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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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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3
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Liu Y, Huang Y, Zhu R, Farag MA, Capanoglu E, Zhao C. Structural elucidation approaches in carbohydrates: A comprehensive review on techniques and future trends. Food Chem 2023; 400:134118. [DOI: 10.1016/j.foodchem.2022.134118] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2022] [Accepted: 09/01/2022] [Indexed: 10/14/2022]
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4
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Toukach PV, Egorova KS. Source files of the Carbohydrate Structure Database: the way to sophisticated analysis of natural glycans. Sci Data 2022; 9:131. [PMID: 35354826 PMCID: PMC8968703 DOI: 10.1038/s41597-022-01186-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2021] [Accepted: 02/03/2022] [Indexed: 11/18/2022] Open
Abstract
The Carbohydrate Structure Database (CSDB, http://csdb.glycoscience.ru/ ) is a free curated repository storing various data on glycans of bacterial, fungal and plant origins. Currently, it maintains a close-to-full coverage on bacterial and fungal carbohydrates up to the year 2020. The CSDB web-interface provides free access to the database content and dedicated tools. Still, the number of these tools and the types of the corresponding analyses is limited, whereas the database itself contains data that can be used in a broader scope of analytical studies. In this paper, we present CSDB source data files and a self-contained SQL dump, and exemplify their possible application in glycan-related studies. By using CSDB in an SQL format, the user can gain access to the chain length distribution or charge distribution (as an example) in a given set of glycans defined according to specific structural, taxonomic, or other parameters, whereas the source text dump files can be imported to any dedicated database with a specific internal architecture differing from that of CSDB.
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Affiliation(s)
- Philip V Toukach
- N.D. Zelinsky Institute of Organic Chemistry, Russian Academy of Sciences, Leninsky prospect 47, Moscow, 119991, Russia.
| | - Ksenia S Egorova
- N.D. Zelinsky Institute of Organic Chemistry, Russian Academy of Sciences, Leninsky prospect 47, Moscow, 119991, Russia.
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5
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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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6
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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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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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8
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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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9
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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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10
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Toukach PV, Egorova KS. New Features of Carbohydrate Structure Database Notation (CSDB Linear), As Compared to Other Carbohydrate Notations. J Chem Inf Model 2019; 60:1276-1289. [DOI: 10.1021/acs.jcim.9b00744] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Philip V. Toukach
- N.D. Zelinsky Institute of Organic Chemistry, Russian Academy of Sciences, Leninsky prosect 47, Moscow, Russia 119991
- National Research University Higher School of Economics, Myasnitskaya 20, Moscow, Russia 101000
| | - Ksenia S. Egorova
- N.D. Zelinsky Institute of Organic Chemistry, Russian Academy of Sciences, Leninsky prosect 47, Moscow, Russia 119991
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11
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Nagarajan B, Sankaranarayanan NV, Desai UR. Perspective on computational simulations of glycosaminoglycans. WILEY INTERDISCIPLINARY REVIEWS. COMPUTATIONAL MOLECULAR SCIENCE 2019; 9:e1388. [PMID: 31080520 PMCID: PMC6504973 DOI: 10.1002/wcms.1388] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/03/2018] [Accepted: 07/07/2018] [Indexed: 01/06/2023]
Abstract
Glycosaminoglycans (GAGs) represent a formidable frontier for chemists, biochemists, biologists, medicinal chemists and drug delivery specialists because of massive structural complexity. GAGs are arguably the most complex, natural linear biopolymers with theoretical diversity orders of magnitude higher than proteins and nucleic acids. Yet, this diversity remains generally untapped. Computational approaches offer major routes to understand GAG structure and dynamics so as to enable novel applications of these biopolymers. In fact, computational algorithms, softwares, online tools and techniques have reached a level of sophistication that help understand atomistic details of conformational variation and protein recognition of individual GAG sequences. This review describes current approaches and challenges in computational study of GAGs. It presents a history of major findings since the earliest mention of GAGs (the 1960s), the development of parameters and force fields specific for GAGs, and the application of these tools in understanding GAG structure-function relationship. This review also presents a section on how to perform simulation of GAGs, which is directed toward researchers interested in entering this promising field with potential to impact therapy.
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Affiliation(s)
- Balaji Nagarajan
- Institute for Structural Biology, Drug Discovery and Development, Virginia Commonwealth University, Richmond,
VA 23298, USA
- Department of Medicinal Chemistry, Virginia Commonwealth University, Richmond, VA 23298, USA
| | - Nehru Viji Sankaranarayanan
- Institute for Structural Biology, Drug Discovery and Development, Virginia Commonwealth University, Richmond,
VA 23298, USA
- Department of Medicinal Chemistry, Virginia Commonwealth University, Richmond, VA 23298, USA
| | - Umesh R. Desai
- Institute for Structural Biology, Drug Discovery and Development, Virginia Commonwealth University, Richmond,
VA 23298, USA
- Department of Medicinal Chemistry, Virginia Commonwealth University, Richmond, VA 23298, USA
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12
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Gourdine JPF, Brush MH, Vasilevsky NA, Shefchek K, Köhler S, Matentzoglu N, Munoz-Torres MC, McMurry JA, Zhang XA, Robinson PN, Haendel MA. Representing glycophenotypes: semantic unification of glycobiology resources for disease discovery. Database (Oxford) 2019; 2019:baz114. [PMID: 31735951 PMCID: PMC6859258 DOI: 10.1093/database/baz114] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2019] [Revised: 08/27/2019] [Accepted: 08/28/2019] [Indexed: 12/11/2022]
Abstract
While abnormalities related to carbohydrates (glycans) are frequent for patients with rare and undiagnosed diseases as well as in many common diseases, these glycan-related phenotypes (glycophenotypes) are not well represented in knowledge bases (KBs). If glycan-related diseases were more robustly represented and curated with glycophenotypes, these could be used for molecular phenotyping to help to realize the goals of precision medicine. Diagnosis of rare diseases by computational cross-species comparison of genotype-phenotype data has been facilitated by leveraging ontological representations of clinical phenotypes, using Human Phenotype Ontology (HPO), and model organism ontologies such as Mammalian Phenotype Ontology (MP) in the context of the Monarch Initiative. In this article, we discuss the importance and complexity of glycobiology and review the structure of glycan-related content from existing KBs and biological ontologies. We show how semantically structuring knowledge about the annotation of glycophenotypes could enhance disease diagnosis, and propose a solution to integrate glycophenotypes and related diseases into the Unified Phenotype Ontology (uPheno), HPO, Monarch and other KBs. We encourage the community to practice good identifier hygiene for glycans in support of semantic analysis, and clinicians to add glycomics to their diagnostic analyses of rare diseases.
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Affiliation(s)
- Jean-Philippe F Gourdine
- Oregon Clinical & Translational Research Institute, Oregon Health & Science University, Portland, OR 97239, USA
- OHSU Library, Oregon Health & Science University Library, Portland, OR 97239, USA
- Monarch Initiative, monarchinitiative.org
| | - Matthew H Brush
- Oregon Clinical & Translational Research Institute, Oregon Health & Science University, Portland, OR 97239, USA
- Monarch Initiative, monarchinitiative.org
| | - Nicole A Vasilevsky
- Oregon Clinical & Translational Research Institute, Oregon Health & Science University, Portland, OR 97239, USA
- Monarch Initiative, monarchinitiative.org
| | - Kent Shefchek
- Monarch Initiative, monarchinitiative.org
- Linus Pauling Institute, Oregon State University, Corvallis, OR 97331, USA
| | - Sebastian Köhler
- Monarch Initiative, monarchinitiative.org
- Charité Centrum für Therapieforschung, Charité-Universitätsmedizin Berlin Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Berlin 10117, Germany
| | - Nicolas Matentzoglu
- Monarch Initiative, monarchinitiative.org
- European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Cambridge, UK
| | - Monica C Munoz-Torres
- Monarch Initiative, monarchinitiative.org
- Linus Pauling Institute, Oregon State University, Corvallis, OR 97331, USA
| | - Julie A McMurry
- Monarch Initiative, monarchinitiative.org
- Linus Pauling Institute, Oregon State University, Corvallis, OR 97331, USA
| | - Xingmin Aaron Zhang
- Monarch Initiative, monarchinitiative.org
- The Jackson Laboratory for Genomic Medicine, Farmington, CT 06032, USA
| | - Peter N Robinson
- Monarch Initiative, monarchinitiative.org
- The Jackson Laboratory for Genomic Medicine, Farmington, CT 06032, USA
| | - Melissa A Haendel
- Oregon Clinical & Translational Research Institute, Oregon Health & Science University, Portland, OR 97239, USA
- Monarch Initiative, monarchinitiative.org
- Linus Pauling Institute, Oregon State University, Corvallis, OR 97331, USA
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13
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Egorova KS, Toukach PV. Glykoinformatik: Brücken zwischen isolierten Inseln im Datenmeer. Angew Chem Int Ed Engl 2018. [DOI: 10.1002/ange.201803576] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Affiliation(s)
- Ksenia S. Egorova
- Zelinsky Institute of Organic ChemistryRussian Academy of Sciences Leninsky Prospect 47 Moscow 119991 Russland
| | - Philip V. Toukach
- Zelinsky Institute of Organic ChemistryRussian Academy of Sciences Leninsky Prospect 47 Moscow 119991 Russland
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14
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Egorova KS, Toukach PV. Glycoinformatics: Bridging Isolated Islands in the Sea of Data. Angew Chem Int Ed Engl 2018; 57:14986-14990. [DOI: 10.1002/anie.201803576] [Citation(s) in RCA: 25] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2018] [Indexed: 11/07/2022]
Affiliation(s)
- Ksenia S. Egorova
- Zelinsky Institute of Organic ChemistryRussian Academy of Sciences Leninsky Prospect 47 Moscow 119991 Russia
| | - Philip V. Toukach
- Zelinsky Institute of Organic ChemistryRussian Academy of Sciences Leninsky Prospect 47 Moscow 119991 Russia
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15
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The Glycoscience of Immunity. Trends Immunol 2018; 39:523-535. [PMID: 29759949 DOI: 10.1016/j.it.2018.04.004] [Citation(s) in RCA: 48] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2018] [Revised: 04/12/2018] [Accepted: 04/13/2018] [Indexed: 02/05/2023]
Abstract
Carbohydrates, or glycans, are as integral to biology as nucleic acids and proteins. In immunology, glycans are well known to drive diverse functions ranging from glycosaminoglycan-mediated chemokine presentation and selectin-dependent leukocyte trafficking to the discrimination of self and non-self through the recognition of sialic acids by Siglec (sialic acid-binding Ig-like lectin) receptors. In recent years, a number of key immunological discoveries are driving a renewed and burgeoning appreciation for the importance of glycans. In this review, we highlight these findings which collectively help to define and refine our knowledge of the function and impact of glycans within the immune response.
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16
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Emerging glycobiology tools: A renaissance in accessibility. Cell Immunol 2018; 333:2-8. [PMID: 29759530 DOI: 10.1016/j.cellimm.2018.04.010] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2018] [Revised: 04/23/2018] [Accepted: 04/24/2018] [Indexed: 01/01/2023]
Abstract
The glycobiology of the immune response is a topic that has garnered increased attention due to a number of key discoveries surrounding IgG function, the specificity of some broadly neutralizing anti-HIV antibodies, cancer immunoregulation by galectin molecules and others. This review is the opening article in a Special Edition of Cellular Immunology focused on glycoimmunology, and has the goal of setting the context for these articles by providing a mini-review of how glycans impact immunity. We also focus on some of the technological and methodological advances in the field of glycobiology that are being deployed to lower the barrier of entry into the glycosciences, and to more fully interrogate the glycome and its function.
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17
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Minkiewicz P, Iwaniak A, Darewicz M. Annotation of Peptide Structures Using SMILES and Other Chemical Codes-Practical Solutions. Molecules 2017; 22:molecules22122075. [PMID: 29186902 PMCID: PMC6149970 DOI: 10.3390/molecules22122075] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2017] [Revised: 11/15/2017] [Accepted: 11/25/2017] [Indexed: 12/20/2022] Open
Abstract
Contemporary peptide science exploits methods and tools of bioinformatics, and cheminformatics. These approaches use different languages to describe peptide structures—amino acid sequences and chemical codes (especially SMILES), respectively. The latter may be applied, e.g., in comparative studies involving structures and properties of peptides and peptidomimetics. Progress in peptide science “in silico” may be achieved via better communication between biologists and chemists, involving the translation of peptide representation from amino acid sequence into SMILES code. Recent recommendations concerning good practice in chemical information include careful verification of data and their annotation. This publication discusses the generation of SMILES representations of peptides using existing software. Construction of peptide structures containing unnatural and modified amino acids (with special attention paid on glycosylated peptides) is also included. Special attention is paid to the detection and correction of typical errors occurring in SMILES representations of peptides and their correction using molecular editors. Brief recommendations for training of staff working on peptide annotations, are discussed as well.
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Affiliation(s)
- Piotr Minkiewicz
- Chair of Food Biochemistry, Faculty of Food Science, University of Warmia and Mazury in Olsztyn, Plac Cieszyński 1, 10-726 Olsztyn-Kortowo, Poland.
| | - Anna Iwaniak
- Chair of Food Biochemistry, Faculty of Food Science, University of Warmia and Mazury in Olsztyn, Plac Cieszyński 1, 10-726 Olsztyn-Kortowo, Poland.
| | - Małgorzata Darewicz
- Chair of Food Biochemistry, Faculty of Food Science, University of Warmia and Mazury in Olsztyn, Plac Cieszyński 1, 10-726 Olsztyn-Kortowo, Poland.
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18
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Hosoda M, Akune Y, Aoki-Kinoshita KF. Development and application of an algorithm to compute weighted multiple glycan alignments. Bioinformatics 2017; 33:1317-1323. [PMID: 28093404 PMCID: PMC5408794 DOI: 10.1093/bioinformatics/btw827] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2016] [Revised: 12/22/2016] [Accepted: 01/10/2017] [Indexed: 11/13/2022] Open
Abstract
Motivation A glycan consists of monosaccharides linked by glycosidic bonds, has branches and forms complex molecular structures. Databases have been developed to store large amounts of glycan-binding experiments, including glycan arrays with glycan-binding proteins. However, there are few bioinformatics techniques to analyze large amounts of data for glycans because there are few tools that can handle the complexity of glycan structures. Thus, we have developed the MCAW (Multiple Carbohydrate Alignment with Weights) tool that can align multiple glycan structures, to aid in the understanding of their function as binding recognition molecules. Results We have described in detail the first algorithm to perform multiple glycan alignments by modeling glycans as trees. To test our tool, we prepared several data sets, and as a result, we found that the glycan motif could be successfully aligned without any prior knowledge applied to the tool, and the known recognition binding sites of glycans could be aligned at a high rate amongst all our datasets tested. We thus claim that our tool is able to find meaningful glycan recognition and binding patterns using data obtained by glycan-binding experiments. The development and availability of an effective multiple glycan alignment tool opens possibilities for many other glycoinformatics analysis, making this work a big step towards furthering glycomics analysis. Availability and Implementation http://www.rings.t.soka.ac.jp. Contact kkiyoko@soka.ac.jp. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Masae Hosoda
- Department of Bioinformatics, Graduate School of Engineering, Soka University, Tokyo, Japan
| | - Yukie Akune
- Department of Bioinformatics, Graduate School of Engineering, Soka University, Tokyo, Japan
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Tsuchiya S, Aoki NP, Shinmachi D, Matsubara M, Yamada I, Aoki-Kinoshita KF, Narimatsu H. Implementation of GlycanBuilder to draw a wide variety of ambiguous glycans. Carbohydr Res 2017; 445:104-116. [PMID: 28525772 DOI: 10.1016/j.carres.2017.04.015] [Citation(s) in RCA: 34] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2016] [Revised: 04/14/2017] [Accepted: 04/14/2017] [Indexed: 11/17/2022]
Abstract
GlyTouCan version 1.0 was released in 2015 as the international glycan structure repository, and a new sequence format called WURCS (Web3 Unique Representation of Carbohydrate Structures) was proposed during the early stages of the GlyTouCan project. GlyTouCan uses WURCS as its base representation for glycans because existing formats were insufficient in their flexibility to represent any and all glycans universally. Therefore, in order to obtain WURCS strings for existing or new glycan structures, conversion tools or glycan structure editors that can export WURCS became necessary. GlycanBuilder was an obvious choice to extend due to its wide usage by the community. However, GlycanBuilder was limited because it was originally developed to support mammalian glycans. It also did not support the newly proposed monosaccharide symbol standard called Symbol Nomenclature for Glycans (SNFG). Therefore in this work, we implemented a new version of GlycanBuilder to greatly increase its usability. The glycan rendering system was refactored so that cyclic glycans, nested repeating units, monosaccharide compositions and cross-linked glycan structures can be represented. Both import and export utilities for WURCS were also implemented and SNFG symbols were incorporated to allow glycans to be exported as graphics using the latest glycan symbol nomenclature. This new version of GlycanBuilder called "GlycanBuilder2", is able to support a wide variety of ambiguous glycans, including structures containing monosaccharides from bacteria and plants. These glycans can also be displayed using the new SNFG symbols. This tool can aid researchers in communicating about the complex, diverse, and ambiguous structures of glycans more rapidly. Moreover, the new GlycanBuilder can now easily output WURCS sequences from glycans drawn on the canvas. Most importantly, because GlyTouCan employs WURCS as the basic format for registration and searching of glycan information, a wider variety of glycans can now be readily registered and queried in GlyTouCan.
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Affiliation(s)
| | - Nobuyuki P Aoki
- Faculty of Science and Engineering, Soka University, Tokyo, Japan
| | | | | | | | - Kiyoko F Aoki-Kinoshita
- Graduate School of Engineering, Soka University, Tokyo, Japan; Faculty of Science and Engineering, Soka University, Tokyo, Japan; Glycoscience and Glycotechnology Research Group, AIST, Tsukuba, Japan.
| | - Hisashi Narimatsu
- Glycoscience and Glycotechnology Research Group, AIST, Tsukuba, Japan
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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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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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Campbell MP, Packer NH. UniCarbKB: New database features for integrating glycan structure abundance, compositional glycoproteomics data, and disease associations. Biochim Biophys Acta Gen Subj 2016; 1860:1669-75. [PMID: 26940363 DOI: 10.1016/j.bbagen.2016.02.016] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2016] [Revised: 02/23/2016] [Accepted: 02/24/2016] [Indexed: 10/22/2022]
Abstract
BACKGROUND UniCarbKB aims to provide a resource for the representation of mammalian glycobiology knowledge by providing a curated database of structural and experimental data, supported by a web application that allows users to easily find and view richly annotated information. The database comprises two levels of annotation (i) global-specific data of oligosaccharides released and characterised from single purified glycoproteins and (ii) information pertaining to site-specific glycan heterogeneity. Additional, contextual information is provided including structural, bibliographic, and taxonomic information for each entry. METHODS Since the launch of UniCarbKB in 2012, we have continued to improve the organisation of our data model. Recently, we have extended our pipeline to collate structural and abundance changes of oligosaccharides in different human disease states and experimental models to extend our coverage of the human glycome. RESULTS In this manuscript, we demonstrate the capability of UniCarbKB to store and query relative glycan abundance data using a set of published colorectal and prostate cancer cell lines as examples. Furthermore, we outline our strategy for managing large-scale glycoproteomics data, site-specific and glycan compositional data, and how this information is adding value to UniCarbKB. Finally, we summarise our efforts to improve the efficient representation of disease terms and associated changes in glycan heterogeneity by integrating the Disease Ontology. CONCLUSIONS Updates and improvements to UniCarbKB have introduced unique features for storing and displaying glycosylation features of mammalian glycoproteins. The integration of site-specific glycosylation data obtained from large-scale glycoproteomics and introduction of cell line studies will improve the analysis of glycoproteins and entire glycomes. GENERAL SIGNIFICANCE Continuing advancements in analytical technologies and new data types are advancing disease-related glycomics. It is increasingly necessary to ensure all the data are comprehensively annotated. UniCarbKB was established with the mission of providing a resource for human glycobiology by capturing a wide range of data with corresponding annotations. This article is part of a Special Issue entitled "Glycans in personalised medicine" Guest Editor: Professor Gordan Lauc.
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Affiliation(s)
- Matthew P Campbell
- Department of Chemistry and Biomolecular Sciences, Biomolecular Frontiers Research Centre, Macquarie University, Sydney 2109, Australia.
| | - Nicolle H Packer
- Department of Chemistry and Biomolecular Sciences, Biomolecular Frontiers Research Centre, Macquarie University, Sydney 2109, Australia
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Narimatsu H. Development of M2BPGi: a novel fibrosis serum glyco-biomarker for chronic hepatitis/cirrhosis diagnostics. Expert Rev Proteomics 2015; 12:683-93. [PMID: 26394846 DOI: 10.1586/14789450.2015.1084874] [Citation(s) in RCA: 45] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Many proteins in the living body are glycoproteins, which present glycans linked on their surface. Glycan structures reflect the degree of cell differentiation or canceration and are cell specific. These characteristics are advantageous in the development of various disease biomarkers. Glycoprotein-based biomarkers (glyco-biomarkers) are developed by utilizing the specific changes in the glycan structure on a glycoprotein secreted from the diseased cells of interest. Therefore, quantification of the altered glycan structures is the key to developing a new glyco-biomarker. Glycoscience is a relatively new area of molecular science, and recent advancement of glycotechnologies is remarkable. In the author's institute, new glycoscience technologies have been designed to be efficiently utilized for the development of new diagnostic agents. This paper introduces a strategy for glyco-biomarker development, which was successfully applied in the development of Wisteria floribunda agglutinin-positive Mac-2 binding protein M2BPGi, a liver fibrosis marker now commercially available for clinical use.
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Affiliation(s)
- Hisashi Narimatsu
- a Research Center for Medical Glycoscience (RCMG), National Institute of Advanced Industrial Science and Technology (AIST), Central 2, 1-1-1 Umezono, Tsukuba, Ibaraki, 305-8568, Japan
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Toukach PV, Egorova KS. Carbohydrate structure database merged from bacterial, archaeal, plant and fungal parts. Nucleic Acids Res 2015; 44:D1229-36. [PMID: 26286194 PMCID: PMC4702937 DOI: 10.1093/nar/gkv840] [Citation(s) in RCA: 134] [Impact Index Per Article: 14.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2015] [Accepted: 08/07/2015] [Indexed: 12/31/2022] Open
Abstract
The Carbohydrate Structure Databases (CSDBs, http://csdb.glycoscience.ru) store structural, bibliographic, taxonomic, NMR spectroscopic, and other data on natural carbohydrates and their derivatives published in the scientific literature. The CSDB project was launched in 2005 for bacterial saccharides (as BCSDB). Currently, it includes two parts, the Bacterial CSDB and the Plant&Fungal CSDB. In March 2015, these databases were merged to the single CSDB. The combined CSDB includes information on bacterial and archaeal glycans and derivatives (the coverage is close to complete), as well as on plant and fungal glycans and glycoconjugates (almost all structures published up to 1998). CSDB is regularly updated via manual expert annotation of original publications. Both newly annotated data and data imported from other databases are manually curated. The CSDB data are exportable in a number of modern formats, such as GlycoRDF. CSDB provides additional services for simulation of (1)H, (13)C and 2D NMR spectra of saccharides, NMR-based structure prediction, glycan-based taxon clustering and other.
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Affiliation(s)
- Philip V Toukach
- N.D. Zelinsky Institute of Organic Chemistry, Russian Academy of Sciences, Moscow 119991, Russia
| | - Ksenia S Egorova
- N.D. Zelinsky Institute of Organic Chemistry, Russian Academy of Sciences, Moscow 119991, Russia
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