Automated identification of crystallographic ligands using sparse-density representations
A novel procedure for identifying ligands in macromolecular crystallographic electron-density maps is introduced. Density clusters in such maps can be rapidly attributed to one of 82 different ligands in an automated manner. A novel procedure for the automatic identification of ligands in macromolecular crystallographic electron-density maps is introduced. It is based on the sparse parameterization of density clusters and the matching of the pseudo-atomic grids thus created to conformationally variant ligands using mathematical descriptors of molecular shape, size and topology. In large-scale tests on experimental data derived from the Protein Data Bank, the procedure could quickly identify the deposited ligand within the top-ranked compounds from a database of candidates. This indicates the suitability of the method for the identification of binding entities in fragment-based drug screening and in model completion in macromolecular structure determination.
- OSTI ID:
- 22347770
- Journal Information:
- Acta Crystallographica. Section D: Biological Crystallography, Vol. 70, Issue Pt 7; Other Information: PMCID: PMC4089483; PMID: 25004962; PUBLISHER-ID: wd5232; OAI: oai:pubmedcentral.nih.gov:4089483; Copyright (c) Carolan & Lamzin 2014; This is an open-access article distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original authors and source are cited.; Country of input: International Atomic Energy Agency (IAEA); ISSN 0907-4449
- Country of Publication:
- Denmark
- Language:
- English
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