors used between pathogens and hosts remain unknown; therefore, research is needed to identify these factors to prevent intestinal infection or treat it in its early stages. This review aims to present a vision for the prevention and treatment of infectious diseases by identifying the role of the host glycans in the immune response against pathogenic intestinal bacteria through studies on the lectin-glycan interaction.
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11
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Dealing with the Ambiguity of Glycan Substructure Search. MOLECULES (BASEL, SWITZERLAND) 2021; 27:molecules27010065. [PMID: 35011294 PMCID: PMC8746581 DOI: 10.3390/molecules27010065] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/02/2021] [Revised: 12/17/2021] [Accepted: 12/17/2021] [Indexed: 01/15/2023]
Abstract
The level of ambiguity in describing glycan structure has significantly increased with the upsurge of large-scale glycomics and glycoproteomics experiments. Consequently, an ontology-based model appears as an appropriate solution for navigating these data. However, navigation is not sufficient and the model should also enable advanced search and comparison. A new ontology with a tree logical structure is introduced to represent glycan structures irrespective of the precision of molecular details. The model heavily relies on the GlycoCT encoding of glycan structures. Its implementation in the GlySTreeM knowledge base was validated with GlyConnect data and benchmarked with the Glycowork library. GlySTreeM is shown to be fast, consistent, reliable and more flexible than existing solutions for matching parts of or whole glycan structures. The model is also well suited for painless future expansion.
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12
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Imberty A, Bonnardel F, Lisacek F. UniLectin, A One-Stop-Shop to Explore and Study Carbohydrate-Binding Proteins. Curr Protoc 2021; 1:e305. [PMID: 34826352 DOI: 10.1002/cpz1.305] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
All eukaryotic cells are covered with a dense layer of glycoconjugates, and the cell walls of bacteria are made of various polysaccharides, putting glycans in key locations for mediating protein-protein interactions at cell interfaces. Glycan function is therefore mainly defined as binding to other molecules, and lectins are proteins that specifically recognize and interact non-covalently with glycans. UniLectin was designed based on insight into the knowledge of lectins, their classification, and their biological role. This modular platform provides a curated and periodically updated classification of lectins along with a set of comparative and visualization tools, as well as structured results of screening comprehensive sequence datasets. UniLectin can be used to explore lectins, find precise information on glycan-protein interactions, and mine the results of predictive tools based on HMM profiles. This usage is illustrated here with two protocols. The first one highlights the fine-tuned role of the O blood group antigen in distinctive pathogen recognition, while the second compares the various bacterial lectin arsenals that clearly depend on living conditions of species even in the same genus. © 2021 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Searching for the structural details of lectins binding the O blood group antigen Basic Protocol 2: Comparing the lectomes of related organisms in different environments.
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Affiliation(s)
- Anne Imberty
- Université Grenoble Alpes, CNRS, CERMAV, Grenoble, France
| | - François Bonnardel
- Université Grenoble Alpes, CNRS, CERMAV, Grenoble, France.,SIB Swiss Institute of Bioinformatics, Geneva, Switzerland.,Computer Science Department, UniGe, Geneva, Switzerland
| | - Frédérique Lisacek
- SIB Swiss Institute of Bioinformatics, Geneva, Switzerland.,Computer Science Department, UniGe, Geneva, Switzerland.,Section of Biology, UniGe, Geneva, Switzerland
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13
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Glycoinformatics Tools for Comprehensive Characterization of Glycans Enzymatically Released from Proteins. Methods Mol Biol 2021. [PMID: 34611862 DOI: 10.1007/978-1-0716-1685-7_1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register]
Abstract
Glycosylation is important in biology, contributing to both protein conformation and function. Structurally, glycosylation is complex and diverse. This complexity is reflected in the topology, composition, monosaccharide linkages, and isomerism of each oligosaccharide. Glycoanalytics is a discipline that addresses the understanding and characterization of this complexity and its correlation with biology. It includes analytical steps such as sample preparation, instrument measurements, and data analyses. Of these, data analysis has emerged as a critical bottleneck because data collection has increasingly become high-throughput. This has resulted in data-rich workflows that lack rapid and automated data analytics. To address this issue, the field has been developing software for interpretation of quantitative glycomics studies. Here, we describe a protocol using available informatics tools for analysis of data from analysis of released glycans using high-/ultraperformance liquid chromatography (H/UPLC) coupled with mass spectrometry (MS).
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14
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Proteome-wide prediction of bacterial carbohydrate-binding proteins as a tool for understanding commensal and pathogen colonisation of the vaginal microbiome. NPJ Biofilms Microbiomes 2021; 7:49. [PMID: 34131152 PMCID: PMC8206207 DOI: 10.1038/s41522-021-00220-9] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2021] [Accepted: 05/20/2021] [Indexed: 12/16/2022] Open
Abstract
Bacteria use carbohydrate-binding proteins (CBPs), such as lectins and carbohydrate-binding modules (CBMs), to anchor to specific sugars on host surfaces. CBPs in the gut microbiome are well studied, but their roles in the vagina microbiome and involvement in sexually transmitted infections, cervical cancer and preterm birth are largely unknown. We established a classification system for lectins and designed Hidden Markov Model (HMM) profiles for data mining of bacterial genomes, resulting in identification of >100,000 predicted bacterial lectins available at unilectin.eu/bacteria. Genome screening of 90 isolates from 21 vaginal bacterial species shows that those associated with infection and inflammation produce a larger CBPs repertoire, thus enabling them to potentially bind a wider array of glycans in the vagina. Both the number of predicted bacterial CBPs and their specificities correlated with pathogenicity. This study provides new insights into potential mechanisms of colonisation by commensals and potential pathogens of the reproductive tract that underpin health and disease states.
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15
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Sudhakar P, Machiels K, Verstockt B, Korcsmaros T, Vermeire S. Computational Biology and Machine Learning Approaches to Understand Mechanistic Microbiome-Host Interactions. Front Microbiol 2021; 12:618856. [PMID: 34046017 PMCID: PMC8148342 DOI: 10.3389/fmicb.2021.618856] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2020] [Accepted: 03/19/2021] [Indexed: 12/11/2022] Open
Abstract
The microbiome, by virtue of its interactions with the host, is implicated in various host functions including its influence on nutrition and homeostasis. Many chronic diseases such as diabetes, cancer, inflammatory bowel diseases are characterized by a disruption of microbial communities in at least one biological niche/organ system. Various molecular mechanisms between microbial and host components such as proteins, RNAs, metabolites have recently been identified, thus filling many gaps in our understanding of how the microbiome modulates host processes. Concurrently, high-throughput technologies have enabled the profiling of heterogeneous datasets capturing community level changes in the microbiome as well as the host responses. However, due to limitations in parallel sampling and analytical procedures, big gaps still exist in terms of how the microbiome mechanistically influences host functions at a system and community level. In the past decade, computational biology and machine learning methodologies have been developed with the aim of filling the existing gaps. Due to the agnostic nature of the tools, they have been applied in diverse disease contexts to analyze and infer the interactions between the microbiome and host molecular components. Some of these approaches allow the identification and analysis of affected downstream host processes. Most of the tools statistically or mechanistically integrate different types of -omic and meta -omic datasets followed by functional/biological interpretation. In this review, we provide an overview of the landscape of computational approaches for investigating mechanistic interactions between individual microbes/microbiome and the host and the opportunities for basic and clinical research. These could include but are not limited to the development of activity- and mechanism-based biomarkers, uncovering mechanisms for therapeutic interventions and generating integrated signatures to stratify patients.
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Affiliation(s)
- Padhmanand Sudhakar
- Department of Chronic Diseases, Metabolism and Ageing, Translational Research Center for Gastrointestinal Disorders (TARGID), KU Leuven, Leuven, Belgium
- Earlham Institute, Norwich, United Kingdom
- Quadram Institute Bioscience, Norwich, United Kingdom
| | - Kathleen Machiels
- Department of Chronic Diseases, Metabolism and Ageing, Translational Research Center for Gastrointestinal Disorders (TARGID), KU Leuven, Leuven, Belgium
| | - Bram Verstockt
- Department of Chronic Diseases, Metabolism and Ageing, Translational Research Center for Gastrointestinal Disorders (TARGID), KU Leuven, Leuven, Belgium
- Department of Gastroenterology and Hepatology, University Hospitals Leuven, KU Leuven, Leuven, Belgium
| | - Tamas Korcsmaros
- Earlham Institute, Norwich, United Kingdom
- Quadram Institute Bioscience, Norwich, United Kingdom
| | - Séverine Vermeire
- Department of Chronic Diseases, Metabolism and Ageing, Translational Research Center for Gastrointestinal Disorders (TARGID), KU Leuven, Leuven, Belgium
- Department of Gastroenterology and Hepatology, University Hospitals Leuven, KU Leuven, Leuven, Belgium
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16
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Kellman BP, Lewis NE. Big-Data Glycomics: Tools to Connect Glycan Biosynthesis to Extracellular Communication. Trends Biochem Sci 2021; 46:284-300. [PMID: 33349503 PMCID: PMC7954846 DOI: 10.1016/j.tibs.2020.10.004] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2020] [Revised: 10/05/2020] [Accepted: 10/22/2020] [Indexed: 12/12/2022]
Abstract
Characteristically, cells must sense and respond to environmental cues. Despite the importance of cell-cell communication, our understanding remains limited and often lacks glycans. Glycans decorate proteins and cell membranes at the cell-environment interface, and modulate intercellular communication, from development to pathogenesis. Providing further challenges, glycan biosynthesis and cellular behavior are co-regulating systems. Here, we discuss how glycosylation contributes to extracellular responses and signaling. We further organize approaches for disentangling the roles of glycans in multicellular interactions using newly available datasets and tools, including glycan biosynthesis models, omics datasets, and systems-level analyses. Thus, emerging tools in big data analytics and systems biology are facilitating novel insights on glycans and their relationship with multicellular behavior.
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Affiliation(s)
- Benjamin P Kellman
- Department of Pediatrics, University of California San Diego School of Medicine, La Jolla, CA, USA; Department of Bioengineering, University of California San Diego School of Medicine, La Jolla, CA, USA; Bioinformatics and Systems Biology Program, University of California San Diego School of Medicine, La Jolla, CA, USA
| | - Nathan E Lewis
- Department of Pediatrics, University of California San Diego School of Medicine, La Jolla, CA, USA; Department of Bioengineering, University of California San Diego School of Medicine, La Jolla, CA, USA; Bioinformatics and Systems Biology Program, University of California San Diego School of Medicine, La Jolla, CA, USA; Novo Nordisk Foundation Center for Biosustainability at the University of California San Diego School of Medicine, La Jolla, CA, USA.
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17
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Bonnardel F, Mariethoz J, Pérez S, Imberty A, Lisacek F. LectomeXplore, an update of UniLectin for the discovery of carbohydrate-binding proteins based on a new lectin classification. Nucleic Acids Res 2021; 49:D1548-D1554. [PMID: 33174598 PMCID: PMC7778903 DOI: 10.1093/nar/gkaa1019] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2020] [Revised: 10/13/2020] [Accepted: 10/16/2020] [Indexed: 12/22/2022] Open
Abstract
Lectins are non-covalent glycan-binding proteins mediating cellular interactions but their annotation in newly sequenced organisms is lacking. The limited size of functional domains and the low level of sequence similarity challenge usual bioinformatics tools. The identification of lectin domains in proteomes requires the manual curation of sequence alignments based on structural folds. A new lectin classification is proposed. It is built on three levels: (i) 35 lectin domain folds, (ii) 109 classes of lectins sharing at least 20% sequence similarity and (iii) 350 families of lectins sharing at least 70% sequence similarity. This information is compiled in the UniLectin platform that includes the previously described UniLectin3D database of curated lectin 3D structures. Since its first release, UniLectin3D has been updated with 485 additional 3D structures. The database is now complemented by two additional modules: PropLec containing predicted β-propeller lectins and LectomeXplore including predicted lectins from sequences of the NBCI-nr and UniProt for every curated lectin class. UniLectin is accessible at https://www.unilectin.eu/.
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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
- Computer Science Department, University of Geneva, CH-1227 Geneva, Switzerland
| | - Julien Mariethoz
- Proteome Informatics Group, SIB Swiss Institute of Bioinformatics, CH-1227 Geneva, Switzerland
- Computer Science Department, University of Geneva, CH-1227 Geneva, Switzerland
- Section of Biology, University of Geneva, CH-1205 Geneva, Switzerland
| | - Serge Pérez
- Univ. Grenoble Alpes, CNRS, CERMAV, 38000 Grenoble, France
| | - Anne Imberty
- Univ. Grenoble Alpes, CNRS, CERMAV, 38000 Grenoble, France
| | - Frédérique Lisacek
- Proteome Informatics Group, SIB Swiss Institute of Bioinformatics, CH-1227 Geneva, Switzerland
- Computer Science Department, University of Geneva, CH-1227 Geneva, Switzerland
- Section of Biology, University of Geneva, CH-1205 Geneva, Switzerland
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18
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Scherbinina SI, Toukach PV. Three-Dimensional Structures of Carbohydrates and Where to Find Them. Int J Mol Sci 2020; 21:E7702. [PMID: 33081008 PMCID: PMC7593929 DOI: 10.3390/ijms21207702] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2020] [Revised: 10/15/2020] [Accepted: 10/16/2020] [Indexed: 02/06/2023] Open
Abstract
Analysis and systematization of accumulated data on carbohydrate structural diversity is a subject of great interest for structural glycobiology. Despite being a challenging task, development of computational methods for efficient treatment and management of spatial (3D) structural features of carbohydrates breaks new ground in modern glycoscience. This review is dedicated to approaches of chemo- and glyco-informatics towards 3D structural data generation, deposition and processing in regard to carbohydrates and their derivatives. Databases, molecular modeling and experimental data validation services, and structure visualization facilities developed for last five years are reviewed.
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Affiliation(s)
- Sofya I. Scherbinina
- N.D. Zelinsky Institute of Organic Chemistry, Russian Academy of Science, Leninsky prospect 47, 119991 Moscow, Russia
- Higher Chemical College, D. Mendeleev University of Chemical Technology of Russia, Miusskaya Square 9, 125047 Moscow, Russia
| | - Philip V. Toukach
- N.D. Zelinsky Institute of Organic Chemistry, Russian Academy of Science, Leninsky prospect 47, 119991 Moscow, Russia
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19
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Lal K, Bermeo R, Perez S. Computational tools for drawing, building and displaying carbohydrates: a visual guide. Beilstein J Org Chem 2020; 16:2448-2468. [PMID: 33082879 PMCID: PMC7537382 DOI: 10.3762/bjoc.16.199] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2020] [Accepted: 09/17/2020] [Indexed: 01/08/2023] Open
Abstract
Drawing and visualisation of molecular structures are some of the most common tasks carried out in structural glycobiology, typically using various software. In this perspective article, we outline developments in the computational tools for the sketching, visualisation and modelling of glycans. The article also provides details on the standard representation of glycans, and glycoconjugates, which helps the communication of structure details within the scientific community. We highlight the comparative analysis of the available tools which could help researchers to perform various tasks related to structure representation and model building of glycans. These tools can be useful for glycobiologists or any researcher looking for a ready to use, simple program for the sketching or building of glycans.
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Affiliation(s)
- Kanhaya Lal
- Univ. Grenoble Alpes, CNRS, CERMAV, 38000 Grenoble, France
- Dipartimento di Chimica, Università Degli Studi di Milano, via Golgi 19, I-20133, Italy
| | - Rafael Bermeo
- Univ. Grenoble Alpes, CNRS, CERMAV, 38000 Grenoble, France
- Dipartimento di Chimica, Università Degli Studi di Milano, via Golgi 19, I-20133, Italy
| | - Serge Perez
- Univ. Grenoble Alpes, CNRS, CERMAV, 38000 Grenoble, France
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20
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Roy U. Insight into the structures of Interleukin-18 systems. Comput Biol Chem 2020; 88:107353. [PMID: 32769049 PMCID: PMC7392904 DOI: 10.1016/j.compbiolchem.2020.107353] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2020] [Revised: 07/01/2020] [Accepted: 07/28/2020] [Indexed: 02/08/2023]
Abstract
Structure-based molecular designs play a critical role in the context of next generation drug development. Besides their fundamental scientific aspects, the findings established in this approach have significant implications in the expansions of target-based therapies and vaccines. Interleukin-18 (IL-18), also known as interferon gamma (IFN-γ) inducing factor, is a pro-inflammatory cytokine. The IL-18 binds first to the IL-18α receptor and forms a lower affinity complex. Upon binding with IL-18β a hetero-trimeric complex with higher affinity is formed that initiates the signal transduction process. The present study, including structural and molecular dynamics simulations, takes a close look at the structural stabilities of IL-18 and IL-18 receptor-bound ligand structures as functions of time. The results help to identify the conformational changes of the ligand due to receptor binding, as well as the structural orders of the apo and holo IL-18 protein complexes.
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Affiliation(s)
- Urmi Roy
- Department of Chemistry & Biomolecular Science, Clarkson University, 8 Clarkson Avenue, Potsdam, NY 13699-5820, United States.
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21
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Databases and Bioinformatic Tools for Glycobiology and Glycoproteomics. Int J Mol Sci 2020; 21:ijms21186727. [PMID: 32937895 PMCID: PMC7556027 DOI: 10.3390/ijms21186727] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2020] [Revised: 09/03/2020] [Accepted: 09/11/2020] [Indexed: 02/07/2023] Open
Abstract
Glycosylation plays critical roles in various biological processes and is closely related to diseases. Deciphering the glycocode in diverse cells and tissues offers opportunities to develop new disease biomarkers and more effective recombinant therapeutics. In the past few decades, with the development of glycobiology, glycomics, and glycoproteomics technologies, a large amount of glycoscience data has been generated. Subsequently, a number of glycobiology databases covering glycan structure, the glycosylation sites, the protein scaffolds, and related glycogenes have been developed to store, analyze, and integrate these data. However, these databases and tools are not well known or widely used by the public, including clinicians and other researchers who are not in the field of glycobiology, but are interested in glycoproteins. In this study, the representative databases of glycan structure, glycoprotein, glycan-protein interactions, glycogenes, and the newly developed bioinformatic tools and integrated portal for glycoproteomics are reviewed. We hope this overview could assist readers in searching for information on glycoproteins of interest, and promote further clinical application of glycobiology.
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22
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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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23
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Neelamegham S, Aoki-Kinoshita K, Bolton E, Frank M, Lisacek F, Lütteke T, O'Boyle N, Packer NH, Stanley P, Toukach P, Varki A, Woods RJ. Updates to the Symbol Nomenclature for Glycans guidelines. Glycobiology 2020; 29:620-624. [PMID: 31184695 DOI: 10.1093/glycob/cwz045] [Citation(s) in RCA: 257] [Impact Index Per Article: 64.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2019] [Revised: 05/15/2019] [Accepted: 06/06/2019] [Indexed: 11/14/2022] Open
Abstract
The Symbol Nomenclature for Glycans (SNFG) is a community-curated standard for the depiction of monosaccharides and complex glycans using various colored-coded, geometric shapes, along with defined text additions. It is hosted by the National Center for Biotechnology Information (NCBI) at the NCBI-Glycans Page (www.ncbi.nlm.nih.gov/glycans/snfg.html). Several changes have been made to the SNFG page in the past year to update the rules for depicting glycans using the SNFG, to include more examples of use, particularly for non-mammalian organisms, and to provide guidelines for the depiction of ambiguous glycan structures. This Glycoforum article summarizes these recent changes.
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Affiliation(s)
- Sriram Neelamegham
- Department of Chemical & Biological Engineering and Medicine, State University of New York, 906 Furnas Hall, Buffalo, NY 14260, USA
| | - Kiyoko Aoki-Kinoshita
- Glycan & Life System Integration Center (GaLSIC), Faculty of Science and Engineering, Soka University, 1-236 Tangi-machi, Hachioji, Tokyo 192-8577, Japan
| | - Evan Bolton
- National Library of Medicine, 8600 Rockville Pike, Bldg. 38A, Room 8S810, Bethesda, MD 20896, USA
| | - Martin Frank
- Biognos AB, Generatorsgatan 1 / Box 8963, 402 74 Göteborg, Sweden
| | - Frederique Lisacek
- Proteome Informatics Group, SIB Swiss Institute of Bioinformatics, Computer Science Department, University of Geneva, route de Drize 7, CH - 1227 Geneva Switzerland, and also Section of Biology, University of Geneva, Geneva, Switzerland
| | - Thomas Lütteke
- GIP GmbH, Strahlenberger Str. 112, 63067 Offenbach, Germany
| | - Noel O'Boyle
- NextMove Software, Innovation Centre, Cambridge Science Park, Milton Road, Cambridge, CB4 0EY, UK
| | - Nicolle H Packer
- Department of Molecular Sciences, Faculty of Science & Engineering, Rm 307, Building E8C, Macquarie University, Sydney, NSW 2109, Australia
| | - Pamela Stanley
- Department of Cell Biology, Albert Einstein College of Medicine, 1300 Morris Park Ave, New York, NY, 10461, USA
| | - Philip Toukach
- Laboratory of Carbohydrate Chemistry, Zelinsky Institute of Organic Chemistry, Russian Academy of Sciences. 119991 Moscow, Leninsky prospect 47, Russia
| | - Ajit Varki
- Glycobiology Research and Training Center, University of California, San Diego, La Jolla, CA 92093, USA
| | - Robert J Woods
- Complex Carbohydrate Research Center, University of Georgia, 315 Riverbend Rd, Athens, GA, 30602, USA
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24
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Cao WQ, Liu MQ, Kong SY, Wu MX, Huang ZZ, Yang PY. Novel methods in glycomics: a 2019 update. Expert Rev Proteomics 2020; 17:11-25. [PMID: 31914820 DOI: 10.1080/14789450.2020.1708199] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/16/2023]
Abstract
Introduction: Glycomics, which aims to define the glycome of a biological system to better assess the biological attributes of the glycans, has attracted increasing interest. However, the complexity and diversity of glycans present challenging barriers to glycome definition. Technological advances are major drivers in glycomics.Areas covered: This review summarizes the main methods and emphasizes the most recent advances in mass spectrometry-based methods regarding glycomics following the general workflow in glycomic analysis.Expert opinion: Recent mass spectrometry-based technological advances have significantly lowered the barriers in glycomics. The field of glycomics is moving toward both generic and precise analysis.
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Affiliation(s)
- Wei-Qian Cao
- Shanghai Fifth People's Hospital and Institutes of Biomedical Sciences, Fudan University, Shanghai, China.,NHC Key Laboratory of Glycoconjugates Research, Fudan University, Shanghai, China
| | - Ming-Qi Liu
- Shanghai Fifth People's Hospital and Institutes of Biomedical Sciences, Fudan University, Shanghai, China
| | - Si-Yuan Kong
- Shanghai Fifth People's Hospital and Institutes of Biomedical Sciences, Fudan University, Shanghai, China
| | - Meng-Xi Wu
- Shanghai Fifth People's Hospital and Institutes of Biomedical Sciences, Fudan University, Shanghai, China.,Department of Chemistry, Fudan University, Shanghai, China
| | - Zheng-Ze Huang
- Shanghai Fifth People's Hospital and Institutes of Biomedical Sciences, Fudan University, Shanghai, China
| | - Peng-Yuan Yang
- Shanghai Fifth People's Hospital and Institutes of Biomedical Sciences, Fudan University, Shanghai, China.,NHC Key Laboratory of Glycoconjugates Research, Fudan University, Shanghai, China.,Department of Chemistry, Fudan University, Shanghai, China
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25
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Bonnardel F, Perez S, Lisacek F, Imberty A. Structural Database for Lectins and the UniLectin Web Platform. Methods Mol Biol 2020; 2132:1-14. [PMID: 32306309 DOI: 10.1007/978-1-0716-0430-4_1] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
The search for new biomolecules requires a clear understanding of biosynthesis and degradation pathways. This view applies to most metabolites as well as other molecule types such as glycans whose repertoire is still poorly characterized. Lectins are proteins that recognize specifically and interact noncovalently with glycans. This particular class of proteins is considered as playing a major role in biology. Glycan-binding is based on multivalence, which gives lectins a unique capacity to interact with surface glycans and significantly contribute to cell-cell recognition and interactions. Lectins have been studied for many years using multiple technologies and part of the resulting information is available online in databases. Unfortunately, the connectivity of these databases with the most popular omics databases (genomics, proteomics, and glycomics) remains limited. Moreover, lectin diversity is extended and requires setting out a flexible classification that remains compatible with new sequences and 3D structures that are continuously released. We have designed UniLectin as a new insight into the knowledge of lectins, their classification, and their biological role. This platform encompasses UniLectin3D, a curated database of lectin 3D structures that follows a periodically updated classification, a set of comparative and visualizing tools and gradually released modules dedicated to specific lectins predicted in sequence databases. The second module is PropLec, focused on β-propeller lectin prediction in all species based on five distinct family profiles. This chapter describes how UniLectin can be used to explore the diversity of lectins, their 3D structures, and associated functional information as well as to perform reliable predictions of β-propeller lectins.
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Affiliation(s)
- François Bonnardel
- Univ. Grenoble Alpes, CNRS, CERMAV, Grenoble, France.,Swiss Institute of Bioinformatics, Geneva, Switzerland.,Computer Science Department, UniGe, Geneva, Switzerland
| | - Serge Perez
- Univ. Grenoble Alpes, CNRS, CERMAV, Grenoble, France
| | - Frédérique Lisacek
- Swiss Institute of Bioinformatics, Geneva, Switzerland. .,Computer Science Department, UniGe, Geneva, Switzerland. .,Section of Biology, UniGe, Geneva, Switzerland.
| | - Anne Imberty
- Univ. Grenoble Alpes, CNRS, CERMAV, Grenoble, France.
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26
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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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27
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Clerc O, Mariethoz J, Rivet A, Lisacek F, Pérez S, Ricard-Blum S. A pipeline to translate glycosaminoglycan sequences into 3D models. Application to the exploration of glycosaminoglycan conformational space. Glycobiology 2019; 29:36-44. [PMID: 30239692 DOI: 10.1093/glycob/cwy084] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2018] [Accepted: 09/16/2018] [Indexed: 12/11/2022] Open
Abstract
Mammalian glycosaminoglycans are linear complex polysaccharides comprising heparan sulfate, heparin, dermatan sulfate, chondroitin sulfate, keratan sulfate and hyaluronic acid. They bind to numerous proteins and these interactions mediate their biological activities. GAG-protein interaction data reported in the literature are curated mostly in MatrixDB database (http://matrixdb.univ-lyon1.fr/). However, a standard nomenclature and a machine-readable format of GAGs together with bioinformatics tools for mining these interaction data are lacking. We report here the building of an automated pipeline to (i) standardize the format of GAG sequences interacting with proteins manually curated from the literature, (ii) translate them into the machine-readable GlycoCT format and into SNFG (Symbol Nomenclature For Glycan) images and (iii) convert their sequences into a format processed by a builder generating three-dimensional structures of polysaccharides based on a repertoire of conformations experimentally validated by data extracted from crystallized GAG-protein complexes. We have developed for this purpose a converter (the CT23D converter) to automatically translate the GlycoCT code of a GAG sequence into the input file required to construct a three-dimensional model.
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Affiliation(s)
- Olivier Clerc
- University Lyon, University Claude Bernard Lyon 1, CNRS, INSA Lyon, CPE, Institute of Molecular and Supramolecular Chemistry and Biochemistry, UMR 5246, Villeurbanne Cedex, France
| | - Julien Mariethoz
- SIB Swiss Institute of Bioinformatics, Geneva 4, Switzerland.,Department of Computer Science, University of Geneva, Geneva 4, Switzerland
| | - Alain Rivet
- Centre de Recherches sur les MAcromolécules Végétales, UPR 5301 CNRS, University Grenoble Alpes, Grenoble, France
| | - Frédérique Lisacek
- SIB Swiss Institute of Bioinformatics, Geneva 4, Switzerland.,Department of Computer Science, University of Geneva, Geneva 4, Switzerland.,Section of Biology, University of Geneva, Geneva 4, Switzerland
| | - Serge Pérez
- Centre de Recherches sur les MAcromolécules Végétales, UPR 5301 CNRS, University Grenoble Alpes, Grenoble, France
| | - Sylvie Ricard-Blum
- University Lyon, University Claude Bernard Lyon 1, CNRS, INSA Lyon, CPE, Institute of Molecular and Supramolecular Chemistry and Biochemistry, UMR 5246, Villeurbanne Cedex, France
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28
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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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29
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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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30
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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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31
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Ruhaak LR, Xu G, Li Q, Goonatilleke E, Lebrilla CB. Mass Spectrometry Approaches to Glycomic and Glycoproteomic Analyses. Chem Rev 2018; 118:7886-7930. [PMID: 29553244 PMCID: PMC7757723 DOI: 10.1021/acs.chemrev.7b00732] [Citation(s) in RCA: 253] [Impact Index Per Article: 42.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
Glycomic and glycoproteomic analyses involve the characterization of oligosaccharides (glycans) conjugated to proteins. Glycans are produced through a complicated nontemplate driven process involving the competition of enzymes that extend the nascent chain. The large diversity of structures, the variations in polarity of the individual saccharide residues, and the poor ionization efficiencies of glycans all conspire to make the analysis arguably much more difficult than any other biopolymer. Furthermore, the large number of glycoforms associated with a specific protein site makes it more difficult to characterize than any post-translational modification. Nonetheless, there have been significant progress, and advanced separation and mass spectrometry methods have been at its center and the main reason for the progress. While glycomic and glycoproteomic analyses are still typically available only through highly specialized laboratories, new software and workflow is making it more accessible. This review focuses on the role of mass spectrometry and separation methods in advancing glycomic and glycoproteomic analyses. It describes the current state of the field and progress toward making it more available to the larger scientific community.
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Affiliation(s)
- L. Renee Ruhaak
- Department of Clinical Chemistry and Laboratory Medicine, Leiden University Medical Center, 2333 ZA Leiden, The Netherlands
| | - Gege Xu
- Department of Chemistry, University of California, Davis, One Shields Avenue, Davis, California 95616, United States
| | - Qiongyu Li
- Department of Chemistry, University of California, Davis, One Shields Avenue, Davis, California 95616, United States
| | - Elisha Goonatilleke
- Department of Chemistry, University of California, Davis, One Shields Avenue, Davis, California 95616, United States
| | - Carlito B. Lebrilla
- Department of Chemistry, University of California, Davis, One Shields Avenue, Davis, California 95616, United States
- Department of Biochemistry and Molecular Medicine, University of California, Davis, Davis, California 95616, United States
- Foods for Health Institute, University of California, Davis, Davis, California 95616, United States
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32
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Tiemeyer M, Aoki K, Paulson J, Cummings RD, York WS, Karlsson NG, Lisacek F, Packer NH, Campbell MP, Aoki NP, Fujita A, Matsubara M, Shinmachi D, Tsuchiya S, Yamada I, Pierce M, Ranzinger R, Narimatsu H, Aoki-Kinoshita KF. GlyTouCan: an accessible glycan structure repository. Glycobiology 2017; 27:915-919. [PMID: 28922742 PMCID: PMC5881658 DOI: 10.1093/glycob/cwx066] [Citation(s) in RCA: 103] [Impact Index Per Article: 14.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2017] [Revised: 07/06/2017] [Accepted: 07/07/2017] [Indexed: 11/12/2022] Open
Abstract
Rapid and continued growth in the generation of glycomic data has revealed the need for enhanced development of basic infrastructure for presenting and interpreting these datasets in a manner that engages the broader biomedical research community. Early in their growth, the genomic and proteomic fields implemented mechanisms for assigning unique gene and protein identifiers that were essential for organizing data presentation and for enhancing bioinformatic approaches to extracting knowledge. Similar unique identifiers are currently absent from glycomic data. In order to facilitate continued growth and expanded accessibility of glycomic data, the authors strongly encourage the glycomics community to coordinate the submission of their glycan structures to the GlyTouCan Repository and to make use of GlyTouCan identifiers in their communications and publications. The authors also deeply encourage journals to recommend a submission workflow in which submitted publications utilize GlyTouCan identifiers as a standard reference for explicitly describing glycan structures cited in manuscripts.
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Affiliation(s)
- Michael Tiemeyer
- Complex Carbohydrate Research Center, The University of Georgia, 315 Riverbend Rd, Athens, Georgia 30602, USA
| | - Kazuhiro Aoki
- Complex Carbohydrate Research Center, The University of Georgia, 315 Riverbend Rd, Athens, Georgia 30602, USA
| | - James Paulson
- The Scripps Research Institute, 10550 N. Torrey Pines Road, La Jolla, CA 92037, USA
| | - Richard D Cummings
- Harvard Medical School, 330 Brookline Ave, Room SL-0408, Boston, MA 02115, USA
| | - William S York
- Complex Carbohydrate Research Center, The University of Georgia, 315 Riverbend Rd, Athens, Georgia 30602, USA
| | | | - Frederique Lisacek
- Swiss Institute of Bioinformatics, CUI - 7, route de Drize, CH-1211 Geneva, Switzerland
| | - Nicolle H Packer
- Institute for Glycomics, Gold Coast Campus, Griffith University, Parklands Drive, Gold Coast, QLD 4222, Australia
- Macquarie University, Balaclava Road, North Ryde, NSW 2109, Australia
| | - Matthew P Campbell
- Institute for Glycomics, Gold Coast Campus, Griffith University, Parklands Drive, Gold Coast, QLD 4222, Australia
| | - Nobuyuki P Aoki
- Soka University, 1-236 Tangi-machi, Hachioji, Tokyo 192-8577, Japan
| | - Akihiro Fujita
- Soka University, 1-236 Tangi-machi, Hachioji, Tokyo 192-8577, Japan
| | - Masaaki Matsubara
- Complex Carbohydrate Research Center, The University of Georgia, 315 Riverbend Rd, Athens, Georgia 30602, USA
| | | | | | - Issaku Yamada
- The Noguchi Institute, 1-9-7, Kaga, Itabashi-ku, Tokyo 173-0003, Japan
| | - Michael Pierce
- Complex Carbohydrate Research Center, The University of Georgia, 315 Riverbend Rd, Athens, Georgia 30602, USA
| | - René Ranzinger
- Complex Carbohydrate Research Center, The University of Georgia, 315 Riverbend Rd, Athens, Georgia 30602, USA
| | - Hisashi Narimatsu
- National Institute of Advanced Industrial Science and Technology (AIST), 1-1-1 Umezono, Tsukuba, Ibaraki 305-0046, Japan
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33
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Karlsson NG, Jin C, Rojas-Macias MA, Adamczyk B. Next Generation O-Linked Glycomics. TRENDS GLYCOSCI GLYC 2017. [DOI: 10.4052/tigg.1602.1e] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
Affiliation(s)
- Niclas G. Karlsson
- Department of Medical Biochemistry and Cell Biology, Institute of Biomedicine, Sahlgrenska Academy, University of Gothenburg
| | - Chunsheng Jin
- Department of Medical Biochemistry and Cell Biology, Institute of Biomedicine, Sahlgrenska Academy, University of Gothenburg
| | - Miguel A. Rojas-Macias
- Department of Medical Biochemistry and Cell Biology, Institute of Biomedicine, Sahlgrenska Academy, University of Gothenburg
| | - Barbara Adamczyk
- Department of Medical Biochemistry and Cell Biology, Institute of Biomedicine, Sahlgrenska Academy, University of Gothenburg
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34
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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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35
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Lisacek F, Mariethoz J, Alocci D, Rudd PM, Abrahams JL, Campbell MP, Packer NH, Ståhle J, Widmalm G, Mullen E, Adamczyk B, Rojas-Macias MA, Jin C, Karlsson NG. Databases and Associated Tools for Glycomics and Glycoproteomics. Methods Mol Biol 2017; 1503:235-264. [PMID: 27743371 DOI: 10.1007/978-1-4939-6493-2_18] [Citation(s) in RCA: 36] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/03/2022]
Abstract
The access to biodatabases for glycomics and glycoproteomics has proven to be essential for current glycobiological research. This chapter presents available databases that are devoted to different aspects of glycobioinformatics. This includes oligosaccharide sequence databases, experimental databases, 3D structure databases (of both glycans and glycorelated proteins) and association of glycans with tissue, disease, and proteins. Specific search protocols are also provided using tools associated with experimental databases for converting primary glycoanalytical data to glycan structural information. In particular, researchers using glycoanalysis methods by U/HPLC (GlycoBase), MS (GlycoWorkbench, UniCarb-DB, GlycoDigest), and NMR (CASPER) will benefit from this chapter. In addition we also include information on how to utilize glycan structural information to query databases that associate glycans with proteins (UniCarbKB) and with interactions with pathogens (SugarBind).
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Affiliation(s)
- Frederique Lisacek
- Proteome Informatics Group, SIB Swiss Institute of Bioinformatics, Geneva, Switzerland
| | - Julien Mariethoz
- Proteome Informatics Group, SIB Swiss Institute of Bioinformatics, Geneva, Switzerland
| | - Davide Alocci
- Proteome Informatics Group, SIB Swiss Institute of Bioinformatics, Geneva, Switzerland
| | - Pauline M Rudd
- NIBRT GlycoScience Group, NIBRT-The National Institute for Bioprocessing Research and Training, Fosters Avenue, Mount Merrion, Blackrock, Co., Dublin, Ireland
| | - Jodie L Abrahams
- Biomolecular Frontiers Research Centre, Macquarie University, North Ryde, NSW, Australia
| | - Matthew P Campbell
- Biomolecular Frontiers Research Centre, Macquarie University, North Ryde, NSW, Australia
| | - Nicolle H Packer
- Biomolecular Frontiers Research Centre, Macquarie University, North Ryde, NSW, Australia
| | - Jonas Ståhle
- Department of Organic Chemistry, Arrhenius Laboratory, Stockholm University, Stockholm, Sweden
| | - Göran Widmalm
- Department of Organic Chemistry, Arrhenius Laboratory, Stockholm University, Stockholm, Sweden
| | | | - Barbara Adamczyk
- NIBRT GlycoScience Group, NIBRT-The National Institute for Bioprocessing Research and Training, Fosters Avenue, Mount Merrion, Blackrock, Co., Dublin, Ireland
- Department of Medical Biochemistry and Cell Biology, Institute of Biomedicine, Sahlgrenska Academy, University of Gothenburg, Box 440, 405 30, Gothenburg, Sweden
| | - Miguel A Rojas-Macias
- Department of Medical Biochemistry and Cell Biology, Institute of Biomedicine, Sahlgrenska Academy, University of Gothenburg, Box 440, 405 30, Gothenburg, Sweden
| | - Chunsheng Jin
- Department of Medical Biochemistry and Cell Biology, Institute of Biomedicine, Sahlgrenska Academy, University of Gothenburg, Box 440, 405 30, Gothenburg, Sweden
| | - Niclas G Karlsson
- Department of Medical Biochemistry and Cell Biology, Institute of Biomedicine, Sahlgrenska Academy, University of Gothenburg, Box 440, 405 30, Gothenburg, Sweden.
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36
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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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37
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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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38
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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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The SIB Swiss Institute of Bioinformatics' resources: focus on curated databases. Nucleic Acids Res 2015; 44:D27-37. [PMID: 26615188 PMCID: PMC4702916 DOI: 10.1093/nar/gkv1310] [Citation(s) in RCA: 46] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2015] [Accepted: 11/09/2015] [Indexed: 12/15/2022] Open
Abstract
The SIB Swiss Institute of Bioinformatics (www.isb-sib.ch) provides world-class bioinformatics databases, software tools, services and training to the international life science community in academia and industry. These solutions allow life scientists to turn the exponentially growing amount of data into knowledge. Here, we provide an overview of SIB's resources and competence areas, with a strong focus on curated databases and SIB's most popular and widely used resources. In particular, SIB's Bioinformatics resource portal ExPASy features over 150 resources, including UniProtKB/Swiss-Prot, ENZYME, PROSITE, neXtProt, STRING, UniCarbKB, SugarBindDB, SwissRegulon, EPD, arrayMap, Bgee, SWISS-MODEL Repository, OMA, OrthoDB and other databases, which are briefly described in this article.
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