Biology:Carbohydrate Structure Database

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Carbohydrate Structure Database
Carbohydrate Structure Database logo.gif
Content
DescriptionNatural carbohydrate structures with NMR, bibliographic and biological annotations.
Data types
captured
carbohydrate structures and related data
Organisms
Contact
Research centreZelinsky Institute of Organic Chemistry
Author(s)Philip V. Toukach, Ksenia S. Egorova, Yuri A. Knirel, et al.
Primary citationCarbohydrate Structure Database [1]
Release date2005
Access
Websitehttp://csdb.glycoscience.ru/
Download URLexport feature in web-interface
Tools
Web application
Miscellaneous
Software versioningyes
Data release
frequency
annual
Version1 (merged)
Curation policyyes (manual and automatic)

Carbohydrate Structure Database (CSDB) is a free curated database and service platform in glycoinformatics, launched in 2005[2] by a group of Russian scientists from N.D. Zelinsky Institute of Organic Chemistry, Russian Academy of Sciences. CSDB stores published structural, taxonomical, bibliographic and NMR-spectroscopic data on natural carbohydrates and carbohydrate-related molecules.

Overview

The main data stored in CSDB are carbohydrate structures of bacterial, fungal, and plant origin. Each structure is assigned to an organism and is provided with the link(s) to the corresponding scientific publication(s), in which it was described. Apart from structural data, CSDB also stores NMR spectra, information on methods used to decipher a particular structure, and some other data.[1][3] CSDB provides access to several carbohydrate-related research tools:

History and funding

Until 2015, Bacterial Carbohydrate Structure Database (BCSDB) and Plant&Fungal Carbohydrate Structure Database (PFCSDB) databases existed in parallel. In 2015, they were joined into the single Carbohydrate Structure Database (CSDB).[1] The development and maintenance of CSDB have been funded by International Science and Technology Center (2005-2007), Russian Federation President grant program (2005-2006), Russian Foundation for Basic Research (2005-2007,2012-2014,2015-2017,2018-2020), Deutsches Krebsforschungszentrum (short-term in 2006-2010), and Russian Science Foundation (2018-2020).

Data sources and coverage

The main sources of CSDB data are:

The data are selected and added to CSDB manually by browsing original scientific publications. The data originating from other databases are subject to error-correction and approval procedures.[14] As of 2017, the coverage on bacteria and archaea is ca. 80% of carbohydrate structures published in scientific literature [1] The time lag between the publication of relative data and their deposition into CSDB is about 18 months. Plants are covered up to 1997, and fungi up to 2012.[15] CSDB does not cover data from the animalia domain, except unicellular metazoa. There is a number of dedicated databases on animal carbohydrates, e.g. UniCarbKB [16] or GLYCOSCIENCES.de.[17]

CSDB is reported as one of the biggest projects in glycoinformatics.[18][19][20][21][22][23][24] It is employed in structural studies of natural carbohydrates[25][26][27] and in glyco-profiling.[28] The content of CSDB has been used as a data source in other glycoinformatics projects.[29][30][31][32]

Deposited objects

  • Molecular structures of glycans, glycopolymers and glycoconjugates: primary structure, aglycon information, polymerization degree and class of molecule. Structural scope includes molecules composed of residues (monosaccharides, alditols, amino acids, fatty acids etc.) linked by glycosidic, ester, amidic, ketal, phospho- or sulpho-diester bonds, in which at least one residue is a monosaccharide or its derivative.
  • Bibliography associated with structures: imprint data, keywords, abstracts, IDs in bibliographic databases
  • Biological context of structures: associated taxon, strain, serogroup, host organism, disease information. The covered domains are: prokaryotes, plants, fungi and selected pathogenic unicellular metazoa. The database contains only glycans originating from these domains or obtained by chemical modification of such glycans.
  • Assigned NMR spectra and experimental conditions.
  • Glycosyltransferases associated with taxons: gene and enzyme identifiers, full structures, donor and substrates, methods used to prove enzymatic activity, trustworthiness level.
  • References to other databases
  • Other data collected from original publications
  • Conformation maps of disaccharides derived from molecular dynamics simulations.

Interrelation with other databases

CSDB is cross-linked to other glycomics databases,[33][34] such as MonosaccharideDB, Glycosciences.DE, NCBI Pubmed, NCBI Taxonomy, NLM catalog, International Classification of Diseases 11, etc. Besides a native notation, CSDB Linear,[35] structures are presented in multiple carbohydrate notations (SNFG,[36] SweetDB,[37] GlycoCT,[38] WURCS,[39] GLYCAM,[40] etc.). CSDB is exportable as a Resource Description Framework (RDF) feed according to the GlycoRDF ontology.[41][42]

External links

References

  1. 1.0 1.1 1.2 1.3 Toukach Ph.V.; Egorova K.S. (2016). "Carbohydrate structure database merged from bacterial, archaeal, plant and fungal parts". Nucleic Acids Research 44 (D1): D1229–D1236. doi:10.1093/nar/gkv840. PMID 26286194. 
  2. Toukach F.V.; Knirel Y.A. (2005). "New database of bacterial carbohydrate structures". Glycoconjugate Journal 22 (4–6): 216–217. 
  3. Harvey D.J. (2015). "Analysis of carbohydrates and glycoconjugates by matrix-assisted laser desorption/ionization mass spectrometry: An update for 2011-2012". Mass Spectrometry Reviews 36 (3): 255–422. doi:10.1002/mas.21471. PMID 26270629. 
  4. Kapaev R.R.; Egorova K.S.; Toukach Ph.V. (2014). "Carbohydrate structure generalization scheme for database-driven simulation of experimental observables, such as NMR chemical shifts". Journal of Chemical Information and Modeling 54 (9): 2594–2611. doi:10.1021/ci500267u. PMID 25020143. 
  5. Kapaev R.R.; Toukach Ph.V. (2015). "Improved carbohydrate structure generalization scheme for 1H and 13C NMR simulations". Analytical Chemistry 87 (14): 7006–7010. doi:10.1021/acs.analchem.5b01413. PMID 26087011. 
  6. Kapaev R.R.; Toukach Ph.V. (2016). "Simulation of 2D NMR Spectra of Carbohydrates Using GODDESS Software". Journal of Chemical Information and Modeling 56 (6): 1100–1104. doi:10.1021/acs.jcim.6b00083. PMID 27227420. 
  7. Kapaev R.R.; Toukach Ph.V. (2018). "GRASS: semi-automated NMR-based structure elucidation of saccharides". Bioinformatics 34 (6): 957–963. doi:10.1093/bioinformatics/btx696. PMID 29092007. 
  8. 8.0 8.1 Egorova K.S.; Kondakova A.N.; Toukach Ph.V. (2015). "Carbohydrate structure database: tools for statistical analysis of bacterial, plant and fungal glycomes". Database 2015: ID bav073. doi:10.1093/database/bav073. PMID 26337239. 
  9. Herget S.; Toukach Ph.V.; Ranzinger R.; Hull W.E.; Knirel Y.; von der Lieth C.-W. (2008). "Statistical analysis of the Bacterial Carbohydrate Structure Data Base (BCSDB): Characteristics and diversity of bacterial carbohydrates in comparison with mammalian glycans". BMC Structural Biology 8: ID 35. doi:10.1186/1472-6807-8-35. PMID 18694500. 
  10. Chernyshov I.Y.; Toukach Ph.V. (2018). "REStLESS: Automated Translation of Glycan Sequences from Residue-Based Notation to SMILES and Atomic Coordinates". Bioinformatics 34 (15): 2679–2681. doi:10.1093/bioinformatics/bty168. PMID 29547883. 
  11. Toukach Ph.V.; Egorova K.S. (2017). "CSDB_GT: a new curated database on glycosyltransferases". Glycobiology 27 (4): 285–290. doi:10.1093/glycob/cww137. PMID 28011601. 
  12. Egorova K.S.; Knirel Y.A.; Toukach Ph.V. (2019). "Expanding CSDB_GT glycosyltransferase database with Escherichia coli". Glycobiology 29 (4): 285–287. doi:10.1093/glycob/cwz006. PMID 30759212. 
  13. Doubet S.; Albersheim P. (1992). "CarbBank". Glycobiology 2 (6): 505–507. doi:10.1093/glycob/2.6.505. PMID 1472756. 
  14. Egorova K.S.; Toukach Ph.V. (2012). "Critical analysis of CCSD data quality". Journal of Chemical Information and Modeling 52 (11): 2812–2814. doi:10.1021/ci3002815. PMID 23025661. 
  15. Egorova K.S.; Toukach Ph.V. (2013). "Expansion of coverage of Carbohydrate Structure Database (CSDB)". Carbohydrate Research 389: 112–114. doi:10.1016/j.carres.2013.10.009. PMID 24680503. 
  16. Campbell M.P.; Packer N.H. (2016). "UniCarbKB: New database features for integrating glycan structure abundance, compositional glycoproteomics data, and disease associations". Biochimica et Biophysica Acta (BBA) - General Subjects 1860 (8): 1669–1675. doi:10.1016/j.bbagen.2016.02.016. PMID 26940363. 
  17. Lütteke T.; Bohne-Lang A.; Loss A.; Goetz T.; Frank M.; von der Lieth C.-W. (2006). "GLYCOSCIENCES.de: an Internet portal to support glycomics and glycobiology research". Glycobiology 16 (5): 71R–81R. doi:10.1093/glycob/cwj049. PMID 16239495. 
  18. Rigden D.J.; Fernández-Suárez X.M.; Galperin M.Y. (2016). "The 2016 database issue of Nucleic Acids Research and an updated molecular biology database collection". Nucleic Acids Research 44 (D1): D1–D6. doi:10.1093/nar/gkv1356. PMID 26740669. 
  19. Aoki-Kinoshita K.F. (2013). "Using databases and web resources for glycomics research". Molecular & Cellular Proteomics 12 (4): 1036–1045. doi:10.1074/mcp.R112.026252. PMID 23325765. 
  20. Frank M.; Schloissnig S. (2010). "Bioinformatics and molecular modeling in glycobiology.". Cellular and Molecular Life Sciences 67 (16): 2749–2772. doi:10.1007/s00018-010-0352-4. PMID 20364395. 
  21. Artemenko N.V.; McDonald A.G.; Davey G.P.; Rudd P.M. (2012). Databases and tools in glycobiology. Methods in Molecular Biology. 899. pp. 325–350. doi:10.1007/978-1-61779-921-1_21. ISBN 978-1-61779-920-4. 
  22. Lütteke T. (2012). "The use of glycoinformatics in glycochemistry". Beilstein Journal of Organic Chemistry 8: 915–929. doi:10.3762/bjoc.8.104. PMID 23015842. 
  23. Zhulin I.B. (2015). "Databases for Microbiologists". Journal of Bacteriology 197 (15): 2458–2467. doi:10.1128/JB.00330-15. PMID 26013493. 
  24. Yamada K.; Kakehi K. (2011). "Recent advances in the analysis of carbohydrates for biomedical use". Journal of Pharmaceutical and Biomedical Analysis 55 (4): 702–727. doi:10.1016/j.jpba.2011.02.003. PMID 21382683. 
  25. Fontana C.; Zaccheus M.; Weintraub A.; Ansaruzzaman M.; Widmalm G. (2016). "Structural studies of a polysaccharide from Vibrio parahaemolyticus strain AN-16000". Carbohydrate Research 432: 41–49. doi:10.1016/j.carres.2016.06.004. PMID 27392309. http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-133271. 
  26. Potekhina N.V.; Shashkov A.S.; Senchenkova S.N.; Dorofeeva L.V.; Evtushenko L.I. (2012). "Structure of hexasaccharide 1-phosphate polymer from Arthrobacter uratoxydans VKM Ac-1979(T) cell wall". Biochemistry (Moscow) 77 (11): 1294–1302. doi:10.1134/S0006297912110089. PMID 23240567. 
  27. Chapot-Chartier M.P.; Vinogradov E.; Sadovskaya I.; Andre G.; Mistou M.Y.; Trieu-Cuot P.; Furlan S.; Bidnenko E. et al. (2010). "Cell surface of Lactococcus lactis is covered by a protective polysaccharide pellicle". Journal of Biological Chemistry 285 (14): 10464–10471. doi:10.1074/jbc.M109.082958. PMID 20106971. 
  28. Walsh I.; Zhao S.; Campbell M.; Taron C.H.; Rudd P.M. (2016). "Quantitative profiling of glycans and glycopeptides: an informatics' perspective". Current Opinion in Structural Biology 40: 70–80. doi:10.1016/j.sbi.2016.07.022. PMID 27522273. 
  29. Ranzinger R.; York W.S. (2015). GlycomeDB. Methods in Molecular Biology. 1273. pp. 109–124. doi:10.1007/978-1-4939-2343-4_8. ISBN 978-1-4939-2342-7. 
  30. Ranzinger R.; Herget S.; von der Lieth C.-W.; Frank M. (2011). "GlycomeDB - a unified database for carbohydrate structures". Nucleic Acids Research 39 (Database issue): D373-376. doi:10.1093/nar/gkq1014. PMID 21045056. 
  31. Aoki-Kinoshita K.F. (2016). "GlyTouCan 1.0 - The international glycan structure repository". Nucleic Acids Research 44 (D1): D1237-1242. doi:10.1093/nar/gkv1041. PMID 26476458. 
  32. Campbell M.P.; Ranzinger R.; Lütteke T.; Mariethoz J.; Hayes CA.; Zhang J.; Akune Y.; Aoki-Kinoshita K.F. et al. (2014). "Toolboxes for a standardised and systematic study of glycans". BMC Bioinformatics 15: Suppl 1:S9. doi:10.1186/1471-2105-15-S1-S9. PMID 24564482. 
  33. Ranzinger R.; Herget S.; Wetter T.; von der Lieth C.-W. (2008). "GlycomeDB - integration of open-access carbohydrate structure databases". BMC Bioinformatics 9: ID 384. doi:10.1186/1471-2105-9-384. PMID 18803830. 
  34. Toukach Ph.V.; Joshi H.; Ranzinger R.; Knirel Y.; von der Lieth C.-W. (2007). "Sharing of worldwide distributed carbohydrate-related digital resources: online connection of the Bacterial Carbohydrate Structure DataBase and GLYCOSCIENCES.de". Nucleic Acids Research 35 (Database issue): D280–D286. doi:10.1093/nar/gkl883. PMID 17202164. 
  35. Toukach Ph.V.; Egorova K.S. (2020). "New features of CSDB Linear, as compared to other carbohydrate notations". Journal of Chemical Information and Modeling 60 (3): 1276–1289. doi:10.1021/acs.jcim.9b00744. PMID 31790229. 
  36. Varki A. (2015). "Symbol Nomenclature for Graphical Representations of Glycans". Glycobiology 25 (12): 1323–1324. doi:10.1093/glycob/cwv091. PMID 26543186. 
  37. Loss A.; Bunsmann P.; Bohne A.; Loss A.; Schwarzer E.; Lang E.; von der Lieth C.-W. (2002). "SWEET-DB: an attempt to create annotated data collections for carbohydrates". Nucleic Acids Research 30 (1): 405–408. doi:10.1093/nar/30.1.405. PMID 11752350. 
  38. Herget S.; Ranzinger R.; Maass K.; von der Lieth C.-W. (2008). "GlycoCT - a unifying sequence format for carbohydrates". Carbohydrate Research 343 (12): 2162–2171. doi:10.1016/j.carres.2008.03.011. PMID 18436199. 
  39. Tanaka K.; Aoki-Kinoshita K.F.; Kotera M.; Sawaki H.; Tsuchiya S.; Fujita N.; Shikanai T.; Kato M. et al. (2014). "WURCS: the Web3 unique representation of carbohydrate structures". Journal of Chemical Information and Modeling 54 (6): 1558–1566. doi:10.1021/ci400571e. PMID 24897372. 
  40. Kirschner K.N.; Yongye A.B.; Tschampel S.M.; González-Outeiriño J.; Daniels C.R.; Foley B.L.; Woods R.J. (2008). "GLYCAM06: a generalizable biomolecular force field. Carbohydrates". Journal of Computational Chemistry 29 (4): 622–655. doi:10.1002/jcc.20820. PMID 17849372. 
  41. Ranzinger R.; Aoki-Kinoshita K.F.; Campbell M.P.; Kawano S.; Lütteke T.; Okuda S.; Shinmachi D.; Shikanai T. et al. (2015). "GlycoRDF: An ontology to standardize Glycomics data in RDF". Bioinformatics 31 (6): 919–925. doi:10.1093/bioinformatics/btu732. PMID 25388145. 
  42. Aoki-Kinoshita K.F.; Bolleman J.; Campbell M.P.; Kawano S.; Kim J.; Lütteke T.; Matsubara M.; Okuda S. et al. (2013). "Introducing glycomics data into the Semantic Web". Journal of Biomedical Semantics 4 (1): ID 39. doi:10.1186/2041-1480-4-39. PMID 24280648.