Ligand Electron Density Shape Recognition Using 3D Zernike Descriptors View Full Text


Ontology type: schema:Chapter      Open Access: True


Chapter Info

DATE

2009

AUTHORS

Prasad Gunasekaran , Scott Grandison , Kevin Cowtan , Lora Mak , David M. Lawson , Richard J. Morris

ABSTRACT

We present a novel approach to crystallographic ligand density interpretation based on Zernike shape descriptors. Electron density for a bound ligand is expanded in an orthogonal polynomial series (3D Zernike polynomials) and the coefficients from this expansion are employed to construct rotation-invariant descriptors. These descriptors can be compared highly efficiently against large databases of descriptors computed from other molecules. In this manuscript we describe this process and show initial results from an electron density interpretation study on a dataset containing over a hundred OMIT maps. We could identify the correct ligand as the first hit in about 30 % of the cases, within the top five in a further 30 % of the cases, and giving rise to an 80 % probability of getting the correct ligand within the top ten matches. In all but a few examples, the top hit was highly similar to the correct ligand in both shape and chemistry. Further extensions and intrinsic limitations of the method are discussed. More... »

PAGES

125-136

References to SciGraph publications

  • 1999-05-01. Automated protein model building combined with iterative structure refinement in NATURE STRUCTURAL & MOLECULAR BIOLOGY
  • Book

    TITLE

    Pattern Recognition in Bioinformatics

    ISBN

    978-3-642-04030-6
    978-3-642-04031-3

    Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/978-3-642-04031-3_12

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    http://dx.doi.org/10.1007/978-3-642-04031-3_12

    DIMENSIONS

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