Name
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. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1007/978-3-642-04031-3_12 | PRIB |
Keywords | Field | DocType |
zernike shape descriptors,ligand density interpretation,correct ligand,ligand electron density shape,hundred omit map,top hit,electron density interpretation study,electron density,zernike polynomial,rotation-invariant descriptors,zernike descriptors,bound ligand,pattern recognition,orthogonal polynomial,structural bioinformatics,protein crystallography | Structural bioinformatics,Electron density,Pattern recognition,Orthogonal polynomials,Ligand,Zernike polynomials,Artificial intelligence,Geometry,Machine learning,Mathematics | Conference |
Volume | ISSN | Citations |
5780 | 0302-9743 | 1 |
PageRank | References | Authors |
0.37 | 5 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Prasad Gunasekaran | 1 | 112 | 8.46 |
| Scott Grandison | 2 | 5 | 1.24 |
| Kevin Cowtan | 3 | 1 | 0.37 |
| Lora Mak | 4 | 204 | 7.97 |
| David M. R. Lawson | 5 | 3 | 1.82 |
| Richard J Morris | 6 | 89 | 7.21 |