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Conformational Analysis of Oligosaccharides and Polysaccharides Using Molecular Dynamics Simulations

  • Martin Frank
Protocol
Part of the Methods in Molecular Biology book series (MIMB, volume 1273)

Abstract

Complex carbohydrates usually have a large number of rotatable bonds and consequently a large number of theoretically possible conformations can be generated (combinatorial explosion). The application of systematic search methods for conformational analysis of carbohydrates is therefore limited to disaccharides and trisaccharides in a routine analysis. An alternative approach is to use Monte-Carlo methods or (high-temperature) molecular dynamics (MD) simulations to explore the conformational space of complex carbohydrates. This chapter describes how to use MD simulation data to perform a conformational analysis (conformational maps, hydrogen bonds) of oligosaccharides and how to build realistic 3D structures of large polysaccharides using Conformational Analysis Tools (CAT).

Key words

Molecular dynamics simulation Conformational analysis Conformational maps Carbohydrates Polysaccharides Hydrogen bonds 

References

  1. 1.
    Frank M, Bohne-Lang A, Wetter T, von der Lieth CW (2002) Rapid generation of a representative ensemble of N-glycans conformations. In Silico Biol 2:427–439PubMedGoogle Scholar
  2. 2.
    Frank M, Schloissnig S (2010) Bioinformatics and molecular modeling in glycobiology. Cell Mol Life Sci 67:2749–2772CrossRefPubMedCentralPubMedGoogle Scholar
  3. 3.
    Woods RJ, Tessier MB (2010) Computational glycoscience: characterizing the spatial and temporal properties of glycans and glycan-protein complexes. Curr Opin Struct Biol 20:575–583CrossRefPubMedCentralPubMedGoogle Scholar
  4. 4.
    Frank M (2009) Predicting carbohydrate 3D structures using theoretical methods. In: von der Lieth C-W, Lütteke T, Frank M (eds) Bioinformatics for glycobiology and glycomics. Wiley, New York, pp 359–388CrossRefGoogle Scholar
  5. 5.
    Imberty A, Perez S (2000) Structure, conformation, and dynamics of bioactive oligosaccharides: theoretical approaches and experimental validations. Chem Rev 100:4567–4588CrossRefPubMedGoogle Scholar
  6. 6.
    von der Lieth CW, Kozar T, Hull WE (1997) A (critical) survey of modelling protocols used to explore the conformational space of oligosaccharides. THEOCHEM 395:225–244CrossRefGoogle Scholar
  7. 7.
    Ranzinger R, Herget S, von der Lieth C-W, Frank M (2011) GlycomeDB—a unified database for carbohydrate structures. Nucleic Acids Res 39:D373–D376CrossRefPubMedCentralPubMedGoogle Scholar
  8. 8.
    Frank M, Lutteke T, von der Lieth CW (2007) GlycoMapsDB: a database of the accessible conformational space of glycosidic linkages. Nucleic Acids Res 35:287–290CrossRefPubMedCentralPubMedGoogle Scholar
  9. 9.
    Allinger NL, Yan LQ (1993) Molecular mechanics (MM3). Calculations of furan, vinyl ethers, and related compounds. J Am Chem Soc 115:11918–11925CrossRefGoogle Scholar
  10. 10.
    Stortz CA (2005) Comparative performance of MM3(92) and two TINKER MM3 versions for the modeling of carbohydrates. J Comput Chem 26:471–483Google Scholar
  11. 11.
    Woods Group (2005–2013) GLYCAM Web. Complex Carbohydrate Research Center, University of Georgia, Athens, GA. http://www.glycam.com
  12. 12.
    Jordan RC, Brant DA, Cesaro A (1978) A Monte Carlo study of the amylosic chain conformation. Biopolymers 17:2617–2632CrossRefGoogle Scholar
  13. 13.
    Sapay N, Nurisso A, Imberty A (2013) Simulation of carbohydrates, from molecular docking to dynamics in water. Methods Mol Biol 924:469–483CrossRefPubMedGoogle Scholar
  14. 14.
    Demarco ML, Woods RJ (2008) Structural glycobiology: a game of snakes and ladders. Glycobiology 18:426–440CrossRefPubMedCentralPubMedGoogle Scholar
  15. 15.
    Varki A, Cummings RD, Esko JD et al (2009) Symbol nomenclature for glycan representation. Proteomics 9:5398–5399CrossRefPubMedCentralPubMedGoogle Scholar
  16. 16.
    Bohne A, Lang E, von der Lieth CW (1999) SWEET—WWW-based rapid 3D construction of oligo- and polysaccharides. Bioinformatics 15:767–768CrossRefPubMedGoogle Scholar
  17. 17.
    Salomon-Ferrer R, Case DA, Walker RC (2013) An overview of the amber biomolecular simulation package. WIREs Comput Mol Sci 3:198–210CrossRefGoogle Scholar
  18. 18.
    Kuttel M, Mao Y, Widmalm G (2011) CarbBuilder: an adjustable tool for building 3D molecular structures of carbohydrates for molecular simulation. Presented at the 2011 IEEE 7th international conference on E-Science (e-Science). doi: 10.1109/eScience.2011.61Google Scholar
  19. 19.
    Engelsen SB, Hansen PI, Perez S (2014) POLYS 2.0: An open source software package for building three-dimensional structures of polysaccharides. Biopolymers 101:733–743. doi:  10.1002/bip.22449
  20. 20.
    Cros S, Garnier C, Axelos MAV et al (1996) Solution conformations of pectin polysaccharides: determination of chain characteristics by small angle neutron scattering, viscometry, and molecular modeling. Biopolymers 39:339–352CrossRefPubMedGoogle Scholar
  21. 21.
    Boutherin B, Mazeau K, Tvaroska I (1997) Conformational statistics of pectin substances in solution by a Metropolis Monte Carlo study. Carbohydr Polym 32:255–266CrossRefGoogle Scholar
  22. 22.
    Kroon-Batenburg LMJ, Kruiskamp PH, Vliegenthart JFG, Kroon J (1997) Estimation of the persistence length of polymers by MD simulations on small fragments in solution. Application to cellulose. J Phys Chem B 101:8454–8459CrossRefGoogle Scholar
  23. 23.
    Humphrey W, Dalke A, Schulten K (1996) VMD: visual molecular dynamics. J Mol Graph 14:33–38, 27–28CrossRefPubMedGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  1. 1.Biognos ABGöteborgSweden

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