Back to Search Start Over

Ligand Electron Density Shape Recognition Using 3D Zernike Descriptors.

Authors :
Gunasekaran, Prasad
Grandison, Scott
Cowtan, Kevin
Mak, Lora
Lawson, David M.
Morris, Richard J.
Source :
Pattern Recognition in Bioinformatics (9783642040306); 2009, p125-136, 12p
Publication Year :
2009

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. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISBNs :
9783642040306
Database :
Complementary Index
Journal :
Pattern Recognition in Bioinformatics (9783642040306)
Publication Type :
Book
Accession number :
76739463
Full Text :
https://doi.org/10.1007/978-3-642-04031-3_12