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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Kellman BP, Mariethoz J, Zhang Y, Shaul S, Alteri M, Sandoval D, Jeffris M, Armingol E, Bao B, Lisacek F, Bojar D, Lewis NE. Decoding glycosylation potential from protein structure across human glycoproteins with a multi-view recurrent neural network. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.15.594334. [PMID: 38798633 PMCID: PMC11118808 DOI: 10.1101/2024.05.15.594334] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/29/2024]
Abstract
Glycosylation is described as a non-templated biosynthesis. Yet, the template-free premise is antithetical to the observation that different N-glycans are consistently placed at specific sites. It has been proposed that glycosite-proximal protein structures could constrain glycosylation and explain the observed microheterogeneity. Using site-specific glycosylation data, we trained a hybrid neural network to parse glycosites (recurrent neural network) and match them to feasible N-glycosylation events (graph neural network). From glycosite-flanking sequences, the algorithm predicts most human N-glycosylation events documented in the GlyConnect database and proposed structures corresponding to observed monosaccharide composition of the glycans at these sites. The algorithm also recapitulated glycosylation in Enhanced Aromatic Sequons, SARS-CoV-2 spike, and IgG3 variants, thus demonstrating the ability of the algorithm to predict both glycan structure and abundance. Thus, protein structure constrains glycosylation, and the neural network enables predictive in silico glycosylation of uncharacterized or novel protein sequences and genetic variants.
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Affiliation(s)
- Benjamin P. Kellman
- Department of Pediatrics, University of California, San Diego, La Jolla, CA 92093, USA
- Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093, USA
- Bioinformatics and Systems Biology Graduate Program, University of California, San Diego, La Jolla, CA 92093, USA
- Augment Biologics, La Jolla, CA 92092
- Ragon Institute of MGH, MIT, and Harvard, Cambridge, MA, USA
| | - Julien Mariethoz
- Proteome Informatics Group, Swiss Institute of Bioinformatics, CH-1227 Geneva, Switzerland
| | - Yujie Zhang
- Department of Pediatrics, University of California, San Diego, La Jolla, CA 92093, USA
| | - Sigal Shaul
- Department of Pediatrics, University of California, San Diego, La Jolla, CA 92093, USA
- Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093, USA
| | - Mia Alteri
- Department of Pediatrics, University of California, San Diego, La Jolla, CA 92093, USA
- Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093, USA
| | - Daniel Sandoval
- Department of Cellular and Molecular Medicine, University of California, San Diego, La Jolla, CA 92093, USA
| | - Mia Jeffris
- Department of Pediatrics, University of California, San Diego, La Jolla, CA 92093, USA
- Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093, USA
| | - Erick Armingol
- Department of Pediatrics, University of California, San Diego, La Jolla, CA 92093, USA
- Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093, USA
- Bioinformatics and Systems Biology Graduate Program, University of California, San Diego, La Jolla, CA 92093, USA
| | - Bokan Bao
- Department of Pediatrics, University of California, San Diego, La Jolla, CA 92093, USA
- Bioinformatics and Systems Biology Graduate Program, University of California, San Diego, La Jolla, CA 92093, USA
| | - Frederique Lisacek
- Proteome Informatics Group, Swiss Institute of Bioinformatics, CH-1227 Geneva, Switzerland
- Computer Science Department & Section of Biology, University of Geneva, route de Drize 7, CH-1227, Geneva, Switzerland
| | - Daniel Bojar
- Wallenberg Centre for Molecular and Translational Medicine, University of Gothenburg, Gothenburg 41390, Sweden
- Department of Chemistry and Molecular Biology, University of Gothenburg, Gothenburg 41390, Sweden
| | - Nathan E. Lewis
- Department of Pediatrics, University of California, San Diego, La Jolla, CA 92093, USA
- Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093, USA
- Bioinformatics and Systems Biology Graduate Program, University of California, San Diego, La Jolla, CA 92093, USA
- Ragon Institute of MGH, MIT, and Harvard, Cambridge, MA, USA
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3
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Cummings RD. A periodic table of monosaccharides. Glycobiology 2024; 34:cwad088. [PMID: 37935401 DOI: 10.1093/glycob/cwad088] [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: 09/11/2023] [Revised: 10/23/2023] [Accepted: 10/31/2023] [Indexed: 11/09/2023] Open
Abstract
It is important to recognize the great diversity of monosaccharides commonly encountered in animals, plants, and microbes, as well as to organize them in a visually interesting style that also emphasizes their similarities and relatedness. This article discusses the nature of building blocks, monosaccharides, and monosaccharide derivatives-terms commonly used in discussing "glycomolecules" found in nature. To aid in awareness of monosaccharide diversity, here is presented a Periodic Table of Monosaccharides. The rationale is given for construction of the Table and the selection of 103 monosaccharides, which is largely based on those presented in the KEGG and SNFG websites of monosaccharides, and includes room to enlarge as new discoveries are made. The Table should have educational value and is intended to capture the attention and foster imagination of those not very familiar with glycosciences, and encourage researchers to delve deeper into this fascinating area.
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Affiliation(s)
- Richard D Cummings
- Department of Surgery, Beth Israel Deaconess Medical Center, Harvard Medical School, CLS 11087-3 Blackfan Circle, Boston, MA 02115, United States
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4
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Sobitan A, Gebremedhin B, Yao Q, Xie G, Gu X, Li J, Teng S. A Computational Approach: The Functional Effects of Thyroid Peroxidase Variants in Thyroid Cancer and Genetic Disorders. JCO Clin Cancer Inform 2024; 8:e2300140. [PMID: 38295322 PMCID: PMC10843385 DOI: 10.1200/cci.23.00140] [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: 07/24/2023] [Revised: 09/16/2023] [Accepted: 10/18/2023] [Indexed: 02/02/2024] Open
Abstract
PURPOSE Thyroid peroxidase (TPO) is essential for the synthesis of thyroid hormones. However, specific mutations render TPO antigenic and prone to autoimmune attacks leading to thyroid cancer, TPO deficiency, and congenital hypothyroidism (CH). Despite technological advancement, most experimental procedures cannot quickly identify the genetic causes of CH nor detect thyroid cancer in the early stages. METHODS We performed saturated computational mutagenesis to calculate the folding energy changes (∆∆G) caused by missense mutations and analyzed the mutations involved in post-translational modifications (PTMs). RESULTS Our results showed that the functional important missense mutations occurred in the heme peroxidase domain. Through computational saturation mutagenesis, we identified the TPO mutations in G393 and G348 affecting protein stability and PTMs. Our folding energy calculations revealed that seven of nine somatic thyroid cancer mutations destabilized TPO. CONCLUSION These findings highlight the impact of these specific mutations on TPO stability, linking them to thyroid cancer and other genetic thyroid-related disorders. Our results show that computational mutagenesis of proteins provides a quick insight into rare mutations causing Mendelian disorders and cancers in humans.
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Affiliation(s)
| | | | - Qiaobin Yao
- Department of Biology, Howard University, Washington, DC
| | - Guiqin Xie
- Department of Oral Pathology, Howard University, Washington, DC
| | - Xinbin Gu
- Department of Oral Pathology, Howard University, Washington, DC
| | - Jiang Li
- Department of Electrical Engineering and Computer Science, Howard University, Washington, DC
| | - Shaolei Teng
- Department of Biology, Howard University, Washington, DC
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5
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Vora J, Navelkar R, Vijay-Shanker K, Edwards N, Martinez K, Ding X, Wang T, Su P, Ross K, Lisacek F, Hayes C, Kahsay R, Ranzinger R, Tiemeyer M, Mazumder R. The Glycan Structure Dictionary-a dictionary describing commonly used glycan structure terms. Glycobiology 2023; 33:354-357. [PMID: 36799723 PMCID: PMC10243773 DOI: 10.1093/glycob/cwad014] [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: 02/10/2022] [Revised: 01/28/2023] [Accepted: 02/08/2023] [Indexed: 02/18/2023] Open
Abstract
Recent technological advances in glycobiology have resulted in a large influx of data and the publication of many papers describing discoveries in glycoscience. However, the terms used in describing glycan structural features are not standardized, making it difficult to harmonize data across biomolecular databases, hampering the harvesting of information across studies and hindering text mining and curation efforts. To address this shortcoming, the Glycan Structure Dictionary has been developed as a reference dictionary to provide a standardized list of widely used glycan terms that can help in the curation and mapping of glycan structures described in publications. Currently, the dictionary has 190 glycan structure terms with 297 synonyms linked to 3,332 publications. For a term to be included in the dictionary, it must be present in at least 2 peer-reviewed publications. Synonyms, annotations, and cross-references to GlyTouCan, GlycoMotif, and other relevant databases and resources are also provided when available. The purpose of this effort is to facilitate biocuration, assist in the development of text mining tools, improve the harmonization of search, and browse capabilities in glycoinformatics resources and help to map glycan structures to function and disease. It is also expected that authors will use these terms to describe glycan structures in their manuscripts over time. A mechanism is also provided for researchers to submit terms for potential incorporation. The dictionary is available at https://wiki.glygen.org/Glycan_structure_dictionary.
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Affiliation(s)
- Jeet Vora
- Department of Biochemistry & Molecular Medicine, The George Washington School of Medicine and Health Sciences, 2300 I Street NW, Washington, DC 20037, USA
| | - Rahi Navelkar
- Department of Biochemistry & Molecular Medicine, The George Washington School of Medicine and Health Sciences, 2300 I Street NW, Washington, DC 20037, USA
| | - K Vijay-Shanker
- Department of Computer and Information Science, University of Delaware, Smith Hall, 18 Amstel Ave Newark, DE 19716, USA
| | - Nathan Edwards
- Department of Biochemistry and Molecular & Cellular Biology, Georgetown University, Washington, 3900 Reservoir Rd NW #337, DC 20007, USA
| | - Karina Martinez
- Department of Biochemistry & Molecular Medicine, The George Washington School of Medicine and Health Sciences, 2300 I Street NW, Washington, DC 20037, USA
| | - Xiying Ding
- Department of Biochemistry & Molecular Medicine, The George Washington School of Medicine and Health Sciences, 2300 I Street NW, Washington, DC 20037, USA
| | - Tianyi Wang
- Department of Biochemistry & Molecular Medicine, The George Washington School of Medicine and Health Sciences, 2300 I Street NW, Washington, DC 20037, USA
| | - Peng Su
- Department of Computer and Information Science, University of Delaware, Smith Hall, 18 Amstel Ave Newark, DE 19716, USA
| | - Karen Ross
- Department of Biochemistry and Molecular & Cellular Biology, Georgetown University, Washington, 3900 Reservoir Rd NW #337, DC 20007, USA
| | - Frederique Lisacek
- University of Geneva and Swiss Institute of Bioinformatics, CUI - 7, route de Drize, Geneva 1211, Switzerland
| | - Catherine Hayes
- University of Geneva and Swiss Institute of Bioinformatics, CUI - 7, route de Drize, Geneva 1211, Switzerland
| | - Robel Kahsay
- Department of Biochemistry & Molecular Medicine, The George Washington School of Medicine and Health Sciences, 2300 I Street NW, Washington, DC 20037, USA
| | - Rene Ranzinger
- Complex Carbohydrate Research Center, The University of Georgia, 315 Riverbend Rd, Athens, GA 30602, USA
| | - Michael Tiemeyer
- Complex Carbohydrate Research Center, The University of Georgia, 315 Riverbend Rd, Athens, GA 30602, USA
| | - Raja Mazumder
- Department of Biochemistry & Molecular Medicine, The George Washington School of Medicine and Health Sciences, 2300 I Street NW, Washington, DC 20037, USA
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6
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Groth T, Diehl AD, Gunawan R, Neelamegham S. GlycoEnzOnto: a GlycoEnzyme pathway and molecular function ontology. Bioinformatics 2022; 38:5413-5420. [PMID: 36282863 PMCID: PMC9750110 DOI: 10.1093/bioinformatics/btac704] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2022] [Revised: 08/22/2022] [Accepted: 10/24/2022] [Indexed: 12/25/2022] Open
Abstract
MOTIVATION The 'glycoEnzymes' include a set of proteins having related enzymatic, metabolic, transport, structural and cofactor functions. Currently, there is no established ontology to describe glycoEnzyme properties and to relate them to glycan biosynthesis pathways. RESULTS We present GlycoEnzOnto, an ontology describing 403 human glycoEnzymes curated along 139 glycosylation pathways, 134 molecular functions and 22 cellular compartments. The pathways described regulate nucleotide-sugar metabolism, glycosyl-substrate/donor transport, glycan biosynthesis and degradation. The role of each enzyme in the glycosylation initiation, elongation/branching and capping/termination phases is described. IUPAC linear strings present systematic human/machine-readable descriptions of individual reaction steps and enable automated knowledge-based curation of biochemical networks. All GlycoEnzOnto knowledge is integrated with the Gene Ontology biological processes. GlycoEnzOnto enables improved transcript overrepresentation analyses and glycosylation pathway identification compared to other available schema, e.g. KEGG and Reactome. Overall, GlycoEnzOnto represents a holistic glycoinformatics resource for systems-level analyses. AVAILABILITY AND IMPLEMENTATION https://github.com/neel-lab/GlycoEnzOnto. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Theodore Groth
- Department of Chemical and Biological Engineering, University at Buffalo, State University of New York, Buffalo, NY 14260, USA
| | - Alexander D Diehl
- Department of Biomedical Informatics, University at Buffalo, State University of New York, Buffalo, NY 14260, USA
| | - Rudiyanto Gunawan
- Department of Chemical and Biological Engineering, University at Buffalo, State University of New York, Buffalo, NY 14260, USA
| | - Sriram Neelamegham
- Department of Chemical and Biological Engineering, University at Buffalo, State University of New York, Buffalo, NY 14260, USA
- Department of Biomedical Engineering, University at Buffalo, State University of New York, Buffalo, NY 14260, USA
- Department of Medicine, University at Buffalo, State University of New York, Buffalo, NY 14260, USA
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7
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Lyman DF, Bell A, Black A, Dingerdissen H, Cauley E, Gogate N, Liu D, Joseph A, Kahsay R, Crichton DJ, Mehta A, Mazumder R. Modeling and integration of N-glycan biomarkers in a comprehensive biomarker data model. Glycobiology 2022; 32:855-870. [PMID: 35925813 PMCID: PMC9487899 DOI: 10.1093/glycob/cwac046] [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: 05/26/2021] [Revised: 06/30/2022] [Accepted: 07/04/2022] [Indexed: 11/13/2022] Open
Abstract
Molecular biomarkers measure discrete components of biological processes that can contribute to disorders when impaired. Great interest exists in discovering early cancer biomarkers to improve outcomes. Biomarkers represented in a standardized data model, integrated with multi-omics data, may improve understanding and use of novel biomarkers such as glycans and glycoconjugates. Among altered components in tumorigenesis, N-glycans exhibit substantial biomarker potential, when analyzed with their protein carriers. However, such data are distributed across publications and databases of diverse formats, which hampers their use in research and clinical application. Mass spectrometry measures of fifty N-glycans, on seven serum proteins in liver disease, were integrated (as a panel) into a cancer biomarker data model, providing a unique identifier, standard nomenclature, links to glycan resources, and accession and ontology annotations to standard protein, gene, disease, and biomarker information. Data provenance was documented with a standardized FDA-supported BioCompute Object. Using the biomarker data model allows capture of granular information, such as glycans with different levels of abundance in cirrhosis, hepatocellular carcinoma, and transplant groups. Such representation in a standardized data model harmonizes glycomics data in a unified framework, making glycan-protein biomarker data exploration more available to investigators and to other data resources. The biomarker data model we describe can be used by researchers to describe their novel glycan and glycoconjugate biomarkers, can integrate N-glycan biomarker data with multi-source biomedical data, and can foster discovery and insight within a unified data framework for glycan biomarker representation thereby making the data FAIR (Findable, Accessible, Interoperable, Reusable) (https://www.go-fair.org/fair-principles/).
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Affiliation(s)
- Daniel F Lyman
- The Department of Biochemistry & Molecular Medicine, The George Washington University Medical Center, Washington, DC 20037, United States of America
| | - Amanda Bell
- The Department of Biochemistry & Molecular Medicine, The George Washington University Medical Center, Washington, DC 20037, United States of America
| | - Alyson Black
- The Department of Cell & Molecular Pharmacology, The Medical University of South Carolina, Charleston, SC, 29403, United States of America
| | - Hayley Dingerdissen
- The Department of Biochemistry & Molecular Medicine, The George Washington University Medical Center, Washington, DC 20037, United States of America
| | - Edmund Cauley
- The Department of Biochemistry & Molecular Medicine, The George Washington University Medical Center, Washington, DC 20037, United States of America.,The McCormick Genomic and Proteomic Center, The George Washington University, Washington, DC 20037, United States of America
| | - Nikhita Gogate
- The Department of Biochemistry & Molecular Medicine, The George Washington University Medical Center, Washington, DC 20037, United States of America
| | - David Liu
- NASA Jet Propulsion Laboratory, Pasadena, CA 91109, United States of America
| | - Ashia Joseph
- The Department of Biochemistry & Molecular Medicine, The George Washington University Medical Center, Washington, DC 20037, United States of America
| | - Robel Kahsay
- The Department of Biochemistry & Molecular Medicine, The George Washington University Medical Center, Washington, DC 20037, United States of America
| | - Daniel J Crichton
- NASA Jet Propulsion Laboratory, Pasadena, CA 91109, United States of America
| | - Anand Mehta
- The Department of Cell & Molecular Pharmacology, The Medical University of South Carolina, Charleston, SC, 29403, United States of America
| | - Raja Mazumder
- The Department of Biochemistry & Molecular Medicine, The George Washington University Medical Center, Washington, DC 20037, United States of America.,The McCormick Genomic and Proteomic Center, The George Washington University, Washington, DC 20037, United States of America
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8
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Toukach PV, Shirkovskaya AI. Carbohydrate Structure Database and Other Glycan Databases as an Important Element of Glycoinformatics. RUSSIAN JOURNAL OF BIOORGANIC CHEMISTRY 2022. [DOI: 10.1134/s1068162022030190] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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9
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Frey LJ. Informatics Ecosystems to Advance the Biology of Glycans. Methods Mol Biol 2022; 2303:655-673. [PMID: 34626414 DOI: 10.1007/978-1-0716-1398-6_50] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Glycomics researchers have identified the need for integrated database systems for collecting glycomics information in a consistent format. The goal is to create a resource for knowledge discovery and dissemination to wider research communities. This has the potential and has exhibited initial success, to extend the research community to include biologists, clinicians, chemists, and computer scientists. This chapter discusses the technology and approach needed to create integrated data resources and informatics ecosystems to empower the broader community to leverage extant glycomics data. The focus is on glycosaminoglycan (GAGs) and proteoglycan research, but the approach can be generalized. The methods described span the development of glycomics standards from CarbBank to Glyco Connection Tables. Integrated data sets provide a foundation for novel methods of analysis such as machine learning and deep learning for knowledge discovery. The implications of predictive analysis are examined in relation to disease biomarker to expand the target audience of GAG and proteoglycan research.
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Affiliation(s)
- Lewis J Frey
- Department of Public Health Sciences, College of Medicine, Medical University of South Carolina, Charleston, SC, USA.
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10
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Communicating regulatory high-throughput sequencing data using BioCompute Objects. Drug Discov Today 2022; 27:1108-1114. [DOI: 10.1016/j.drudis.2022.01.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2021] [Revised: 08/20/2021] [Accepted: 01/18/2022] [Indexed: 11/23/2022]
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11
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Wulff-Fuentes E, Berendt RR, Massman L, Danner L, Malard F, Vora J, Kahsay R, Olivier-Van Stichelen S. The human O-GlcNAcome database and meta-analysis. Sci Data 2021; 8:25. [PMID: 33479245 PMCID: PMC7820439 DOI: 10.1038/s41597-021-00810-4] [Citation(s) in RCA: 121] [Impact Index Per Article: 40.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2020] [Accepted: 01/05/2021] [Indexed: 02/06/2023] Open
Abstract
Over the past 35 years, ~1700 articles have characterized protein O-GlcNAcylation. Found in almost all living organisms, this post-translational modification of serine and threonine residues is highly conserved and key to biological processes. With half of the primary research articles using human models, the O-GlcNAcome recently reached a milestone of 5000 human proteins identified. Herein, we provide an extensive inventory of human O-GlcNAcylated proteins, their O-GlcNAc sites, identification methods, and corresponding references ( www.oglcnac.mcw.edu ). In the absence of a comprehensive online resource for O-GlcNAcylated proteins, this list serves as the only database of O-GlcNAcylated proteins. Based on the thorough analysis of the amino acid sequence surrounding 7002 O-GlcNAc sites, we progress toward a more robust semi-consensus sequence for O-GlcNAcylation. Moreover, we offer a comprehensive meta-analysis of human O-GlcNAcylated proteins for protein domains, cellular and tissue distribution, and pathways in health and diseases, reinforcing that O-GlcNAcylation is a master regulator of cell signaling, equal to the widely studied phosphorylation.
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Affiliation(s)
| | - Rex R Berendt
- Department of Biochemistry, Medical College of Wisconsin, Milwaukee, USA
| | - Logan Massman
- Department of Biochemistry, Medical College of Wisconsin, Milwaukee, USA
| | - Laura Danner
- Department of Biochemistry, Medical College of Wisconsin, Milwaukee, USA
| | - Florian Malard
- Department of Biochemistry, Medical College of Wisconsin, Milwaukee, USA
| | - Jeet Vora
- Department of Biochemistry & Molecular Medicine, The George Washington School of Medicine and Health Sciences, Washington, DC, 20052, USA
| | - Robel Kahsay
- Department of Biochemistry & Molecular Medicine, The George Washington School of Medicine and Health Sciences, Washington, DC, 20052, USA
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12
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Rosenbalm KE, Tiemeyer M, Wells L, Aoki K, Zhao P. Glycomics-informed glycoproteomic analysis of site-specific glycosylation for SARS-CoV-2 spike protein. STAR Protoc 2020; 1:100214. [PMID: 33377107 PMCID: PMC7757665 DOI: 10.1016/j.xpro.2020.100214] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022] Open
Abstract
This protocol describes an integrated approach for analyzing site-specific N- and O-linked glycosylation of SARS-CoV-2 spike protein by mass spectrometry. Glycoproteomics analyzes intact glycopeptides to examine site-specific microheterogeneity of glycoproteins. Glycomics provides structural characterization on any glycan assignments by glycoproteomics. This procedure can be modified and applied to a variety of N- and/or O-linked glycoproteins. Combined with bioinformatics, the glycomics-informed glycoproteomics may be useful in generating 3D molecular dynamics simulations of certain glycoproteins alone or interacting with one another. For complete details on the use and execution of this protocol, please refer to Zhao et al. (2020).
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Affiliation(s)
- Katelyn E. Rosenbalm
- Complex Carbohydrate Research Center, University of Georgia, Athens, GA 30602, USA
- Department of Biochemistry and Molecular Biology, University of Georgia, Athens, GA 30602, USA
| | - Michael Tiemeyer
- Complex Carbohydrate Research Center, University of Georgia, Athens, GA 30602, USA
- Department of Biochemistry and Molecular Biology, University of Georgia, Athens, GA 30602, USA
| | - Lance Wells
- Complex Carbohydrate Research Center, University of Georgia, Athens, GA 30602, USA
- Department of Biochemistry and Molecular Biology, University of Georgia, Athens, GA 30602, USA
- Department of Chemistry, University of Georgia, Athens, GA 30602, USA
| | - Kazuhiro Aoki
- Complex Carbohydrate Research Center, University of Georgia, Athens, GA 30602, USA
| | - Peng Zhao
- Complex Carbohydrate Research Center, University of Georgia, Athens, GA 30602, USA
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13
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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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14
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Zhao P, Praissman JL, Grant OC, Cai Y, Xiao T, Rosenbalm KE, Aoki K, Kellman BP, Bridger R, Barouch DH, Brindley MA, Lewis NE, Tiemeyer M, Chen B, Woods RJ, Wells L. Virus-Receptor Interactions of Glycosylated SARS-CoV-2 Spike and Human ACE2 Receptor. Cell Host Microbe 2020; 28:586-601.e6. [PMID: 32841605 PMCID: PMC7443692 DOI: 10.1016/j.chom.2020.08.004] [Citation(s) in RCA: 289] [Impact Index Per Article: 72.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2020] [Revised: 07/22/2020] [Accepted: 08/10/2020] [Indexed: 12/13/2022]
Abstract
The SARS-CoV-2 betacoronavirus uses its highly glycosylated trimeric Spike protein to bind to the cell surface receptor angiotensin converting enzyme 2 (ACE2) glycoprotein and facilitate host cell entry. We utilized glycomics-informed glycoproteomics to characterize site-specific microheterogeneity of glycosylation for a recombinant trimer Spike mimetic immunogen and for a soluble version of human ACE2. We combined this information with bioinformatics analyses of natural variants and with existing 3D structures of both glycoproteins to generate molecular dynamics simulations of each glycoprotein both alone and interacting with one another. Our results highlight roles for glycans in sterically masking polypeptide epitopes and directly modulating Spike-ACE2 interactions. Furthermore, our results illustrate the impact of viral evolution and divergence on Spike glycosylation, as well as the influence of natural variants on ACE2 receptor glycosylation. Taken together, these data can facilitate immunogen design to achieve antibody neutralization and inform therapeutic strategies to inhibit viral infection.
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Affiliation(s)
- Peng Zhao
- Complex Carbohydrate Research Center, Department of Biochemistry and Molecular Biology, and Department of Chemistry, University of Georgia, Athens, GA 30602, USA
| | - Jeremy L Praissman
- Complex Carbohydrate Research Center, Department of Biochemistry and Molecular Biology, and Department of Chemistry, University of Georgia, Athens, GA 30602, USA
| | - Oliver C Grant
- Complex Carbohydrate Research Center, Department of Biochemistry and Molecular Biology, and Department of Chemistry, University of Georgia, Athens, GA 30602, USA
| | - Yongfei Cai
- Division of Molecular Medicine, Children's Hospital and Department of Pediatrics, Harvard Medical School, Boston, MA 02115, USA
| | - Tianshu Xiao
- Division of Molecular Medicine, Children's Hospital and Department of Pediatrics, Harvard Medical School, Boston, MA 02115, USA
| | - Katelyn E Rosenbalm
- Complex Carbohydrate Research Center, Department of Biochemistry and Molecular Biology, and Department of Chemistry, University of Georgia, Athens, GA 30602, USA
| | - Kazuhiro Aoki
- Complex Carbohydrate Research Center, Department of Biochemistry and Molecular Biology, and Department of Chemistry, University of Georgia, Athens, GA 30602, USA
| | - Benjamin P Kellman
- Departments of Pediatrics and Bioengineering, University of California, San Diego, La Jolla, CA 92093, USA
| | - Robert Bridger
- Complex Carbohydrate Research Center, Department of Biochemistry and Molecular Biology, and Department of Chemistry, University of Georgia, Athens, GA 30602, USA
| | - Dan H Barouch
- Center for Virology and Vaccine Research, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA 02215, USA
| | - Melinda A Brindley
- Department of Infectious Diseases, Department of Population Health, Center for Vaccines and Immunology, College of Veterinary Medicine, University of Georgia, Athens, GA 30602, USA
| | - Nathan E Lewis
- Departments of Pediatrics and Bioengineering, University of California, San Diego, La Jolla, CA 92093, USA; Novo Nordisk Foundation Center for Biosustainability at UC San Diego, La Jolla, CA 92093, USA
| | - Michael Tiemeyer
- Complex Carbohydrate Research Center, Department of Biochemistry and Molecular Biology, and Department of Chemistry, University of Georgia, Athens, GA 30602, USA
| | - Bing Chen
- Division of Molecular Medicine, Children's Hospital and Department of Pediatrics, Harvard Medical School, Boston, MA 02115, USA
| | - Robert J Woods
- Complex Carbohydrate Research Center, Department of Biochemistry and Molecular Biology, and Department of Chemistry, University of Georgia, Athens, GA 30602, USA.
| | - Lance Wells
- Complex Carbohydrate Research Center, Department of Biochemistry and Molecular Biology, and Department of Chemistry, University of Georgia, Athens, GA 30602, USA.
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15
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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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16
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Zhao P, Praissman JL, Grant OC, Cai Y, Xiao T, Rosenbalm KE, Aoki K, Kellman BP, Bridger R, Barouch DH, Brindley MA, Lewis NE, Tiemeyer M, Chen B, Woods RJ, Wells L. Virus-Receptor Interactions of Glycosylated SARS-CoV-2 Spike and Human ACE2 Receptor. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2020:2020.06.25.172403. [PMID: 32743578 PMCID: PMC7386495 DOI: 10.1101/2020.06.25.172403] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
The current COVID-19 pandemic is caused by the SARS-CoV-2 betacoronavirus, which utilizes its highly glycosylated trimeric Spike protein to bind to the cell surface receptor ACE2 glycoprotein and facilitate host cell entry. We utilized glycomics-informed glycoproteomics to characterize site-specific microheterogeneity of glycosylation for a recombinant trimer Spike mimetic immunogen and for a soluble version of human ACE2. We combined this information with bioinformatic analyses of natural variants and with existing 3D-structures of both glycoproteins to generate molecular dynamics simulations of each glycoprotein alone and interacting with one another. Our results highlight roles for glycans in sterically masking polypeptide epitopes and directly modulating Spike-ACE2 interactions. Furthermore, our results illustrate the impact of viral evolution and divergence on Spike glycosylation, as well as the influence of natural variants on ACE2 receptor glycosylation that, taken together, can facilitate immunogen design to achieve antibody neutralization and inform therapeutic strategies to inhibit viral infection.
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Affiliation(s)
- Peng Zhao
- Complex Carbohydrate Research Center, Department of Biochemistry and Molecular Biology, and Department of Chemistry, University of Georgia, Athens, Georgia, 30602, USA
| | - Jeremy L. Praissman
- Complex Carbohydrate Research Center, Department of Biochemistry and Molecular Biology, and Department of Chemistry, University of Georgia, Athens, Georgia, 30602, USA
| | - Oliver C. Grant
- Complex Carbohydrate Research Center, Department of Biochemistry and Molecular Biology, and Department of Chemistry, University of Georgia, Athens, Georgia, 30602, USA
| | - Yongfei Cai
- Division of Molecular Medicine, Children’s Hospital and Department of Pediatrics, Harvard Medical School, Boston, Massachusetts, 02115, USA
| | - Tianshu Xiao
- Division of Molecular Medicine, Children’s Hospital and Department of Pediatrics, Harvard Medical School, Boston, Massachusetts, 02115, USA
| | - Katelyn E. Rosenbalm
- Complex Carbohydrate Research Center, Department of Biochemistry and Molecular Biology, and Department of Chemistry, University of Georgia, Athens, Georgia, 30602, USA
| | - Kazuhiro Aoki
- Complex Carbohydrate Research Center, Department of Biochemistry and Molecular Biology, and Department of Chemistry, University of Georgia, Athens, Georgia, 30602, USA
| | - Benjamin P. Kellman
- Departments of Pediatrics and Bioengineering, University of California, San Diego, La Jolla, California, 92093, USA
| | - Robert Bridger
- Complex Carbohydrate Research Center, Department of Biochemistry and Molecular Biology, and Department of Chemistry, University of Georgia, Athens, Georgia, 30602, USA
| | - Dan H. Barouch
- Center for Virology and Vaccine Research, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts, 02215, USA
| | - Melinda A. Brindley
- Department of Infectious Diseases, Department of Population Health, Center for Vaccines and Immunology, College of Veterinary Medicine, University of Georgia, Athens, GA 30602, USA
| | - Nathan E. Lewis
- Departments of Pediatrics and Bioengineering, University of California, San Diego, La Jolla, California, 92093, USA
- Novo Nordisk Foundation Center for Biosustainability at UC San Diego, La Jolla, California, 92093, USA
| | - Michael Tiemeyer
- Complex Carbohydrate Research Center, Department of Biochemistry and Molecular Biology, and Department of Chemistry, University of Georgia, Athens, Georgia, 30602, USA
| | - Bing Chen
- Division of Molecular Medicine, Children’s Hospital and Department of Pediatrics, Harvard Medical School, Boston, Massachusetts, 02115, USA
| | - Robert J. Woods
- Complex Carbohydrate Research Center, Department of Biochemistry and Molecular Biology, and Department of Chemistry, University of Georgia, Athens, Georgia, 30602, USA
| | - Lance Wells
- Complex Carbohydrate Research Center, Department of Biochemistry and Molecular Biology, and Department of Chemistry, University of Georgia, Athens, Georgia, 30602, USA
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