The interactions of G-protein-coupled receptors (GPCRs) with other proteins are critical in several cellular processes but resolving their structural dynamics remains challenging. An increasing number of GPCR complexes have been experimentally resolved but others including receptor variants are yet to be characterized, necessitating computational predictions of their interactions. Although integrative approaches with multi-scale simulations would provide rigorous estimates of their conformational dynamics, protein-protein docking remains a first tool of choice of many researchers due to the availability of open-source programs and easy to use web servers with reasonable predictive power. Protein-protein docking algorithms have limited ability to consider protein flexibility, environment effects, and entropy contributions and are usually a first step towards more integrative approaches. The two critical steps of docking: the sampling and scoring algorithms have improved considerably and their performance has been validated against experimental data. In this chapter, we provide an overview and generalized protocol of a few docking protocols using GPCRs as test cases. In particular, we demonstrate the interactions of GPCRs with extracellular protein ligands and an intracellular protein effectors (G-protein) predicted from docking approaches and test their limitations. The current chapter will help researchers critically assess docking protocols and predict experimentally testable structures of GPCR complexes.

We present an approach that, by integrating structural data with Direct Coupling Analysis, is able to pinpoint most of the interaction hotspots (i.e. key residues for the biological activity) across very sparse protein families in a single run. An application to the Class A G-protein coupled receptors (GPCRs), both in their active and inactive states, demonstrates the predictive power of our approach. The latter can be easily extended to any other kind of protein family, where it is expected to highlight most key sites involved in their functional activity.

Here we review concepts related to an ensemble description of G-protein-coupled receptors (GPCRs). The ensemble is characterized by both inactive and active states, whose equilibrium populations and exchange rates depend sensitively on ligand, environment, and allosteric factors. This review focuses on the adenosine A2 receptor (A2A R), a prototypical class A GPCR. 19 F Nuclear Magnetic Resonance (NMR) studies show that apo A2A R is characterized by a broad ensemble of conformers, spanning inactive to active states, and resembling states defined earlier for rhodopsin. In keeping with ideas associated with a conformational selection mechanism, addition of agonist serves to allosterically restrict the overall degrees of freedom at the G protein binding interface and bias both states and functional dynamics to facilitate G protein binding and subsequent activation. While the ligand does not necessarily \"induce\" activation, it does bias sampling of states, increase the cooperativity of the activation process and thus, the lifetimes of functional activation intermediates, while restricting conformational dynamics to that needed for activation.

GPCR allosteric modulators target at the allosteric binding pockets of G protein-coupled receptors (GPCRs) with indirect influence on the effects of an orthosteric ligand. Such modulators exhibit significant advantages compared to the corresponding orthosteric ligands, including better chemical tractability or physicochemical properties, improved selectivity, and reduced risk of oversensitization towards their receptors. Metabotropic glutamate receptor 5 (mGlu5), a member of class C GPCRs, is a promising therapeutic target for treating many central nervous system diseases. The crystal structure of mGlu5 in the complex with the negative allosteric modulator mavoglurant was recently reported, providing a fundamental model for designing new allosteric modulators. Computational fragment-based drug discovery represents a powerful scaffold-hopping and lead structure-optimization tool for drug design. In the present work, a set of integrated computational methodologies was first used, such as fragment library generation and retrosynthetic combinatorial analysis procedure (RECAP) for novel compound generation. Then, the compounds generated were assessed by benchmark dataset verification, docking studies, and QSAR model simulation. Subsequently, structurally diverse compounds, with reported or unreported scaffolds, can be observed from top 20 in silico synthesized compounds, which were predicted to be potential mGlu5 modulators. In silico compounds with reported scaffolds may fill SAR holes in known, patented series of mGlu5 modulators. And the generation of compounds without reported tests on mGluR indicates that our approach is doable for exploring and designing novel compounds. Our case study of designing allosteric modulators on mGlu5 demonstrated that the established computational fragment-based approach is a useful methodology for facilitating new compound design in the future.

Composition of mamba\'s venom is quite atypical and characterized by the presence of a large diversity of three-finger fold toxins (3FTx) interacting with various enzymes, receptors and ion channels. In particular, 3FTx from mambas display the unique property to interact with class A GPCRs, sometimes with a high affinity and selectivity. A screening of five of these toxins (MT1, MT3, MT7, ρ-Da1a and ρ-Da1b) on 29 different subtypes of bioaminergic receptors, using competition binding experiments, highlights the diversity of their pharmacological profiles. These toxins may display either absolute selectivity for one receptor subtype or a polypharmacological property for various bioaminergic receptors. Nevertheless, adrenoceptor is the main receptor family targeted by these toxins. Furthermore, a new receptor target was identified for 3FTx and toxins in general, the ρ-Da1b interacting competitively with the human dopamine D3 receptor in the micromolar range. This result expands the diversity of GPCRs targeted by toxins and more generally highlights the multipotent interacting property of 3FTx. Phylogenic analyzes of these toxins show that muscarinic, adrenergic and dopaminergic toxins may be pooled in one family called aminergic toxins, this family coming probably from a specific radiation of ligands present in mamba venoms.