Comparative Analysis of Protein-Bound Ligand Conformations with Respect to Catalyst's Conformational Space Subsampling Algorithms

We examined the quality of Catalyst's conformational model generation algorithm via a large scale study
based on the crystal structures of a sample of 510 pharmaceutically relevant protein−ligand complexes
extracted from the Protein Data Bank (PDB). Our results show that the tested algorithms implemented
within Catalyst are able to produce high quality conformers, which in most of the cases are well suited for
in silico drug research. Catalyst-specific settings were analyzed, such as the method used for the conformational model generation (FAST vs BEST) and the maximum number of generated conformers. By
setting these options for higher fitting quality, the average RMS values describing the similarity of experimental and simulated conformers were improved from an RMS of 1.06 with max. 50 FAST generated
conformers to an RMS of 0.93 with max. 255 BEST generated conformers, which represents an improvement
by 12%. Each method provides best fitting conformers with an RMS value < 1.50 in more than 80% of all
cases. We analyzed the computing time/quality ratio of various conformational model generation settings
and examined ligands in high energy conformations. Furthermore, properties of the same ligands in various
proteins were investigated, and the fitting qualities of experimental conformations from the PDB and the
Cambridge Structural Database (CSD) were compared. One of the most important conclusions of former
studies, the fact that bioactive conformers often have energy high above that of global minima, was confirmed.