Fragmentation-tree density representation for crystallographic modelling of bound ligands.

The identification and modelling of ligands into macromolecular models is important for understanding molecule's function and for designing inhibitors to modulate its activities. We describe new algorithms for the automated building of ligands into electron density maps in crystal structure determination. Location of the ligand-binding site is achieved by matching numerical shape features describing the ligand to those of density clusters using a "fragmentation-tree" density representation. The ligand molecule is built using two distinct algorithms exploiting free atoms with inter-atomic connectivity and Metropolis-based optimisation of the conformational state of the ligand, producing an ensemble of structures from which the final model is derived. The method was validated on several thousand entries from the Protein Data Bank. In the majority of cases, the ligand-binding site could be correctly located and the ligand model built with a coordinate accuracy of better than 1 Å. We anticipate that the method will be of routine use to anyone modelling ligands, lead compounds or even compound fragments as part of protein functional analyses or drug design efforts.

Assessment of automatic ligand building in ARP/wARP.

The efficiency of the ligand-building module of ARP/wARP version 6.1 has been assessed through extensive tests on a large variety of protein-ligand complexes from the PDB, as available from the Uppsala Electron Density Server. Ligand building in ARP/wARP involves two main steps: automatic identification of the location of the ligand and the actual construction of its atomic model. The first step is most successful for large ligands. The second step, ligand construction, is more powerful with X-ray data at high resolution and ligands of small to medium size. Both steps are successful for ligands with low to moderate atomic displacement parameters. The results highlight the strengths and weaknesses of both the method of ligand building and the large-scale validation procedure and help to identify means of further improvement.