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EUDOC: a computer program for identification of drug interaction sites in macromolecules and drug leads from chemical databases

Authors

  • Yuan-Ping Pang,

    Corresponding author
    1. Mayo Clinic Cancer Center, Mayo Foundation for Medical Education and Research, 200 First Street SW, Rochester, Minnesota 55905
    2. Tumor Biology Program, Mayo Foundation for Medical Education and Research, 200 First Street SW, Rochester, Minnesota 55905
    3. Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Foundation for Medical Education and Research, 200 First Street SW, Rochester, Minnesota 55905
    4. Molecular Neuroscience Program, Mayo Foundation for Medical Education and Research, 200 First Street SW, Rochester, Minnesota 55905
    • Mayo Clinic Cancer Center, Mayo Foundation for Medical Education and Research, 200 First Street SW, Rochester, Minnesota 55905, Tumor Biology Program, Mayo Foundation for Medical Education and Research, 200 First Street SW, Rochester, Minnesota 55905, Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Foundation for Medical Education and Research, 200 First Street SW, Rochester, Minnesota 55905, Molecular Neuroscience Program, Mayo Foundation for Medical Education and Research, 200 First Street SW, Rochester, Minnesota 55905
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  • Emanuele Perola,

    1. Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Foundation for Medical Education and Research, 200 First Street SW, Rochester, Minnesota 55905
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  • Kun Xu,

    1. Tumor Biology Program, Mayo Foundation for Medical Education and Research, 200 First Street SW, Rochester, Minnesota 55905
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  • Franklyn G. Prendergast

    1. Mayo Clinic Cancer Center, Mayo Foundation for Medical Education and Research, 200 First Street SW, Rochester, Minnesota 55905
    2. Tumor Biology Program, Mayo Foundation for Medical Education and Research, 200 First Street SW, Rochester, Minnesota 55905
    3. Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Foundation for Medical Education and Research, 200 First Street SW, Rochester, Minnesota 55905
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Abstract

The completion of the Human Genome Project, the growing effort on proteomics, and the Structural Genomics Initiative have recently intensified the attention being paid to reliable computer docking programs able to identify molecules that can affect the function of a macromolecule through molecular complexation. We report herein an automated computer docking program, EUDOC, for prediction of ligand–receptor complexes from 3D receptor structures, including metalloproteins, and for identification of a subset enriched in drug leads from chemical databases. This program was evaluated from the standpoints of force field and sampling issues using 154 experimentally determined ligand–receptor complexes and four “real-life” applications of the EUDOC program. The results provide evidence for the reliability and accuracy of the EUDOC program. In addition, key principles underlying molecular recognition, and the effects of structural water molecules in the active site and different atomic charge models on docking results are discussed. © 2001 John Wiley & Sons, Inc. J Comput Chem 22: 1750–1771, 2001

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