Optimization of High Throughput Virtual Screening by Combining Shape-Matching and Docking Methods

Receptor flexibility is a critical issue in structure-based virtual screening methods. Although a multiple-receptor conformation docking is an efficient way to account for receptor flexibility, it is still too slow for
large molecular libraries. It was reported that a fast ligand-centric, shape-based virtual screening was more
consistent for hit enrichment than a typical single-receptor conformation docking. Thus, we designed a
“distributed docking” method that improves virtual high throughput screening by combining a shape-matching
method with a multiple-receptor conformation docking. Database compounds are classified in advance based
on shape similarities to one of the crystal ligands complexed with the target protein. This classification
enables us to pick the appropriate receptor conformation for a single-receptor conformation docking of a
given compound, thereby avoiding time-consuming multiple docking. In particular, this approach utilizes
cross-docking scores of known ligands to all available receptor structures in order to optimize the algorithm.
The present virtual screening method was tested for reidentification of known PPARγ and p38 MAP kinase
active compounds. We demonstrate that this method improves the enrichment while maintaining the
computation speed of a typical single-receptor conformation docking.