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Ligand Electron Density Shape Recognition Using 3D Zernike Descriptors

  • Prasad Gunasekaran
  • Scott Grandison
  • Kevin Cowtan
  • Lora Mak
  • David M. Lawson
  • Richard J. Morris
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5780)

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.

Keywords

pattern recognition structural bioinformatics electron density protein crystallography 3D Zernike moments 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Prasad Gunasekaran
    • 1
  • Scott Grandison
    • 1
  • Kevin Cowtan
    • 2
  • Lora Mak
    • 3
  • David M. Lawson
    • 4
  • Richard J. Morris
    • 1
  1. 1.Department of Computational & Systems BiologyJohn Innes CentreNorwichUK
  2. 2.Structural Biology Laboratory, Department of ChemistryUniversity of York, HeslingtonYorkUK
  3. 3.Theoretical Systems BiologyInstitute of Food ResearchNorwichUK
  4. 4.Department of Biological ChemistryJohn Innes CentreNorwichUK

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