1
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Osterne VJS, De Sloover G, Van Damme EJM. Revisiting legume lectins: Structural organization and carbohydrate-binding properties. Carbohydr Res 2024; 544:109241. [PMID: 39153325 DOI: 10.1016/j.carres.2024.109241] [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: 05/28/2024] [Revised: 07/18/2024] [Accepted: 08/13/2024] [Indexed: 08/19/2024]
Abstract
Legume lectins are a diverse family of carbohydrate-binding proteins that share significant similarities in their primary, secondary, and tertiary structures, yet exhibit remarkable variability in their quaternary structures and carbohydrate-binding specificities. The tertiary structure of legume lectins, characterized by a conserved β-sandwich fold, provides the scaffold for the formation of a carbohydrate-recognition domain (CRD) responsible for ligand binding. The structural basis for the binding is similar between members of the family, with key residues interacting with the sugar through hydrogen bonds, hydrophobic interactions, and van der Waals forces. Variability in substructures and residues within the CRD are responsible for the large array of specificities and enable legume lectins to recognize diverse sugar structures, while maintaining a consistent structural fold. Therefore, legume lectins can be classified into several specificity groups based on their preferred ligands, including mannose/glucose-specific, N-acetyl-d-galactosamine/galactose-specific, N-acetyl-d-glucosamine-specific, l-fucose-specific, and α-2,3 sialic acid-specific lectins. In this context, this review examined the structural aspects and carbohydrate-binding properties of representative legume lectins and their specific ligands in detail. Understanding the structure/binding relationships of lectins continues to provide valuable insights into their biological roles, while also assisting in the potential applications of these proteins in glycobiology, diagnostics, and therapeutics.
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Affiliation(s)
- Vinicius J S Osterne
- Laboratory of Biochemistry and Glycobiology, Department of Biotechnology, Ghent University, Proeftuinstraat 86, 9000, Ghent, Belgium
| | - Gilles De Sloover
- Laboratory of Biochemistry and Glycobiology, Department of Biotechnology, Ghent University, Proeftuinstraat 86, 9000, Ghent, Belgium
| | - Els J M Van Damme
- Laboratory of Biochemistry and Glycobiology, Department of Biotechnology, Ghent University, Proeftuinstraat 86, 9000, Ghent, Belgium.
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2
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Chittum JE, Thompson A, Desai UR. Glycosaminoglycan microarrays for studying glycosaminoglycan-protein systems. Carbohydr Polym 2024; 335:122106. [PMID: 38616080 PMCID: PMC11032185 DOI: 10.1016/j.carbpol.2024.122106] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2024] [Revised: 03/25/2024] [Accepted: 03/26/2024] [Indexed: 04/16/2024]
Abstract
More than 3000 proteins are now known to bind to glycosaminoglycans (GAGs). Yet, GAG-protein systems are rather poorly understood in terms of selectivity of recognition, molecular mechanism of action, and translational promise. High-throughput screening (HTS) technologies are critically needed for studying GAG biology and developing GAG-based therapeutics. Microarrays, developed within the past two decades, have now improved to the point of being the preferred tool in the HTS of biomolecules. GAG microarrays, in which GAG sequences are immobilized on slides, while similar to other microarrays, have their own sets of challenges and considerations. GAG microarrays are rapidly becoming the first choice in studying GAG-protein systems. Here, we review different modalities and applications of GAG microarrays presented to date. We discuss advantages and disadvantages of this technology, explain covalent and non-covalent immobilization strategies using different chemically reactive groups, and present various assay formats for qualitative and quantitative interpretations, including selectivity screening, binding affinity studies, competitive binding studies etc. We also highlight recent advances in implementing this technology, cataloging of data, and project its future promise. Overall, the technology of GAG microarray exhibits enormous potential of evolving into more than a mere screening tool for studying GAG - protein systems.
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Affiliation(s)
- John E Chittum
- Department of Medicinal Chemistry, School of Pharmacy, Virginia Commonwealth University, Richmond, VA 23298, United States of America; Institute for Structural Biology, Drug Discovery and Development, Virginia Commonwealth University, Richmond, VA 23219, United States of America
| | - Ally Thompson
- Department of Medicinal Chemistry, School of Pharmacy, Virginia Commonwealth University, Richmond, VA 23298, United States of America; Institute for Structural Biology, Drug Discovery and Development, Virginia Commonwealth University, Richmond, VA 23219, United States of America
| | - Umesh R Desai
- Department of Medicinal Chemistry, School of Pharmacy, Virginia Commonwealth University, Richmond, VA 23298, United States of America; Institute for Structural Biology, Drug Discovery and Development, Virginia Commonwealth University, Richmond, VA 23219, United States of America.
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3
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Lisacek F, Tiemeyer M, Mazumder R, Aoki-Kinoshita KF. Worldwide Glycoscience Informatics Infrastructure: The GlySpace Alliance. JACS AU 2023; 3:4-12. [PMID: 36711080 PMCID: PMC9875223 DOI: 10.1021/jacsau.2c00477] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/30/2022] [Revised: 11/04/2022] [Accepted: 11/07/2022] [Indexed: 06/18/2023]
Abstract
The GlySpace Alliance was formed in 2018 among the principal investigators of three major glycoscience portals: Glyco@Expasy, GlyCosmos, and GlyGen, representing Europe, Asia, and the United States, respectively. While each of these portals has its unique user interface, the aim is to provide the same basic data set of glycan-related omics data. These portals will be introduced with the aim to enable users to find their target information in the most efficient manner, in particular, in terms of the chemical structures of glycans and their functions.
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Affiliation(s)
- Frederique Lisacek
- Proteome
Informatics Group, SIB Swiss Institute of Bioinformatics, University of Geneva, Geneva CH-1227, Switzerland
- Computer
Science Department & Section of Biology, University of Geneva, Geneva CH-1227, Switzerland
| | - Michael Tiemeyer
- Complex
Carbohydrate Research Center, University
of Georgia, Athens, Georgia 30602, United States
| | - Raja Mazumder
- George
Washington University, Washington, District of Columbia 20037, United States
| | - Kiyoko F. Aoki-Kinoshita
- Glycan
and Life Systems Integration Center (GaLSIC), Soka University, Hachioji, Tokyo 192-8577, Japan
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4
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Sasmal A, Khan N, Khedri Z, Kellman BP, Srivastava S, Verhagen A, Yu H, Bruntse AB, Diaz S, Varki N, Beddoe T, Paton AW, Paton JC, Chen X, Lewis NE, Varki A. Simple and practical sialoglycan encoding system reveals vast diversity in nature and identifies a universal sialoglycan-recognizing probe derived from AB5 toxin B subunits. Glycobiology 2022; 32:1101-1115. [PMID: 36048714 PMCID: PMC9680115 DOI: 10.1093/glycob/cwac057] [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: 04/04/2022] [Revised: 08/20/2022] [Accepted: 08/23/2022] [Indexed: 01/07/2023] Open
Abstract
Vertebrate sialic acids (Sias) display much diversity in modifications, linkages, and underlying glycans. Slide microarrays allow high-throughput explorations of sialoglycan-protein interactions. A microarray presenting ~150 structurally defined sialyltrisaccharides with various Sias linkages and modifications still poses challenges in planning, data sorting, visualization, and analysis. To address these issues, we devised a simple 9-digit code for sialyltrisaccharides with terminal Sias and underlying two monosaccharides assigned from the nonreducing end, with 3 digits assigning a monosaccharide, its modifications, and linkage. Calculations based on the encoding system reveal >113,000 likely linear sialyltrisaccharides in nature. Notably, a biantennary N-glycan with 2 terminal sialyltrisaccharides could thus have >1010 potential combinations and a triantennary N-glycan with 3 terminal sequences, >1015 potential combinations. While all possibilities likely do not exist in nature, sialoglycans encode enormous diversity. While glycomic approaches are used to probe such diverse sialomes, naturally occurring bacterial AB5 toxin B subunits are simpler tools to track the dynamic sialome in biological systems. Sialoglycan microarray was utilized to compare sialoglycan-recognizing bacterial toxin B subunits. Unlike the poor correlation between B subunits and species phylogeny, there is stronger correlation with Sia-epitope preferences. Further supporting this pattern, we report a B subunit (YenB) from Yersinia enterocolitica (broad host range) recognizing almost all sialoglycans in the microarray, including 4-O-acetylated-Sias not recognized by a Yersinia pestis orthologue (YpeB). Differential Sia-binding patterns were also observed with phylogenetically related B subunits from Escherichia coli (SubB), Salmonella Typhi (PltB), Salmonella Typhimurium (ArtB), extra-intestinal E.coli (EcPltB), Vibrio cholera (CtxB), and cholera family homologue of E. coli (EcxB).
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Affiliation(s)
- Aniruddha Sasmal
- Glycobiology Research and Training Center, University of California San Diego, La Jolla, CA 92093, USA
- Department of Cellular and Molecular Medicine, University of California San Diego, La Jolla, CA 92093, USA
| | - Naazneen Khan
- Glycobiology Research and Training Center, University of California San Diego, La Jolla, CA 92093, USA
- Department of Cellular and Molecular Medicine, University of California San Diego, La Jolla, CA 92093, USA
| | - Zahra Khedri
- Glycobiology Research and Training Center, University of California San Diego, La Jolla, CA 92093, USA
- Department of Cellular and Molecular Medicine, University of California San Diego, La Jolla, CA 92093, USA
| | - Benjamin P Kellman
- Department of Pediatrics, University of California San Diego, La Jolla, CA 92093, USA
| | - Saurabh Srivastava
- Glycobiology Research and Training Center, University of California San Diego, La Jolla, CA 92093, USA
- Department of Cellular and Molecular Medicine, University of California San Diego, La Jolla, CA 92093, USA
| | - Andrea Verhagen
- Glycobiology Research and Training Center, University of California San Diego, La Jolla, CA 92093, USA
- Department of Cellular and Molecular Medicine, University of California San Diego, La Jolla, CA 92093, USA
| | - Hai Yu
- Department of Chemistry, University of California Davis, CA 95616, USA
| | - Anders Bech Bruntse
- Department of Pediatrics, University of California San Diego, La Jolla, CA 92093, USA
| | - Sandra Diaz
- Glycobiology Research and Training Center, University of California San Diego, La Jolla, CA 92093, USA
- Department of Cellular and Molecular Medicine, University of California San Diego, La Jolla, CA 92093, USA
| | - Nissi Varki
- Glycobiology Research and Training Center, University of California San Diego, La Jolla, CA 92093, USA
- Department of Cellular and Molecular Medicine, University of California San Diego, La Jolla, CA 92093, USA
| | - Travis Beddoe
- Department of Animal, Plant and Soil Science and Centre for AgriBioscience, La Trobe University, Bundoora, Victoria 3086, Australia
| | - Adrienne W Paton
- Research Centre for Infectious Diseases, Department of Molecular and Biomedical Science, University of Adelaide, Adelaide, SA 5005, Australia
| | - James C Paton
- Research Centre for Infectious Diseases, Department of Molecular and Biomedical Science, University of Adelaide, Adelaide, SA 5005, Australia
| | - Xi Chen
- Department of Chemistry, University of California Davis, CA 95616, USA
| | - Nathan E Lewis
- Glycobiology Research and Training Center, University of California San Diego, La Jolla, CA 92093, USA
- Department of Pediatrics, University of California San Diego, La Jolla, CA 92093, USA
| | - Ajit Varki
- Glycobiology Research and Training Center, University of California San Diego, La Jolla, CA 92093, USA
- Department of Cellular and Molecular Medicine, University of California San Diego, La Jolla, CA 92093, USA
- Research Centre for Infectious Diseases, Department of Molecular and Biomedical Science, University of Adelaide, Adelaide, SA 5005, Australia
- Department of Medicine, University of California at San Diego, La Jolla, CA 92093, USA
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5
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Bojar D, Meche L, Meng G, Eng W, Smith DF, Cummings RD, Mahal LK. A Useful Guide to Lectin Binding: Machine-Learning Directed Annotation of 57 Unique Lectin Specificities. ACS Chem Biol 2022; 17:2993-3012. [PMID: 35084820 PMCID: PMC9679999 DOI: 10.1021/acschembio.1c00689] [Citation(s) in RCA: 100] [Impact Index Per Article: 50.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Glycans are critical to every facet of biology and medicine, from viral infections to embryogenesis. Tools to study glycans are rapidly evolving; however, the majority of our knowledge is deeply dependent on binding by glycan binding proteins (e.g., lectins). The specificities of lectins, which are often naturally isolated proteins, have not been well-defined, making it difficult to leverage their full potential for glycan analysis. Herein, we use a combination of machine learning algorithms and expert annotation to define lectin specificity for this important probe set. Our analysis uses comprehensive glycan microarray analysis of commercially available lectins we obtained using version 5.0 of the Consortium for Functional Glycomics glycan microarray (CFGv5). This data set was made public in 2011. We report the creation of this data set and its use in large-scale evaluation of lectin-glycan binding behaviors. Our motif analysis was performed by integrating 68 manually defined glycan features with systematic probing of computational rules for significant binding motifs using mono- and disaccharides and linkages. Combining machine learning with manual annotation, we create a detailed interpretation of glycan-binding specificity for 57 unique lectins, categorized by their major binding motifs: mannose, complex-type N-glycan, O-glycan, fucose, sialic acid and sulfate, GlcNAc and chitin, Gal and LacNAc, and GalNAc. Our work provides fresh insights into the complex binding features of commercially available lectins in current use, providing a critical guide to these important reagents.
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Affiliation(s)
- Daniel Bojar
- Department
of Chemistry and Molecular Biology and Wallenberg Centre for Molecular
and Translational Medicine, University of
Gothenburg, Gothenburg, Sweden 405 30
| | - Lawrence Meche
- Biomedical
Chemistry Institute, Department of Chemistry, New York University, 100 Washington Square East, Room 1001, New
York, New York 10003, United States
| | - Guanmin Meng
- Department
of Chemistry, University of Alberta, Edmonton, Canada, T6G 2G2
| | - William Eng
- Biomedical
Chemistry Institute, Department of Chemistry, New York University, 100 Washington Square East, Room 1001, New
York, New York 10003, United States
| | - David F. Smith
- Department
of Biochemistry, Glycomics Center, School of Medicine, Emory University, Atlanta, Georgia 30322, United States
| | - Richard D. Cummings
- Department
of Surgery, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts 02115, United States
| | - Lara K. Mahal
- Biomedical
Chemistry Institute, Department of Chemistry, New York University, 100 Washington Square East, Room 1001, New
York, New York 10003, United States,Department
of Chemistry, University of Alberta, Edmonton, Canada, T6G 2G2,E-mail:
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6
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Carpenter EJ, Seth S, Yue N, Greiner R, Derda R. GlyNet: a multi-task neural network for predicting protein-glycan interactions. Chem Sci 2022; 13:6669-6686. [PMID: 35756507 PMCID: PMC9172296 DOI: 10.1039/d1sc05681f] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2021] [Accepted: 05/02/2022] [Indexed: 12/14/2022] Open
Abstract
Advances in diagnostics, therapeutics, vaccines, transfusion, and organ transplantation build on a fundamental understanding of glycan–protein interactions. To aid this, we developed GlyNet, a model that accurately predicts interactions (relative binding strengths) between mammalian glycans and 352 glycan-binding proteins, many at multiple concentrations. For each glycan input, our model produces 1257 outputs, each representing the relative interaction strength between the input glycan and a particular protein sample. GlyNet learns these continuous values using relative fluorescence units (RFUs) measured on 599 glycans in the Consortium for Functional Glycomics glycan arrays and extrapolates these to RFUs from additional, untested glycans. GlyNet's output of continuous values provides more detailed results than the standard binary classification models. After incorporating a simple threshold to transform such continuous outputs the resulting GlyNet classifier outperforms those standard classifiers. GlyNet is the first multi-output regression model for predicting protein–glycan interactions and serves as an important benchmark, facilitating development of quantitative computational glycobiology. GlyNet, a neural net model of glycan-protein binding strengths. Given a glycan it outputs binding to each of several protein samples. Reproducing glycan array data, it extrapolates the binding of untested glycans against the protein samples.![]()
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Affiliation(s)
- Eric J Carpenter
- Department of Chemistry, University of Alberta Edmonton Alberta Canada
| | - Shaurya Seth
- Department of Chemistry, University of Alberta Edmonton Alberta Canada
| | - Noel Yue
- Department of Chemistry, University of Alberta Edmonton Alberta Canada
| | - Russell Greiner
- Department of Computing Science, University of Alberta Edmonton Alberta Canada.,Alberta Machine Intelligence Institute (AMII) Edmonton Alberta Canada
| | - Ratmir Derda
- Department of Chemistry, University of Alberta Edmonton Alberta Canada
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7
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Akune Y, Arpinar S, Silva LM, Palma AS, Tajadura-Ortega V, Aoki-Kinoshita KF, Ranzinger R, Liu Y, Feizi T. OUP accepted manuscript. Glycobiology 2022; 32:552-555. [PMID: 35352122 PMCID: PMC9191619 DOI: 10.1093/glycob/cwac018] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2021] [Revised: 03/07/2022] [Accepted: 03/08/2022] [Indexed: 11/25/2022] Open
Abstract
Glycan microarrays are essential tools in glycobiology and are being widely used for assignment of glycan ligands in diverse glycan recognition systems. We have developed a new software, called Carbohydrate microArray Analysis and Reporting Tool (CarbArrayART), to address the need for a distributable application for glycan microarray data management. The main features of CarbArrayART include: (i) Storage of quantified array data from different array layouts with scan data and array-specific metadata, such as lists of arrayed glycans, array geometry, information on glycan-binding samples, and experimental protocols. (ii) Presentation of microarray data as charts, tables, and heatmaps derived from the average fluorescence intensity values that are calculated based on the imaging scan data and array geometry, as well as filtering and sorting functions according to monosaccharide content and glycan sequences. (iii) Data export for reporting in Word, PDF, and Excel formats, together with metadata that are compliant with the guidelines of MIRAGE (Minimum Information Required for A Glycomics Experiment). CarbArrayART is designed for routine use in recording, storage, and management of any slide-based glycan microarray experiment. In conjunction with the MIRAGE guidelines, CarbArrayART addresses issues that are critical for glycobiology, namely, clarity of data for evaluation of reproducibility and validity.
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Affiliation(s)
- Yukie Akune
- Corresponding author: The Glycosciences Laboratory, Burlington Danes Building Room 509, Du Cane Road, London W12 0NN, United Kingdom.
| | - Sena Arpinar
- Complex Carbohydrate Research Center, University of Georgia, 315 Riverbend Rd, Athens, GA 30602, United States
| | - Lisete M Silva
- Glycosciences Laboratory, Department of Metabolism, Digestion and Reproduction, Imperial College, Du Cane Road, London W12 0NN, United Kingdom
- LAQV-REQUIMTE, Department of Chemistry, University of Aveiro, 3810-193 Aveiro, Portugal
| | - Angelina S Palma
- UCIBIO, Applied Molecular Biosciences Unit, Department of Chemistry, School of Science and Technology, NOVA University Lisbon, 2819-516 Caparica, Portugal
- Associate Laboratory i4HB-Institute for Health and Bioeconomy, School of Science and Technology, NOVA University Lisbon, Lisbon, 2819-516 Caparica, Portugal
| | - Virginia Tajadura-Ortega
- Glycosciences Laboratory, Department of Metabolism, Digestion and Reproduction, Imperial College, Du Cane Road, London W12 0NN, United Kingdom
| | - Kiyoko F Aoki-Kinoshita
- Glycan and Life Systems Integration Center (GaLSIC), Soka University, 1-236 Tangi-machi, Hachioji, Tokyo 192-8577, Japan
| | - René Ranzinger
- Complex Carbohydrate Research Center, University of Georgia, 315 Riverbend Rd, Athens, GA 30602, United States
| | - Yan Liu
- Glycosciences Laboratory, Department of Metabolism, Digestion and Reproduction, Imperial College, Du Cane Road, London W12 0NN, United Kingdom
| | - Ten Feizi
- Glycosciences Laboratory, Department of Metabolism, Digestion and Reproduction, Imperial College, Du Cane Road, London W12 0NN, United Kingdom
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8
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Klamer ZL, Harris CM, Beirne JM, Kelly JE, Zhang J, Haab BB. OUP accepted manuscript. Glycobiology 2022; 32:679-690. [PMID: 35352123 PMCID: PMC9280547 DOI: 10.1093/glycob/cwac022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2022] [Revised: 03/24/2022] [Accepted: 03/24/2022] [Indexed: 11/12/2022] Open
Abstract
Glycan arrays continue to be the primary resource for determining the glycan-binding specificity of proteins. The volume and diversity of glycan-array data are increasing, but no common method and resource exist to analyze, integrate, and use the available data. To meet this need, we developed a resource of analyzed glycan-array data called CarboGrove. Using the ability to process and interpret data from any type of glycan array, we populated the database with the results from 35 types of glycan arrays, 13 glycan families, 5 experimental methods, and 19 laboratories or companies. In meta-analyses of glycan-binding proteins, we observed glycan-binding specificities that were not uncovered from single sources. In addition, we confirmed the ability to efficiently optimize selections of glycan-binding proteins to be used in experiments for discriminating between closely related motifs. Through descriptive reports and a programmatically accessible Application Programming Interface, CarboGrove yields unprecedented access to the wealth of glycan-array data being produced and powerful capabilities for both experimentalists and bioinformaticians.
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Affiliation(s)
- Zachary L Klamer
- Department of Cancer and Cell Biology, Van Andel Institute, 333 Bostwick Ave NE, Grand Rapids, MI 49503, United States
| | | | | | | | | | - Brian B Haab
- Corresponding author: Van Andel Institute, 333 Bostwick Ave NE, Grand Rapids, MI 49504, United States.
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9
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Bao B, Kellman BP, Chiang AWT, Zhang Y, Sorrentino JT, York AK, Mohammad MA, Haymond MW, Bode L, Lewis NE. Correcting for sparsity and interdependence in glycomics by accounting for glycan biosynthesis. Nat Commun 2021; 12:4988. [PMID: 34404781 PMCID: PMC8371009 DOI: 10.1038/s41467-021-25183-5] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2020] [Accepted: 07/27/2021] [Indexed: 11/20/2022] Open
Abstract
Glycans are fundamental cellular building blocks, involved in many organismal functions. Advances in glycomics are elucidating the essential roles of glycans. Still, it remains challenging to properly analyze large glycomics datasets, since the abundance of each glycan is dependent on many other glycans that share many intermediate biosynthetic steps. Furthermore, the overlap of measured glycans can be low across samples. We address these challenges with GlyCompare, a glycomic data analysis approach that accounts for shared biosynthetic steps for all measured glycans to correct for sparsity and non-independence in glycomics, which enables direct comparison of different glycoprofiles and increases statistical power. Using GlyCompare, we study diverse N-glycan profiles from glycoengineered erythropoietin. We obtain biologically meaningful clustering of mutant cell glycoprofiles and identify knockout-specific effects of fucosyltransferase mutants on tetra-antennary structures. We further analyze human milk oligosaccharide profiles and find mother’s fucosyltransferase-dependent secretor-status indirectly impact the sialylation. Finally, we apply our method on mucin-type O-glycans, gangliosides, and site-specific compositional glycosylation data to reveal tissues and disease-specific glycan presentations. Our substructure-oriented approach will enable researchers to take full advantage of the growing power and size of glycomics data. Glycomics can uncover important molecular changes but measured glycans are highly interconnected and incompatible with common statistical methods, introducing pitfalls during analysis. Here, the authors develop an approach to identify glycan dependencies across samples to facilitate comparative glycomics.
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Affiliation(s)
- Bokan Bao
- Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA.,Bioinformatics and Systems Biology Graduate Program, University of California, San Diego, La Jolla, CA, USA.,Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA
| | - Benjamin P Kellman
- Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA.,Bioinformatics and Systems Biology Graduate Program, University of California, San Diego, La Jolla, CA, USA.,Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA
| | - Austin W T Chiang
- Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA.,The Novo Nordisk Foundation Center for Biosustainability at the University of California, San Diego, La Jolla, CA, USA
| | - Yujie Zhang
- Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA
| | - James T Sorrentino
- Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA.,Bioinformatics and Systems Biology Graduate Program, University of California, San Diego, La Jolla, CA, USA.,Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA
| | - Austin K York
- Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA
| | - Mahmoud A Mohammad
- Department of Pediatrics, Children's Nutrition Research Center, US Department of Agriculture/Agricultural Research Service, Baylor College of Medicine, Houston, TX, USA
| | - Morey W Haymond
- Department of Pediatrics, Children's Nutrition Research Center, US Department of Agriculture/Agricultural Research Service, Baylor College of Medicine, Houston, TX, USA
| | - Lars Bode
- Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA
| | - Nathan E Lewis
- Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA. .,Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA. .,The Novo Nordisk Foundation Center for Biosustainability at the University of California, San Diego, La Jolla, CA, USA.
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10
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Klamer Z, Haab B. Combined Analysis of Multiple Glycan-Array Datasets: New Explorations of Protein-Glycan Interactions. Anal Chem 2021; 93:10925-10933. [PMID: 34319080 DOI: 10.1021/acs.analchem.1c01739] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
Glycan arrays are indispensable for learning about the specificities of glycan-binding proteins. Despite the abundance of available data, the current analysis methods do not have the ability to interpret and use the variety of data types and to integrate information across datasets. Here, we evaluated whether a novel, automated algorithm for glycan-array analysis could meet that need. We developed a regression-tree algorithm with simultaneous motif optimization and packaged it in software called MotifFinder. We applied the software to analyze data from eight different glycan-array platforms with widely divergent characteristics and observed an accurate analysis of each dataset. We then evaluated the feasibility and value of the combined analyses of multiple datasets. In an integrated analysis of datasets covering multiple lectin concentrations, the software determined approximate binding constants for distinct motifs and identified major differences between the motifs that were not apparent from single-concentration analyses. Furthermore, an integrated analysis of data sources with complementary sets of glycans produced broader views of lectin specificity than produced by the analysis of just one data source. MotifFinder, therefore, enables the optimal use of the expanding resource of the glycan-array data and promises to advance the studies of protein-glycan interactions.
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Affiliation(s)
- Zachary Klamer
- Van Andel Institute, 333 Bostwick NE, Grand Rapids, Michigan 49503, United States
| | - Brian Haab
- Van Andel Institute, 333 Bostwick NE, Grand Rapids, Michigan 49503, United States
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11
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Kellman BP, Lewis NE. Big-Data Glycomics: Tools to Connect Glycan Biosynthesis to Extracellular Communication. Trends Biochem Sci 2021; 46:284-300. [PMID: 33349503 PMCID: PMC7954846 DOI: 10.1016/j.tibs.2020.10.004] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2020] [Revised: 10/05/2020] [Accepted: 10/22/2020] [Indexed: 12/12/2022]
Abstract
Characteristically, cells must sense and respond to environmental cues. Despite the importance of cell-cell communication, our understanding remains limited and often lacks glycans. Glycans decorate proteins and cell membranes at the cell-environment interface, and modulate intercellular communication, from development to pathogenesis. Providing further challenges, glycan biosynthesis and cellular behavior are co-regulating systems. Here, we discuss how glycosylation contributes to extracellular responses and signaling. We further organize approaches for disentangling the roles of glycans in multicellular interactions using newly available datasets and tools, including glycan biosynthesis models, omics datasets, and systems-level analyses. Thus, emerging tools in big data analytics and systems biology are facilitating novel insights on glycans and their relationship with multicellular behavior.
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Affiliation(s)
- Benjamin P Kellman
- Department of Pediatrics, University of California San Diego School of Medicine, La Jolla, CA, USA; Department of Bioengineering, University of California San Diego School of Medicine, La Jolla, CA, USA; Bioinformatics and Systems Biology Program, University of California San Diego School of Medicine, La Jolla, CA, USA
| | - Nathan E Lewis
- Department of Pediatrics, University of California San Diego School of Medicine, La Jolla, CA, USA; Department of Bioengineering, University of California San Diego School of Medicine, La Jolla, CA, USA; Bioinformatics and Systems Biology Program, University of California San Diego School of Medicine, La Jolla, CA, USA; Novo Nordisk Foundation Center for Biosustainability at the University of California San Diego School of Medicine, La Jolla, CA, USA.
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Cao Y, Park SJ, Mehta AY, Cummings RD, Im W. GlyMDB: Glycan Microarray Database and analysis toolset. Bioinformatics 2020; 36:2438-2442. [PMID: 31841142 DOI: 10.1093/bioinformatics/btz934] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2019] [Revised: 11/04/2019] [Accepted: 12/11/2019] [Indexed: 01/31/2023] Open
Abstract
MOTIVATION Glycan microarrays are capable of illuminating the interactions of glycan-binding proteins (GBPs) against hundreds of defined glycan structures, and have revolutionized the investigations of protein-carbohydrate interactions underlying numerous critical biological activities. However, it is difficult to interpret microarray data and identify structural determinants promoting glycan binding to glycan-binding proteins due to the ambiguity in microarray fluorescence intensity and complexity in branched glycan structures. To facilitate analysis of glycan microarray data alongside protein structure, we have built the Glycan Microarray Database (GlyMDB), a web-based resource including a searchable database of glycan microarray samples and a toolset for data/structure analysis. RESULTS The current GlyMDB provides data visualization and glycan-binding motif discovery for 5203 glycan microarray samples collected from the Consortium for Functional Glycomics. The unique feature of GlyMDB is to link microarray data to PDB structures. The GlyMDB provides different options for database query, and allows users to upload their microarray data for analysis. After search or upload is complete, users can choose the criterion for binder versus non-binder classification. They can view the signal intensity graph including the binder/non-binder threshold followed by a list of glycan-binding motifs. One can also compare the fluorescence intensity data from two different microarray samples. A protein sequence-based search is performed using BLAST to match microarray data with all available PDB structures containing glycans. The glycan ligand information is displayed, and links are provided for structural visualization and redirection to other modules in GlycanStructure.ORG for further investigation of glycan-binding sites and glycan structures. AVAILABILITY AND IMPLEMENTATION http://www.glycanstructure.org/glymdb. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Yiwei Cao
- Departments of Biological Sciences and Bioengineering, Lehigh University, Bethlehem, PA 18015, USA
| | - Sang-Jun Park
- Departments of Biological Sciences and Bioengineering, Lehigh University, Bethlehem, PA 18015, USA
| | - Akul Y Mehta
- Department of Surgery, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA 02115, USA
| | - Richard D Cummings
- Department of Surgery, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA 02115, USA
| | - Wonpil Im
- Departments of Biological Sciences and Bioengineering, Lehigh University, Bethlehem, PA 18015, USA.,School of Computational Sciences, Korea Institute for Advanced Study, Seoul 02455, Republic of Korea
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13
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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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Mehta AY, Heimburg-Molinaro J, Cummings RD. Tools for generating and analyzing glycan microarray data. Beilstein J Org Chem 2020; 16:2260-2271. [PMID: 32983270 PMCID: PMC7492694 DOI: 10.3762/bjoc.16.187] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2020] [Accepted: 08/19/2020] [Indexed: 12/14/2022] Open
Abstract
Glycans are one of the major biological polymers found in the mammalian body. They play a vital role in a number of physiologic and pathologic conditions. Glycan microarrays allow a plethora of information to be obtained on protein–glycan binding interactions. In this review, we describe the intricacies of the generation of glycan microarray data and the experimental methods for studying binding. We highlight the importance of this knowledge before moving on to the data analysis. We then highlight a number of tools for the analysis of glycan microarray data such as data repositories, data visualization and manual analysis tools, automated analysis tools and structural informatics tools.
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Affiliation(s)
- Akul Y Mehta
- Department of Surgery, Beth Israel Deaconess Medical Center, National Center for Functional Glycomics, Harvard Medical School, Boston, MA, 02215, USA
| | - Jamie Heimburg-Molinaro
- Department of Surgery, Beth Israel Deaconess Medical Center, National Center for Functional Glycomics, Harvard Medical School, Boston, MA, 02215, USA
| | - Richard D Cummings
- Department of Surgery, Beth Israel Deaconess Medical Center, National Center for Functional Glycomics, Harvard Medical School, Boston, MA, 02215, USA
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Cipollo JF, Parsons LM. Glycomics and glycoproteomics of viruses: Mass spectrometry applications and insights toward structure-function relationships. MASS SPECTROMETRY REVIEWS 2020; 39:371-409. [PMID: 32350911 PMCID: PMC7318305 DOI: 10.1002/mas.21629] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/15/2019] [Revised: 04/01/2020] [Accepted: 04/05/2020] [Indexed: 05/21/2023]
Abstract
The advancement of viral glycomics has paralleled that of the mass spectrometry glycomics toolbox. In some regard the glycoproteins studied have provided the impetus for this advancement. Viral proteins are often highly glycosylated, especially those targeted by the host immune system. Glycosylation tends to be dynamic over time as viruses propagate in host populations leading to increased number of and/or "movement" of glycosylation sites in response to the immune system and other pressures. This relationship can lead to highly glycosylated, difficult to analyze glycoproteins that challenge the capabilities of modern mass spectrometry. In this review, we briefly discuss five general areas where glycosylation is important in the viral niche and how mass spectrometry has been used to reveal key information regarding structure-function relationships between viral glycoproteins and host cells. We describe the recent past and current glycomics toolbox used in these analyses and give examples of how the requirement to analyze these complex glycoproteins has provided the incentive for some advances seen in glycomics mass spectrometry. A general overview of viral glycomics, special cases, mass spectrometry methods and work-flows, informatics and complementary chemical techniques currently used are discussed. © 2020 The Authors. Mass Spectrometry Reviews published by John Wiley & Sons Ltd. Mass Spec Rev.
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Affiliation(s)
- John F. Cipollo
- Center for Biologics Evaluation and Research, Food and Drug AdministrationSilver SpringMaryland
| | - Lisa M. Parsons
- Center for Biologics Evaluation and Research, Food and Drug AdministrationSilver SpringMaryland
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Coff L, Chan J, Ramsland PA, Guy AJ. Identifying glycan motifs using a novel subtree mining approach. BMC Bioinformatics 2020; 21:42. [PMID: 32019496 PMCID: PMC7001330 DOI: 10.1186/s12859-020-3374-4] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2019] [Accepted: 01/20/2020] [Indexed: 11/17/2022] Open
Abstract
Background Glycans are complex sugar chains, crucial to many biological processes. By participating in binding interactions with proteins, glycans often play key roles in host–pathogen interactions. The specificities of glycan-binding proteins, such as lectins and antibodies, are governed by motifs within larger glycan structures, and improved characterisations of these determinants would aid research into human diseases. Identification of motifs has previously been approached as a frequent subtree mining problem, and we extend these approaches with a glycan notation that allows recognition of terminal motifs. Results In this work, we customised a frequent subtree mining approach by altering the glycan notation to include information on terminal connections. This allows specific identification of terminal residues as potential motifs, better capturing the complexity of glycan-binding interactions. We achieved this by including additional nodes in a graph representation of the glycan structure to indicate the presence or absence of a linkage at particular backbone carbon positions. Combining this frequent subtree mining approach with a state-of-the-art feature selection algorithm termed minimum-redundancy, maximum-relevance (mRMR), we have generated a classification pipeline that is trained on data from a glycan microarray. When applied to a set of commonly used lectins, the identified motifs were consistent with known binding determinants. Furthermore, logistic regression classifiers trained using these motifs performed well across most lectins examined, with a median AUC value of 0.89. Conclusions We present here a new subtree mining approach for the classification of glycan binding and identification of potential binding motifs. The Carbohydrate Classification Accounting for Restricted Linkages (CCARL) method will assist in the interpretation of glycan microarray experiments and will aid in the discovery of novel binding motifs for further experimental characterisation.
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Affiliation(s)
- Lachlan Coff
- School of Science, College of Science, Engineering and Health, RMIT University, 3000, Melbourne, Australia
| | - Jeffrey Chan
- School of Science, College of Science, Engineering and Health, RMIT University, 3000, Melbourne, Australia
| | - Paul A Ramsland
- School of Science, College of Science, Engineering and Health, RMIT University, 3000, Melbourne, Australia.,Department of Immunology, Monash University, 3004, Melbourne, Australia.,Department of Surgery Austin Health, University of Melbourne, 3084, Heidelberg, Australia
| | - Andrew J Guy
- School of Science, College of Science, Engineering and Health, RMIT University, 3000, Melbourne, Australia.
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Haab BB, Klamer Z. Advances in Tools to Determine the Glycan-Binding Specificities of Lectins and Antibodies. Mol Cell Proteomics 2020; 19:224-232. [PMID: 31848260 PMCID: PMC7000120 DOI: 10.1074/mcp.r119.001836] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2019] [Revised: 12/13/2019] [Indexed: 01/17/2023] Open
Abstract
Proteins that bind carbohydrate structures can serve as tools to quantify or localize specific glycans in biological specimens. Such proteins, including lectins and glycan-binding antibodies, are particularly valuable if accurate information is available about the glycans that a protein binds. Glycan arrays have been transformational for uncovering rich information about the nuances and complexities of glycan-binding specificity. A challenge, however, has been the analysis of the data. Because protein-glycan interactions are so complex, simplistic modes of analyzing the data and describing glycan-binding specificities have proven inadequate in many cases. This review surveys the methods for handling high-content data on protein-glycan interactions. We contrast the approaches that have been demonstrated and provide an overview of the resources that are available. We also give an outlook on the promising experimental technologies for generating new insights into protein-glycan interactions, as well as a perspective on the limitations that currently face the field.
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