Automated identification of crystallographic ligands using sparse-density representations

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.