Ligand-steered modeling and docking: A benchmarking study in class A G-protein-coupled receptors

Abstract

Class A G-protein-coupled receptors (GPCRs) are among the most important targets for drug discovery. However, a large set of experimental structures, essential for a structure-based approach, will likely remain unavailable in the near future. Thus, there is an actual need for modeling tools to characterize satisfactorily at least the binding site of these receptors. Using experimentally solved GPCRs, we have enhanced and validated the ligand-steered homology method through cross-modeling and investigated the performance of the thus generated models in docking-based screening. The ligand-steered modeling method uses information about existing ligands to optimize the binding site by accounting for protein flexibility. We found that our method is able to generate quality models of GPCRs by using one structural template. These models perform better than templates, crude homology models, and random selection in small-scale high-throughput docking. Better quality models typically exhibit higher enrichment in docking exercises. Moreover, they were found to be reliable for selectivity prediction. Our results support the fact that the ligand-steered homology modeling method can successfully characterize pharmacologically relevant sites through a full flexible ligand-flexible receptor procedure.