Computational Strategies to Incorporate GPCR Complexity in Drug Design

G protein-coupled receptors (GPCRs) represent the most important family of drug targets to date. However, state-of-the-art experimental procedures, able to characterize in deep both GPCR modulation in health and disease and the molecular mechanisms of drug action at these receptors, have provided a more nuanced picture than previously expected. Several aspects of GPCR function, which are currently being characterized, clarify some regulatory processes regarding these receptors and, at the same time, introduce novel levels of complexity which should be taken into consideration for rational drug design. In this scenario, computational approaches can help in several ways rationalize the increasing amount of data on GPCRs and their ligands. On the one hand, a set of databases devoted to these receptors provide excellent starting points for data mining. On the other, exploitation of the ever-increasing ligand and structure-based information by novel computational techniques can help addressing emerging questions in the GPCR field. Some of these questions comprise the refined modulation of GPCR signaling states by biased agonists, the exploitation of GPCR oligomers as drug targets, the analysis of polypharmacology in GPCR ligands, the development of strategies for receptor deorphanization or the prediction of off-target interactions of known drugs targeting these receptors. In this chapter, we will cover some of these strategies for knowledge-based rational design for GPCRs and will discuss the main hurdles which they may need to overcome to yield novel, safer and more efficacious drugs possessing polished mechanisms of action.

Keywords: Allosteric modulation, biased agonism, chemogenomics, drug design, G protein-coupled receptors, homology modeling, ligand promiscuity, molecular dynamics, oligomerization, virtual screening.