Smooth statistical torsion angle potential derived from a large conformational database via adaptive kernel density estimation improves the quality of NMR protein structures

Authors

  • Guillermo A. Bermejo,

    1. Division of Computational Bioscience, Center for Information Technology, National Institutes of Health, Bethesda, Maryland 20892-5624
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  • G. Marius Clore,

    1. Laboratory of Chemical Physics, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, Maryland 20892-0520
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  • Charles D. Schwieters

    Corresponding author
    1. Division of Computational Bioscience, Center for Information Technology, National Institutes of Health, Bethesda, Maryland 20892-5624
    • Division of Computational Bioscience, Center for Information Technology, National Institutes of Health, 12 South Dr., Building 12A, Room 2041, Bethesda, MD 20892-5624
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Abstract

Statistical potentials that embody torsion angle probability densities in databases of high-quality X-ray protein structures supplement the incomplete structural information of experimental nuclear magnetic resonance (NMR) datasets. By biasing the conformational search during the course of structure calculation toward highly populated regions in the database, the resulting protein structures display better validation criteria and accuracy. Here, a new statistical torsion angle potential is developed using adaptive kernel density estimation to extract probability densities from a large database of more than 106 quality-filtered amino acid residues. Incorporated into the Xplor-NIH software package, the new implementation clearly outperforms an older potential, widely used in NMR structure elucidation, in that it exhibits simultaneously smoother and sharper energy surfaces, and results in protein structures with improved conformation, nonbonded atomic interactions, and accuracy.

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