Computational Analysis of Solvent Inclusion in Docking Studies of Protein–Glycosaminoglycan Systems
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 wordsAtomic 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.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
- 7.Sepuru KM, Nagarajan B, Desai U et al (2016) Molecular basis of chemokine CXCL5-glycosaminoglycan interactions. J Biol Chem. https://doi.org/10.1074/jbc.M116.745265
- 15.Molecular Operating Environment (MOE), 2013.08; Chemical Computing Group Inc., 1010 Sherbooke St. West, Suite #910, Montreal, QC, Canada, H3A 2R7, 2016Google Scholar
- 17.Case DA, Berryman JT, Betz RM et al (2015) AMBER 14. University of California, San FranciscoGoogle Scholar
- 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