Format

Send to

Choose Destination
J Comput Aided Mol Des. 2011 May;25(5):477-89. doi: 10.1007/s10822-011-9433-1. Epub 2011 May 20.

Docking glycosaminoglycans to proteins: analysis of solvent inclusion.

Author information

1
Structural Bioinformatics, BIOTEC TU Dresden, Tatzberg, Germany. sergeys@biotec.tu-dresden.de

Abstract

Glycosaminoglycans (GAGs) are anionic polysaccharides, which participate in key processes in the extracellular matrix by interactions with protein targets. Due to their charged nature, accurate consideration of electrostatic and water-mediated interactions is indispensable for understanding GAGs binding properties. However, solvent is often overlooked in molecular recognition studies. Here we analyze the abundance of solvent in GAG-protein interfaces and investigate the challenges of adding explicit solvent in GAG-protein docking experiments. We observe PDB GAG-protein interfaces being significantly more hydrated than protein-protein interfaces. Furthermore, by applying molecular dynamics approaches we estimate that about half of GAG-protein interactions are water-mediated. With a dataset of eleven GAG-protein complexes we analyze how solvent inclusion affects Autodock 3, eHiTs, MOE and FlexX docking. We develop an approach to de novo place explicit solvent into the binding site prior to docking, which uses the GRID program to predict positions of waters and to locate possible areas of solvent displacement upon ligand binding. To investigate how solvent placement affects docking performance, we compare these results with those obtained by taking into account information about the solvent position in the crystal structure. In general, we observe that inclusion of solvent improves the results obtained with these methods. Our data show that Autodock 3 performs best, though it experiences difficulties to quantitatively reproduce experimental data on specificity of heparin/heparan sulfate disaccharides binding to IL-8. Our work highlights the current challenges of introducing solvent in protein-GAGs recognition studies, which is crucial for exploiting the full potential of these molecules for rational engineering.

PMID:
21597992
PMCID:
PMC3107433
DOI:
10.1007/s10822-011-9433-1
[Indexed for MEDLINE]
Free PMC Article

Supplemental Content

Full text links

Icon for Springer Icon for PubMed Central
Loading ...
Support Center