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Docking software performance in protein-glycosaminoglycan systems.

Urszula Uciechowska-Kaczmarzyk1, Isaure Chauvot de Beauchene2, Sergey A Samsonov1

  • 1Laboratory of Molecular Modeling, Department of Theoretical Chemistry, Faculty of Chemistry, University of Gdansk, Wita Stwosza 63, 80-308, Gdańsk, Poland.

Journal of Molecular Graphics & Modelling
|April 9, 2019
PubMed
Summary

This study benchmarks eight protein-glycosaminoglycan docking programs. Docking program Dock showed the best performance in predicting binding poses and scoring, independent of glycosaminoglycan ligand size.

Keywords:
Binding poseModeling glycosaminoglycansMolecular dockingProtein-glycosaminoglycan interactionsScoring function

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Area of Science:

  • Computational biology
  • Structural bioinformatics
  • Molecular modeling

Background:

  • Protein-glycosaminoglycan interactions are crucial in various biological processes.
  • Accurate computational modeling of these interactions is essential for understanding biological mechanisms and drug discovery.
  • Existing docking programs require evaluation for their efficacy in modeling glycosaminoglycan ligands.

Purpose of the Study:

  • To benchmark the performance of eight popular docking programs for protein-glycosaminoglycan systems.
  • To identify the most effective docking program for predicting binding poses and scoring of glycosaminoglycan ligands.
  • To analyze the influence of ligand size and free energy patterns on docking performance.

Main Methods:

  • A non-redundant dataset of 28 protein-glycosaminoglycan complexes with experimentally determined structures was curated.
  • Eight docking programs (Dock, rDock, ClusPro, PLANTS, HADDOCK, Hex, SwissDock, and ATTRACT) were evaluated.
  • The ability of each program to predict correct ligand binding poses and rank them accurately was assessed.
  • The impact of ligand size (longer than a trimer) and free energy patterns on docking performance was analyzed.

Main Results:

  • Several docking programs successfully predicted ligand binding poses in many cases.
  • However, the accurate ranking of these poses was often poor across most programs.
  • The Dock program demonstrated superior performance in pose placement and scoring, with results independent of ligand size.
  • The effectiveness of Dock was attributed to its implementation of electrostatics and shape complementarity.

Conclusions:

  • The Dock program is recommended for modeling protein-glycosaminoglycan interactions due to its robust performance.
  • Further development in scoring functions and electrostatic implementations may improve other docking programs for glycosaminoglycans.
  • Understanding free energy patterns is crucial for optimizing docking software for complex biomolecular systems.