Glycoinformatics pp 55-85

Part of the Methods in Molecular Biology book series (MIMB, volume 1273) | Cite as

Bacterial, Plant, and Fungal Carbohydrate Structure Databases: Daily Usage

Protocol

Abstract

Natural carbohydrates play important roles in living systems and therefore are used as diagnostic and therapeutic targets. The main goal of glycomics is systematization of carbohydrates and elucidation of their role in human health and disease. The amount of information on natural carbohydrates accumulates rapidly, but scientists still lack databases and computer-assisted tools needed for orientation in the glycomic information space. Therefore, freely available, regularly updated, and cross-linked databases are demanded.

Bacterial Carbohydrate Structure Database (Bacterial CSDB) was developed for provision of structural, bibliographic, taxonomic, NMR spectroscopic, and other related information on bacterial and archaeal carbohydrate structures. Its main features are (1) coverage above 90 %, (2) high data consistence (above 90 % of error-free records), and (3) presence of manually verified bibliographic, NMR spectroscopic, and taxonomic annotations. Recently, CSDB has been expanded to cover carbohydrates of plant and fungal origin. The achievement of full coverage in the plant and fungal domains is expected in the future. CSDB is freely available on the Internet as a web service at http://csdb.glycoscience.ru. This chapter aims at showing how to use CSDB in your daily scientific practice.

Key words

BCSDB CSDB Carbohydrate Database Carbohydrate structure Bacterial carbohydrate Archaeal carbohydrate Plant carbohydrate Fungal carbohydrate Bibliography Taxonomy NMR NMR spectrum prediction Glycan description Glycoinformatics 

References

  1. 1.
    Shriver Z, Raguram S, Sasisekharan K (2004) Glycomics: a pathway to a class of new and improved therapeutics. Nat Rev Drug Discov 3:863–873CrossRefPubMedGoogle Scholar
  2. 2.
    Slovin SF et al (2005) A bivalent conjugate vaccine in the treatment of biochemically relapsed prostate cancer: a study of glycosylated MUC-2-KLH and Globo H-KLH conjugate vaccines given with the new semi-synthetic saponin immunological adjuvant GPI-0100 OR QS-21. Vaccine 23:3114–3122CrossRefPubMedGoogle Scholar
  3. 3.
    Pantophlet R, Wilson IA, Burton DR (2003) Hyperglycosylated mutants of human immunodeficiency virus (HIV) type 1 monomeric gp120 as novel antigens for HIV vaccine design. J Virol 77:5889–5901CrossRefPubMedCentralPubMedGoogle Scholar
  4. 4.
    Jones C (2005) Vaccines based on the cell surface carbohydrates of pathogenic bacteria. An Acad Bras Cienc 77:293–324CrossRefPubMedGoogle Scholar
  5. 5.
    Gornik O et al (2006) Glycoscience – a new frontier in rational drug design. Acta Pharm 56:19–30PubMedGoogle Scholar
  6. 6.
    Ernst B, Magnani JL (2009) From carbohydrate leads to glycomimetic drugs. Nat Rev Drug Discov 8:661–677CrossRefPubMedGoogle Scholar
  7. 7.
    Astronomo RD, Burton DR (2010) Carbohydrate vaccines: developing sweet solutions to sticky situations? Nat Rev Drug Discov 9:308–324CrossRefPubMedGoogle Scholar
  8. 8.
    Boltje TJ, Buskas T, Boons G-J (2009) Opportunities and challenges in synthetic oligosaccharide and glycoconjugate research. Nat Chem 1:611–622CrossRefPubMedCentralPubMedGoogle Scholar
  9. 9.
    von der Lieth C-W, Lütteke T, Frank M (2006) The role of informatics in glycobiology research with special emphasis on automatic interpretation of MS spectra. Biochim Biophys Acta 1760:568–577CrossRefPubMedGoogle Scholar
  10. 10.
    Beattie GA, Lindow SE (1999) Bacterial colonization of leaves: a spectrum of strategies. Phytopathology 89:353–359CrossRefPubMedGoogle Scholar
  11. 11.
    Duus J, Gotfredsen CH, Bock K (2000) Carbohydrate structural determination by NMR spectroscopy: modern methods and limitations. Chem Rev 100:4589–4614CrossRefPubMedGoogle Scholar
  12. 12.
    Hricovini M (2004) Structural aspects of carbohydrates and the relation with their biological properties. Curr Med Chem 11:2565–2583CrossRefPubMedGoogle Scholar
  13. 13.
    Harvey D (2005) Proteomic analysis of glycosylation: structural determination of N- and O-linked glycans by mass spectrometry. Expert Rev Proteomics 2:87–101CrossRefPubMedGoogle Scholar
  14. 14.
    Toukach P et al (2007) Sharing of worldwide distributed carbohydrate-related digital resources: online connection of the bacterial carbohydrate structure DataBase and GLYCOSCIENCES.de. Nucl Acids Res 35(Database):D280–D286CrossRefPubMedCentralPubMedGoogle Scholar
  15. 15.
    Ranzinger R et al (2009) Glycome-DB.org: a portal for querying across the digital world of carbohydrate sequences. Glycobiology 19:1563–1567CrossRefPubMedGoogle Scholar
  16. 16.
    Herget S et al (2008) Statistical analysis of the bacterial carbohydrate structure data base (BCSDB): characteristics and diversity of bacterial carbohydrates in comparison with mammalian glycans. BMC Struct Biol 8:35CrossRefPubMedCentralPubMedGoogle Scholar
  17. 17.
    Doubet S et al (1989) The complex carbohydrate structure database. Trends Biochem Sci 14:475–477CrossRefPubMedGoogle Scholar
  18. 18.
    Doubet S, Albersheim P (1992) CarbBank. Glycobiology 2:505CrossRefPubMedGoogle Scholar
  19. 19.
    Lütteke T et al (2006) GLYCOSCIENCES.de: an Internet portal to support glycomics and glycobiology research. Glycobiology 16:71R–81RCrossRefPubMedGoogle Scholar
  20. 20.
    Cooper CA et al (2003) GlycoSuiteDB: a curated relational database of glycoprotein glycan structures and their biological sources. 2003 Update. Nucleic Acids Res 31:511–513CrossRefPubMedCentralPubMedGoogle Scholar
  21. 21.
    Raman R et al (2006) Advancing glycomics: implementation strategies at the consortium for functional glycomics. Glycobiology 16:82R–90RCrossRefPubMedGoogle Scholar
  22. 22.
    Hashimoto K et al (2006) KEGG as a glycome informatics resource. Glycobiology 16:63R–70RCrossRefPubMedGoogle Scholar
  23. 23.
    Campbell MP et al (2008) GlycoBase and autoGU: tools for HPLC-based glycan analysis. Bioinformatics 24:1214–1216CrossRefPubMedGoogle Scholar
  24. 24.
    Maes E et al (2009) SOACS index: an easy NMR-based query for glycan retrieval. Carbohydr Res 344:322–330CrossRefPubMedGoogle Scholar
  25. 25.
    Stenutz R, Weintraub A, Widmalm G (2006) The structures of Escherichia coli O-polysaccharide antigens. FEMS Microbiol Rev 30:382–403CrossRefPubMedGoogle Scholar
  26. 26.
    von der Lieth C-W et al (2011) EUROCarbDB: an open-access platform for glycoinformatics. Glycobiology 21:493–502CrossRefPubMedCentralPubMedGoogle Scholar
  27. 27.
    Campbell MP et al (2011) UniCarbKB: putting the pieces together for glycomics research. Proteomics 11:4117–4121CrossRefPubMedGoogle Scholar
  28. 28.
    Egorova KS, Toukach PV (2012) Critical analysis of CCSD data quality. J Chem Inf Model 52:2812–2814CrossRefPubMedGoogle Scholar
  29. 29.
    Egorova KS, Toukach PV (2014) Expansion of coverage of carbohydrate structure database (CSDB). Carbohydr Res 389:112–114CrossRefPubMedGoogle Scholar
  30. 30.
    Toukach PV (2011) Bacterial carbohydrate structure database 3: principles and realization. J Chem Inf Model 51:159–170CrossRefPubMedGoogle Scholar
  31. 31.
    Aoki-Kinoshita KF et al (2013) Introducing glycomics data into the semantic Web. J Biomed Semantics 4:39CrossRefPubMedCentralPubMedGoogle Scholar
  32. 32.
    Ceroni A, Dell A, Haslam SM (2007) The GlycanBuilder: a fast, intuitive and flexible software tool for building and displaying glycan structures. Source Code Biol Med 2:3CrossRefPubMedCentralPubMedGoogle Scholar
  33. 33.
    Herget S et al (2008) GlycoCT – a unifying sequence format for carbohydrates. Carbohydr Res 343:2162–2171CrossRefPubMedGoogle Scholar
  34. 34.
    Kirschner KN et al (2008) GLYCAM06: a generalizable biomolecular force field. Carbohydrates. J Comput Chem 29:622–655CrossRefPubMedGoogle Scholar
  35. 35.
    Sayers EW et al (2011) Database resources of the National Center for Biotechnology Information. Nucleic Acids Res 39:D38–D51CrossRefPubMedCentralPubMedGoogle Scholar
  36. 36.
    Loss A et al (2002) SWEET-DB: an attempt to create annotated data collections for carbohydrates. Nucleic Acids Res 30:405–408CrossRefPubMedCentralPubMedGoogle Scholar
  37. 37.
    Sharon N (1988) Nomenclature of glycoproteins, glycopeptides and peptidoglycans. Pure Appl Chem 60:1389–1394CrossRefGoogle Scholar
  38. 38.
    Toukach PV, Shashkov AS (2001) Computer-assisted structural analysis of regular glycopolymers on the basis of 13C NMR data. Carbohydr Res 335:101–114CrossRefPubMedGoogle Scholar
  39. 39.
    Lundborg M, Widmalm G (2011) Structural analysis of glycans by NMR chemical shift prediction. Anal Chem 83:1514–1517CrossRefPubMedGoogle Scholar
  40. 40.
    Toukach FV, Ananikov VP (2013) Recent advances in computational predictions of NMR parameters for the structure elucidation of carbohydrates: methods and limitations. Chem Soc Rev 42:8376–8415CrossRefPubMedGoogle Scholar
  41. 41.
    Kapaev RR, Egorova KS, Toukach PV (2014) Carbohydrate structure generalization scheme for database-driven simulation of experimental observables, such as NMR chemical shifts. J Chem Inf Model 54:2594–2611 http://www.lcme.org/standard.htmGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  1. 1.N.D. Zelinsky Institute of Organic ChemistryRussian Academy of SciencesMoscowRussia

Personalised recommendations