Simulating G protein-coupled receptors in native-like membranes: from monomers to oligomers

Preferential Coupling of Dopamine D2S and D2L Receptor Isoforms with Gi1 and Gi2 Proteins-In Silico Study

The dopamine D₂ receptor belongs to rhodopsin-like G protein-coupled receptors (GPCRs) and it is an important molecular target for the treatment of many disorders, including schizophrenia and Parkinson's disease. Here, computational methods were used to construct the full models of the dopamine D₂ receptor short (D_2S) and long (D_2L) isoforms (differing with 29 amino acids insertion in the third intracellular loop, ICL3) and to study their coupling with G_i1 and G_i2 proteins. It was found that the D_2L isoform preferentially couples with the G_i2 protein and D_2S isoform with the G_i1 protein, which is in accordance with experimental data. Our findings give mechanistic insight into the interplay between isoforms of dopamine D₂ receptors and G_i proteins subtypes, which is important to understand signaling by these receptors and their mediation by pharmaceuticals, in particular psychotic and antipsychotic agents.

Opportunities and Challenges in the Discovery of Allosteric Modulators of GPCRs

From the pharmacological point of view, allosteric modulators may present numerous advantages over orthosteric ligands. Growing availability of novel tools and experimental data provides a tempting opportunity to apply computational methods to improve known modulators and design novel ones. However, recent progress in understanding of complexity of allostery increases awareness of problems involved in design of modulators with desired properties. Deeper insight into phenomena such as probe dependence, altering signaling bias with minor changes in ligand structure, as well as influence of subtle endogenous allosteric factors turns out to be fundamental. These effects make the design of a modulator with precise pharmacological outcome a very challenging task, and need to be taken into consideration throughout the design process. In this chapter, we focus on nuances of targeting GPCR allosteric sites in computational drug design efforts, in particular with application of docking, virtual screening, and molecular dynamics.

Computational studies of G protein-coupled receptor complexes: Structure and dynamics

G protein-coupled receptors (GPCRs) are ubiquitously expressed transmembrane proteins associated with a wide range of diseases such as Alzheimer's, Parkinson, schizophrenia, and also implicated in in several abnormal heart conditions. As such, this family of receptors is regarded as excellent drug targets. However, due to the high number of intracellular signaling partners, these receptors have a complex interaction networks and it becomes challenging to modulate their function. Experimentally determined structures give detailed information on the salient structural properties of these signaling complexes but they are far away from providing mechanistic insights into the underlying process. This chapter presents some of the computational tools, namely molecular dynamics, molecular docking, and molecular modeling and related analyses methods that have been used to complement experimental findings.

Signaling within Allosteric Machines: Signal Transmission Pathways Inside G Protein-Coupled Receptors

In recent years, our understanding of function of G protein-coupled receptors (GPCRs) has changed from a picture of simple signal relays, transmitting only a particular signal to a particular G protein heterotrimer, to versatile machines, capable of various responses to different stimuli and being modulated by various factors. Some recent reports provide not only the data on ligands/modulators and resultant signals induced by them, but also deeper insights into exact pathways of signal migration and mechanisms of signal transmission through receptor structure. Combination of these computational and experimental data sheds more light on underlying mechanisms of signal transmission and signaling bias in GPCRs. In this review we focus on available clues on allosteric pathways responsible for complex signal processing within GPCRs structures, with particular emphasis on linking compatible in silico- and in vitro-derived data on the most probable allosteric connections.

Recent Advances and Applications of Molecular Docking to G Protein-Coupled Receptors

The growing number of studies on G protein-coupled receptors (GPCRs) family are a source of noticeable improvement in our understanding of the functioning of these proteins. GPCRs are responsible for a vast part of signaling in vertebrates and, as such, invariably remain in the spotlight of medicinal chemistry. A deeper insight into the underlying mechanisms of interesting phenomena observed in GPCRs, such as biased signaling or allosteric modulation, can be gained with experimental and computational studies. The latter play an important role in this process, since they allow for observations on scales inaccessible for most other methods. One of the key steps in such studies is proper computational reconstruction of actual ligand-receptor or protein-protein interactions, a process called molecular docking. A number of improvements and innovative applications of this method were documented recently. In this review, we focus particularly on innovations in docking to GPCRs.

In vitro, molecular modeling and behavioral studies of 3-{[4-(5-methoxy-1H-indol-3-yl)-1,2,3,6-tetrahydropyridin-1-yl]methyl}-1,2-dihydroquinolin-2-one (D2AAK1) as a potential antipsychotic

Antipsychotics currently available to treat schizophrenia suffer several limitations: (1) they are efficient against positive but not negative and cognitive symptoms of the disease; (2) they help only a half of patients; (3) they have severe side effects including neurological and metabolic side effects. Thus, novel drugs to treat schizophrenia are highly demanded. We identified a novel dopamine D2 receptor antagonist, D2AAK1, with Ki of 58 nM using structure-based virtual screening. D2AAK1 possesses additional nanomolar or low micromolar affinity to D1, D3, 5-HT1A and 5-HT2A receptors, making it an ideal candidate for a multi-target drug. Here we present homology modeling, molecular docking and molecular dynamics of D2AAK1 and its molecular targets and animal studies of D2AAK1 as a potential antipsychotic. The main contact of D2AAK1 and all the receptors studied is the electrostatic interaction between the protonable nitrogen atom of the ligand and the conserved Asp(3.32) as typical for orthosteric ligands of aminergic GPCRs. We confirmed antagonistic/partial agonistic properties of D2AAK1 towards all the receptors in in vitro essays and in in silico studies as the ligand stabilizes the ionic lock interaction. We also demonstrated neuroleptic, anxiolytic and, importantly, procognitive properties of D2AAK1 in mouse models.

Computational approaches for modeling GPCR dimerization

Growing experimental evidences suggest that dimerization and oligomerization are important for G Protein- Coupled Receptors (GPCRs) function. The detailed structural information of dimeric/oligomeric GPCRs would be very important to understand their function. Although it is encouraging that recently several experimental GPCR structures in oligomeric forms have appeared, experimental determination of GPCR structures in oligomeric forms is still a big challenge, especially in mimicking the membrane environment. Therefore, development of computational approaches to predict dimerization of GPCRs will be highly valuable. In this review, we summarize computational approaches that have been developed and used for modeling of GPCR dimerization. In addition, we introduce a novel two-dimensional Brownian Dynamics based protein docking approach, which we have recently adapted, for GPCR dimer prediction.

Simulation tools for particle-based reaction-diffusion dynamics in continuous space

Particle-based reaction-diffusion algorithms facilitate the modeling of the diffusional motion of individual molecules and the reactions between them in cellular environments. A physically realistic model, depending on the system at hand and the questions asked, would require different levels of modeling detail such as particle diffusion, geometrical confinement, particle volume exclusion or particle-particle interaction potentials. Higher levels of detail usually correspond to increased number of parameters and higher computational cost. Certain systems however, require these investments to be modeled adequately. Here we present a review on the current field of particle-based reaction-diffusion software packages operating on continuous space. Four nested levels of modeling detail are identified that capture incrementing amount of detail. Their applicability to different biological questions is discussed, arching from straight diffusion simulations to sophisticated and expensive models that bridge towards coarse grained molecular dynamics.