Docking-based virtual screening for ligands of G protein-coupled receptors: Not only crystal structures but also in silico models
G protein-coupled receptors (GPCRs) regulate a wide range of physiological functions and hold great pharmaceutical interest. Using the β2-adrenergic receptor as a case study, this article explores the applicability of docking-based virtual screening to the discovery of GPCR ligands and defines methods intended to improve the screening performance. Their controlled computational experiments were performed on a compound dataset containing known agonists and blockers of the receptor as well as many decoys. The screening based on the structure of the receptor crystallized in complex with its inverse agonist carazolol yielded excellent results, with a clearly delineated prioritization of ligands over decoys. Blockers generally were preferred over agonists; however, agonists were also well distinguished from decoys. A method was devised to increase the screening yields by generating an ensemble of alternative conformations of the receptor that accounts for its flexibility. Moreover, a method was devised to improve the retrieval of agonists, based on the optimization of the receptor around a known agonist. Finally, the applicability of docking-based virtual screening also to homology models endowed with different levels of accuracy was proved. This last point is of uttermost importance, since crystal structures are available only for a limited number of GPCRs, and extends their conclusions to the entire superfamily. The outcome of this analysis definitely supports the application of computer-aided techniques to the discovery of novel GPCR ligands, especially in light of the fact that, in the near future, experimental structures are expected to be solved and become available for an ever increasing number of GPCRs.
G protein-coupled receptors (GPCRs) constitute the largest superfamily of human membrane signaling proteins. They are implicated in a vast array of physiological functions and pathological conditions and, thus, are highly pursued as targets for pharmacological intervention.
Due to difficulties inherent the crystallization of GPCRs, for years bovine rhodopsin has been the only member of the superfamily with an experimentally elucidated three-dimensional structure, and has been employed as a template for the construction of three-dimensional (3D) homology models. More recently, however, scientific breakthroughs yielded to the solution of the crystal structures of a few additional receptors, including the β2-adrenergic receptor (β2-AR), while structures of additional receptors are expected to be solved in the near future.
In this work, using the β2-AR as a case study, they investigated the applicability of crystal structures and homology models endowed with different levels of accuracy to the identification of GPCR ligands, and defined methods intended to improve screening performance. They added a pool of known binders of the receptor to a large set of decoy compounds, and subsequently analyzed the ability of a series of controlled docking-based virtual screening experiments to prioritize ligands over decoys.
In particular, they: (a) assessed the excellent results attainable with the crystal structure of the receptor in complex with the inverse agonist carazolol; (b) devised a method that managed to further improve the results through the generation of an ensemble of alternative conformations of the receptor that accounts for its flexibility – general importance: very rarely multiple crystal structures of a GPCR are available and can be used to account for its flexibility; (c) defined a method to invert the tendency of virtual screening to prioritize blockers over agonists, by optimizing the structure of the receptor around bound agonists – general importance: to date, no GPCR has been crystallized in complex with an agonist; (d) assessed the feasibility of the use of homology models in lieu of experimental structures, in the absence of the latter – general importance: for the vast majority of GPCRs crystal structures are not available.
Flowchart representation of the different procedures presented in this study.
Vilar, S., Ferino, G., Phatak, S. S., Berk, B., Cavasotto, C. N., & Costanzi, S. (2011). Docking-based virtual screening for ligands of G protein-coupled receptors: Not only crystal structures but also in silico models. Journal of Molecular Graphics and Modelling, 29(5), 614–623. doi:10.1016/j.jmgm.2010.11.005