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Receptor‐specific scoring functions derived from quantum chemical models improve affinity estimates for in‐silico drug discovery
Author(s) -
Fischer Bernhard,
Fukuzawa Kaori,
Wenzel Wolfgang
Publication year - 2008
Publication title -
proteins: structure, function, and bioinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.699
H-Index - 191
eISSN - 1097-0134
pISSN - 0887-3585
DOI - 10.1002/prot.21607
Subject(s) - in silico , computational biology , drug discovery , quantum chemical , drug , computer science , biological system , biology , chemistry , biochemical engineering , bioinformatics , pharmacology , genetics , engineering , molecule , gene , organic chemistry
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.