Abstract
The search for new biomolecules requires a clear understanding of biosynthesis and degradation pathways. This view applies to most metabolites as well as other molecule types such as glycans whose repertoire is still poorly characterized. Lectins are proteins that recognize specifically and interact noncovalently with glycans. This particular class of proteins is considered as playing a major role in biology. Glycan-binding is based on multivalence, which gives lectins a unique capacity to interact with surface glycans and significantly contribute to cell–cell recognition and interactions. Lectins have been studied for many years using multiple technologies and part of the resulting information is available online in databases. Unfortunately, the connectivity of these databases with the most popular omics databases (genomics, proteomics, and glycomics) remains limited. Moreover, lectin diversity is extended and requires setting out a flexible classification that remains compatible with new sequences and 3D structures that are continuously released. We have designed UniLectin as a new insight into the knowledge of lectins, their classification, and their biological role. This platform encompasses UniLectin3D, a curated database of lectin 3D structures that follows a periodically updated classification, a set of comparative and visualizing tools and gradually released modules dedicated to specific lectins predicted in sequence databases. The second module is PropLec, focused on β-propeller lectin prediction in all species based on five distinct family profiles. This chapter describes how UniLectin can be used to explore the diversity of lectins, their 3D structures, and associated functional information as well as to perform reliable predictions of β-propeller lectins.
Key words
- Lectin
- Carbohydrate-binding protein
- Database
- Classification
- Sequence
- 3D structure
- Profile-based prediction
This is a preview of subscription content, access via your institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsSpringer Nature is developing a new tool to find and evaluate Protocols. Learn more
References
Lis H, Sharon N (2002) Lectins: carbohydrate-specific proteins that mediate cellular recognition. Chem Rev 98:637–674
Gallagher JT (1984) Carbohydrate-binding properties of lectins: a possible approach to lectin nomenclature and classification. Biosci Rep 4:621–632
Peumans WJ, Van Damme EJ, Barre A et al (2001) Classification of plant lectins in families of structurally and evolutionary related proteins. Adv Exp Med Biol 491:27–54
Kaltner H, Gabius H-J (2011) In: Wu AM (ed) Animal lectins: from initial description to elaborated structural and functional classification. The molecular immunology of complex carbohydrates—2 advances in experimental medicine and biology, vol 491. Springer, Boston, MA, pp 79–94
Fujimoto Z, Tateno H, Hirabayashi J (2014) Lectin structures: classification based on the 3-D structures. Methods Mol Biol 1200:579–606
Finn RD, Bateman A, Clements J et al (2014) Pfam: the protein families database. Nucleic Acids Res 42:D222–D2230
Makyio H, Kato R (2016) Classification and comparison of fucose-binding lectins based on their structures. Trends Glycosci Glycotechnol 28:E25–E37
The UniProt Consortium (2019) UniProt: a worldwide hub of protein knowledge. Nucleic Acids Res 47:D506-D515.
Mir S, Alhroub Y, Anyango S et al (2018) PDBe: towards reusable data delivery infrastructure at protein data bank in Europe. Nucleic Acids Res 46:D486–D492
Pérez S, Sarkar A, Rivet A et al (2015) Glyco3d: a portal for structural glycosciences. Methods Mol Biol 1273:241–258
Hirabayashi J, Tateno H, Shikanai T et al (2015) The lectin frontier database (LfDB), and data generation based on frontal affinity chromatography. Molecules 20:951–973
Chandra NR, Kumar N, Jeyakani J et al (2006) Lectindb: a plant lectin database. Glycobiology 16:938–946
Mariethoz J, Khatib K, Alocci D et al (2016) SugarBindDB, a resource of glycan-mediated host-pathogen interactions. Nucleic Acids Res 44:D1243–D1250
Alocci D, Mariethoz J, Gastaldello A et al (2019) GlyConnect: glycoproteomics goes visual, interactive, and analytical. J Proteome Res 18:664–677
Sehnal D, Deshpande M, Vařeková RS et al (2017) LiteMol suite: interactive web-based visualization of large-scale macromolecular structure data. Nat Methods 14:1121–1122
Raman R, Venkataraman M, Ramakrishnan S et al (2006) Advancing glycomics: implementation strategies at the consortium for functional glycomics. Glycobiology 16:82R–90R
Mehta AY, Cummings RD (2019) GLAD: GLycan Array Dashboard, a visual analytics tool for glycan microarrays. Bioinformatics 35(18):3536–3537
Marchler-Bauer A, Derbyshire MK, Gonzales NR et al (2015) CDD: NCBI's conserved domain database. Nucleic Acids Res 43:D222–226
Mitchell AL, Attwood TK, Babbitt PC et al (2019) InterPro in 2019: improving coverage, classification and access to protein sequence annotations. Nucleic Acids Res 47:D351–D360
Chandonia JM, Fox NK, Brenner SE (2019) SCOPe: classification of largemacromolecular structures in the structural classification of proteins-extendeddatabase. Nucleic Acids Res 47:D475–D48
Sillitoe I, Dawson N, Lewis TE et al (2019) CATH: expanding the horizons of structure-based functional annotations for genome sequences. Nucleic Acids Res 47:D280–D284
Lütteke T, von der Lieth CW (2004) PDB-care (PDB CArbohydrate REsidue check): a program to support annotation of complex carbohydrate structures in PDB files. BMC Bioinformatics 5:69
Salentin S, Schreiber S, Haupt VJ et al (2015) PLIP: fully automated protein-ligand interaction profiler. Nucleic Acids Res 43:W443–W447
Varki A, Cummings RD, Aebi M et al (2015) Symbol nomenclature for graphical representation of glycans. Glycobiology 25:1323–1324
Rose AS, Bradley AR, Valasatava Y et al (2018) NGL viewer: web-based molecular graphics for large complexes. Bioinformatics 34:3755–3758
Bienert S, Waterhouse A, De Beer TAP et al (2017) The SWISS-MODEL repository-new features and functionality. Nucleic Acids Res 45:D313–D319
Camacho C, Coulouris G, Avagyan V et al (2009) BLAST+: architecture and applications. BMC bioinformatics 10:421
Finn RD, Clements J, Arndt W et al (2015) HMMER web server: 2015 Update. Nucleic Acids Res 43:W30–W38
O'Leary NA, Wright MW, Brister JR et al (2016) Reference sequence (RefSeq) database at NCBI: current status, taxonomic expansion, and functional annotation. Nucleic Acids Res 44:D733–D745
Brown GR, Hem V, Katz KS et al (2015) Gene: a gene-centered information resource at NCBI. Nucleic Acids Res 43:D36–D42
Edgar RC (2004) MUSCLE: multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res 32:1792–1797
Acknowledgments
The authors acknowledge support by the ANR PIA Glyco@Alps (ANR-15-IDEX-02) and the Alliance Campus Rhodanien Co-funds (http://campusrhodanien.unige-cofunds.ch).
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Science+Business Media, LLC, part of Springer Nature
About this protocol
Cite this protocol
Bonnardel, F., Perez, S., Lisacek, F., Imberty, A. (2020). Structural Database for Lectins and the UniLectin Web Platform. In: Hirabayashi, J. (eds) Lectin Purification and Analysis. Methods in Molecular Biology, vol 2132. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-0430-4_1
Download citation
DOI: https://doi.org/10.1007/978-1-0716-0430-4_1
Published:
Publisher Name: Humana, New York, NY
Print ISBN: 978-1-0716-0429-8
Online ISBN: 978-1-0716-0430-4
eBook Packages: Springer Protocols