Computational Analysis of Solvent Inclusion in Docking Studies of Protein–Glycosaminoglycan Systems

  • Sergey A. Samsonov
Part of the Methods in Molecular Biology book series (MIMB, volume 1762)


Glycosaminoglycans (GAGs) are a class of anionic linear periodic polysaccharides, which play a key role in many cell signaling related processes via interactions with their protein targets. In silico analysis and, in particular, application of molecular docking approaches to these systems still experience many challenges including the need of proper treatment of solvent, which is crucial for protein–GAG interactions. Here, we describe two methods which we developed, to include solvent in the docking studies of protein–GAG systems: the first one allows to de novo predict favorable positions of water molecules as a part of a rigid receptor to be used for further molecular docking; the second one utilizes targeted molecular dynamics in explicit solvent for molecular docking.

Key words

Atomic probes Electrostatics-driven interactions Explicit solvent Free energy calculations Glycosaminoglycans Molecular docking Solvent displacement Targeted molecular dynamics 



This work was supported by National Science Center of Poland (Narodowy Centrum Nauki, grant UMO-2016/21/P/ST4/03995). This project received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No. 665778.


  1. 1.
    Esko JD, Kimata K, Lindahl U (2009) Proteoglycans and sulfated glycosaminoglycans. In: Varki A, Cummings RD, Esko JD et al (eds) Essentials of glycobiology, 2nd edn. Cold Spring Harbor Laboratory Press, New YorkGoogle Scholar
  2. 2.
    Perrimon N, Bernfield M (2000) Specificities of heparan sulphate proteoglycans in developmental processes. Nature 404:725–728CrossRefPubMedGoogle Scholar
  3. 3.
    Habuchi H, Habuchi O, Kimata K (2004) Sulfation pattern in glycosaminoglycan: does it have a code? Glycoconj J 21:47–52CrossRefPubMedGoogle Scholar
  4. 4.
    Sattelle B, Hansen S, Gardiner J et al (2010) Free energy landscapes of iduronic acid and related monosaccharides. J Am Chem Soc 132:13132–13134CrossRefPubMedGoogle Scholar
  5. 5.
    Imberty A, Lortat-Jacob H, Pérez S (2007) Structural view of glycosaminoglycan–protein interactions. Carbohydr Res 342:430–439CrossRefPubMedGoogle Scholar
  6. 6.
    Samsonov SA, Pisabarro MT (2016) Computational analysis of interactions in structurally available protein-glycosaminoglycan complexes. Glycobiology 26:850–861CrossRefPubMedGoogle Scholar
  7. 7.
    Sepuru KM, Nagarajan B, Desai U et al (2016) Molecular basis of chemokine CXCL5-glycosaminoglycan interactions. J Biol Chem.
  8. 8.
    Teyra J, Samsonov SA, Schreiber S et al (2011) SCOWLP update: 3D classification of protein-protein, -peptide, -saccharide and -nucleic acid interactions, and structure-based binding inferences across folds. BMC Bioinformatics 12:398CrossRefPubMedPubMedCentralGoogle Scholar
  9. 9.
    Roberts B, Mancera R (2008) Ligand-protein docking with water molecules. J Chem Inf Model 48:397–408CrossRefPubMedGoogle Scholar
  10. 10.
    Thilagavathi R, Mancera R (2010) Ligand-protein cross-docking with water molecules. J Chem Inf Model 50:415–421CrossRefPubMedGoogle Scholar
  11. 11.
    van Dijk A, Bonvin A (2006) Solvated docking: introducing water into the modelling of biomolecular complexes. Bioinformatics 22:2340–2347CrossRefPubMedGoogle Scholar
  12. 12.
    Samsonov S, Teyra J, Pisabarro MT (2008) A molecular dynamics approach to study the importance of solvent in protein interactions. Proteins 73:515–525CrossRefPubMedGoogle Scholar
  13. 13.
    Samsonov S, Teyra J, Pisabarro MT (2011) Docking glycosaminoglycans to proteins: analysis of solvent inclusion. J Comput Aided Mol Des 25:477–489CrossRefPubMedPubMedCentralGoogle Scholar
  14. 14.
    Samsonov S, Gehrcke JP, Pisabarro MT (2014) Flexibility and explicit solvent in molecular dynamics-based docking of protein-glycosaminoglycan systems. J Chem Inf Model 54:582–592CrossRefPubMedGoogle Scholar
  15. 15.
    Molecular Operating Environment (MOE), 2013.08; Chemical Computing Group Inc., 1010 Sherbooke St. West, Suite #910, Montreal, QC, Canada, H3A 2R7, 2016Google Scholar
  16. 16.
    Goodford P (1985) A computational procedure for determining energetically favorable binding sites on biologically important macromolecules. J Med Chem 28:849–857CrossRefPubMedGoogle Scholar
  17. 17.
    Case DA, Berryman JT, Betz RM et al (2015) AMBER 14. University of California, San FranciscoGoogle Scholar
  18. 18.
    Kirschner K, Yongye A, Tschampel S et al (2008) GLYCAM06: a generalizable biomolecular force field. carbohydrates. J Comput Chem 29:622–655CrossRefPubMedPubMedCentralGoogle Scholar
  19. 19.
    Ester M, Kriegel HP, Sander J et al (1996) A density-based algorithm for discovering clusters in large spatial databases with noise. In: Proceedings of 2nd international conference on knowledge discovery and data mining (KDD-96). American Association for Artificial Intelligence, Menlo Park, CAGoogle Scholar
  20. 20.
    O’Boyle NM, Banck M, James CA et al (2011) Open babel: an open chemical toolbox. J Cheminform 3:33CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Laboratory of Molecular Modeling, Department of Theoretical Chemistry, Faculty of Chemistry University of GdańskGdanskPoland

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