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Receptor-specific scoring functions derived from quantum chemical models improve affinity estimates for in-silico drug discovery

Authors

  • Bernhard Fischer,

    1. Forschungszentrum Karlsruhe, Institut für Nanotechnologie, Postfach 3640, D-76021 Karlsruhe, Germany
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  • Kaori Fukuzawa,

    1. Division of Science and Technology, Mizuho Information & Research Institute, Inc., 2–3 Kanda Nishiki-cho, Chiyoda-ku, Tokyo 101-8443, Japan
    2. CREST, Japan Science and Technology Agency, 4-1-8 Honcho, Kawaguchi, Saitama 332-0012, Japan
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  • Wolfgang Wenzel

    Corresponding author
    1. Forschungszentrum Karlsruhe, Institut für Nanotechnologie, Postfach 3640, D-76021 Karlsruhe, Germany
    • Forschungszentrum Karlsruhe, Institut für Nanotechnologie, Postfach 3640, D-76021 Karlsruhe, Germany
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Abstract

The adaptation of forcefield-based scoring function to specific receptors remains an important challenge for in-silico drug discovery. Here we compare binding energies of forcefield-based scoring functions with models that are reparameterized on the basis of large-scale quantum calculations of the receptor. We compute binding energies of eleven ligands to the human estrogen receptor subtype α (ERα) and four ligands to the human retinoic acid receptor of isotype γ (RARγ). Using the FlexScreen all-atom receptor-ligand docking approach, we compare docking simulations parameterized by quantum-mechanical calculation of a large protein fragment with purely forcefield-based models. The use of receptor flexibility in the FlexScreen permits the treatment of all ligands in the same receptor model. We find a high correlation between the classical binding energy obtained in the docking simulation and quantum mechanical binding energies and a good correlation with experimental affinities R=0.81 for ERα and R=0.95 for RARγ using the quantum derived scoring functions. A significant part of this improvement is retained, when only the receptor is treated with quantum-based parameters, while the ligands are parameterized with a purely classical model. Proteins 2008. © 2007 Wiley-Liss, Inc.

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