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Solvent-Site Prediction for Fragment Docking and Its Implication on Fragment-Based Drug Discovery.

Laura Almena Rodriguez1, Vera A Spanke1,2, Christian Kersten1,3

  • 1Institute of Pharmaceutical and Biomedical Sciences, Johannes Gutenberg-University Mainz, Staudingerweg 5, Mainz 55128, Germany.

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Summary

Including water molecules in docking simulations improves fragment-based drug discovery accuracy. A consensus approach using multiple docking tools and water models enhances performance for fragment redocking and cross-docking tasks.

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Area of Science:

  • Computational chemistry
  • Structural biology
  • Drug discovery

Background:

  • Accurate posing and scoring of low-affinity fragments remain challenging in fragment-based virtual screening.
  • The impact of water molecules on docking performance is frequently debated and lacks conclusive evidence.

Purpose of the Study:

  • To statistically evaluate the effect of crystallographic or predicted water molecules on fragment redocking performance.
  • To elucidate cross-docking of fragments into sites occupied by larger ligands and vice versa, mimicking realistic screening scenarios.

Main Methods:

  • Compilation of a new benchmark dataset, Frag2Lead, comprising 103 fragment-protein and lead-protein complexes.
  • Comprehensive statistical analysis of docking performance with and without water molecules.
  • Evaluation of cross-docking scenarios and the impact of constraints.

Main Results:

  • Inclusion of water molecules generally improved docking performance across various targets.
  • Optimal docking tool and water model combinations varied depending on the specific target.
  • A consensus approach combining multiple solvent models and docking tools proved beneficial for both redocking and cross-docking.

Conclusions:

  • Water molecules play a crucial role in enhancing docking accuracy for fragment-based screening.
  • A consensus strategy is recommended for robust fragment docking performance.
  • Template-based or pharmacophore-constrained docking aids pose prediction in fragment growing approaches.