Plasma membrane preassociation drives ß-arrestin coupling to receptors and activation.

ß-arrestin plays a key role in G protein-coupled receptor (GPCR) signaling and desensitization. Despite recent structural advances, the mechanisms that govern receptor-ß-arrestin interactions at the plasma membrane of living cells remain elusive. Here, we combine single-molecule microscopy with molecular dynamics simulations to dissect the complex sequence of events involved in ß-arrestin interactions with both receptors and the lipid bilayer. Unexpectedly, our results reveal that ß-arrestin spontaneously inserts into the lipid bilayer and transiently interacts with receptors via lateral diffusion on the plasma membrane. Moreover, they indicate that, following receptor interaction, the plasma membrane stabilizes ß-arrestin in a longer-lived, membrane-bound state, allowing it to diffuse to clathrin-coated pits separately from the activating receptor. These results expand our current understanding of ß-arrestin function at the plasma membrane, revealing a critical role for ß-arrestin preassociation with the lipid bilayer in facilitating its interactions with receptors and subsequent activation.

Pharmacological fingerprint of antipsychotic drugs at the serotonin 5-HT2A receptor.

The intricate involvement of the serotonin 5-HT2A receptor (5-HT2AR) both in schizophrenia and in the activity of antipsychotic drugs is widely acknowledged. The currently marketed antipsychotic drugs, although effective in managing the symptoms of schizophrenia to a certain extent, are not without their repertoire of serious side effects. There is a need for better therapeutics to treat schizophrenia for which understanding the mechanism of action of the current antipsychotic drugs is imperative. With bioluminescence resonance energy transfer (BRET) assays, we trace the signaling signature of six antipsychotic drugs belonging to three generations at the 5-HT2AR for the entire spectrum of signaling pathways activated by serotonin (5-HT). The antipsychotic drugs display previously unidentified pathway preference at the level of the individual Gα subunits and ß-arrestins. In particular, risperidone, clozapine, olanzapine and haloperidol showed G protein-selective inverse agonist activity. In addition, G protein-selective partial agonism was found for aripiprazole and cariprazine. Pathway-specific apparent dissociation constants determined from functional analyses revealed distinct coupling-modulating capacities of the tested antipsychotics at the different 5-HT-activated pathways. Computational analyses of the pharmacological and structural fingerprints support a mechanistically based clustering that recapitulate the clinical classification (typical/first generation, atypical/second generation, third generation) of the antipsychotic drugs. The study provides a new framework to functionally classify antipsychotics that should represent a useful tool for the identification of better and safer neuropsychiatric drugs and allows formulating hypotheses on the links between specific signaling cascades and in the clinical outcomes of the existing drugs.

Impact of membrane lipid polyunsaturation on dopamine D2 receptor ligand binding and signaling.

Increasing evidence supports a relationship between lipid metabolism and mental health. In particular, the biostatus of polyunsaturated fatty acids (PUFAs) correlates with some symptoms of psychiatric disorders, as well as the efficacy of pharmacological treatments. Recent findings highlight a direct association between brain PUFA levels and dopamine transmission, a major neuromodulatory system implicated in the etiology of psychiatric symptoms. However, the mechanisms underlying this relationship are still unknown. Here we demonstrate that membrane enrichment in the n-3 PUFA docosahexaenoic acid (DHA), potentiates ligand binding to the dopamine D2 receptor (D2R), suggesting that DHA acts as an allosteric modulator of this receptor. Molecular dynamics simulations confirm that DHA has a high preference for interaction with the D2R and show that membrane unsaturation selectively enhances the conformational dynamics of the receptor around its second intracellular loop. We find that membrane unsaturation spares G protein activity but potentiates the recruitment of ß-arrestin in cells. Furthermore, in vivo n-3 PUFA deficiency blunts the behavioral effects of two D2R ligands, quinpirole and aripiprazole. These results highlight the importance of membrane unsaturation for D2R activity and provide a putative mechanism for the ability of PUFAs to enhance antipsychotic efficacy.

SCoV2-MD: a database for the dynamics of the SARS-CoV-2 proteome and variant impact predictions.

SCoV2-MD (www.scov2-md.org) is a new online resource that systematically organizes atomistic simulations of the SARS-CoV-2 proteome. The database includes simulations produced by leading groups using molecular dynamics (MD) methods to investigate the structure-dynamics-function relationships of viral proteins. SCoV2-MD cross-references the molecular data with the pandemic evolution by tracking all available variants sequenced during the pandemic and deposited in the GISAID resource. SCoV2-MD enables the interactive analysis of the deposited trajectories through a web interface, which enables users to search by viral protein, isolate, phylogenetic attributes, or specific point mutation. Each mutation can then be analyzed interactively combining static (e.g. a variety of amino acid substitution penalties) and dynamic (time-dependent data derived from the dynamics of the local geometry) scores. Dynamic scores can be computed on the basis of nine non-covalent interaction types, including steric properties, solvent accessibility, hydrogen bonding, and other types of chemical interactions. Where available, experimental data such as antibody escape and change in binding affinities from deep mutational scanning experiments are also made available. All metrics can be combined to build predefined or custom scores to interrogate the impact of evolving variants on protein structure and function.

GPCRmd uncovers the dynamics of the 3D-GPCRome.

G-protein-coupled receptors (GPCRs) are involved in numerous physiological processes and are the most frequent targets of approved drugs. The explosion in the number of new three-dimensional (3D) molecular structures of GPCRs (3D-GPCRome) over the last decade has greatly advanced the mechanistic understanding and drug design opportunities for this protein family. Molecular dynamics (MD) simulations have become a widely established technique for exploring the conformational landscape of proteins at an atomic level. However, the analysis and visualization of MD simulations require efficient storage resources and specialized software. Here we present GPCRmd (http://gpcrmd.org/), an online platform that incorporates web-based visualization capabilities as well as a comprehensive and user-friendly analysis toolbox that allows scientists from different disciplines to visualize, analyze and share GPCR MD data. GPCRmd originates from a community-driven effort to create an open, interactive and standardized database of GPCR MD simulations.

Publisher Correction: GPCRmd uncovers the dynamics of the 3D-GPCRome.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

On the construction of LIECE models for the serotonin receptor 5-HT[Formula: see text]R.

Computer-aided approaches to ligand design need to balance accuracy with speed. This is particularly true for one of the key parameters to be optimized during ligand development, the free energy of binding ([Formula: see text]G[Formula: see text]). Here, we developed simple models based on the Linear Interaction Energy approximation to free energy calculation for a G protein-coupled receptor, the serotonin receptor 2A, and critically evaluated their accuracy. Several lessons can be taken from our calculations, providing information on the influence of the docking software used, the conformational state of the receptor, the cocrystallized ligand, and its comparability to the training/test ligands.

Can molecular dynamics simulations improve the structural accuracy and virtual screening performance of GPCR models?

The determination of G protein-coupled receptor (GPCR) structures at atomic resolution has improved understanding of cellular signaling and will accelerate the development of new drug candidates. However, experimental structures still remain unavailable for a majority of the GPCR family. GPCR structures and their interactions with ligands can also be modelled computationally, but such predictions have limited accuracy. In this work, we explored if molecular dynamics (MD) simulations could be used to refine the accuracy of in silico models of receptor-ligand complexes that were submitted to a community-wide assessment of GPCR structure prediction (GPCR Dock). Two simulation protocols were used to refine 30 models of the D3 dopamine receptor (D3R) in complex with an antagonist. Close to 60 µs of simulation time was generated and the resulting MD refined models were compared to a D3R crystal structure. In the MD simulations, the receptor models generally drifted further away from the crystal structure conformation. However, MD refinement was able to improve the accuracy of the ligand binding mode. The best refinement protocol improved agreement with the experimentally observed ligand binding mode for a majority of the models. Receptor structures with improved virtual screening performance, which was assessed by molecular docking of ligands and decoys, could also be identified among the MD refined models. Application of weak restraints to the transmembrane helixes in the MD simulations further improved predictions of the ligand binding mode and second extracellular loop. These results provide guidelines for application of MD refinement in prediction of GPCR-ligand complexes and directions for further method development.

Key phosphorylation sites in GPCRs orchestrate the contribution of ß-Arrestin 1 in ERK1/2 activation.

ß-arrestins (ßarrs) are key regulators of G protein-coupled receptor (GPCR) signaling and trafficking, and their knockdown typically leads to a decrease in agonist-induced ERK1/2 MAP kinase activation. Interestingly, for some GPCRs, knockdown of ßarr1 augments agonist-induced ERK1/2 phosphorylation although a mechanistic basis for this intriguing phenomenon is unclear. Here, we use selected GPCRs to explore a possible correlation between the spatial positioning of receptor phosphorylation sites and the contribution of ßarr1 in ERK1/2 activation. We discover that engineering a spatially positioned double-phosphorylation-site cluster in the bradykinin receptor (B2 R), analogous to that present in the vasopressin receptor (V2 R), reverses the contribution of ßarr1 in ERK1/2 activation from inhibitory to promotive. An intrabody sensor suggests a conformational mechanism for this role reversal of ßarr1, and molecular dynamics simulation reveals a bifurcated salt bridge between this double-phosphorylation site cluster and Lys294 in the lariat loop of ßarr1, which directs the orientation of the lariat loop. Our findings provide novel insights into the opposite roles of ßarr1 in ERK1/2 activation for different GPCRs with a direct relevance to biased agonism and novel therapeutics.

In Silico Study of Allosteric Communication Networks in GPCR Signaling Bias.

Signaling bias is a promising characteristic of G protein-coupled receptors (GPCRs) as it provides the opportunity to develop more efficacious and safer drugs. This is because biased ligands can avoid the activation of pathways linked to side effects whilst still producing the desired therapeutic effect. In this respect, a deeper understanding of receptor dynamics and implicated allosteric communication networks in signaling bias can accelerate the research on novel biased drug candidates. In this review, we aim to provide an overview of computational methods and techniques for studying allosteric communication and signaling bias in GPCRs. This includes (i) the detection of allosteric communication networks and (ii) the application of network theory for extracting relevant information pipelines and highly communicated sites in GPCRs. We focus on the most recent research and highlight structural insights obtained based on the framework of allosteric communication networks and network theory for GPCR signaling bias.

Performance of virtual screening against GPCR homology models: Impact of template selection and treatment of binding site plasticity.

Rational drug design for G protein-coupled receptors (GPCRs) is limited by the small number of available atomic resolution structures. We assessed the use of homology modeling to predict the structures of two therapeutically relevant GPCRs and strategies to improve the performance of virtual screening against modeled binding sites. Homology models of the D2 dopamine (D2R) and serotonin 5-HT2A receptors (5-HT2AR) were generated based on crystal structures of 16 different GPCRs. Comparison of the homology models to D2R and 5-HT2AR crystal structures showed that accurate predictions could be obtained, but not necessarily using the most closely related template. Assessment of virtual screening performance was based on molecular docking of ligands and decoys. The results demonstrated that several templates and multiple models based on each of these must be evaluated to identify the optimal binding site structure. Models based on aminergic GPCRs showed substantial ligand enrichment and there was a trend toward improved virtual screening performance with increasing binding site accuracy. The best models even yielded ligand enrichment comparable to or better than that of the D2R and 5-HT2AR crystal structures. Methods to consider binding site plasticity were explored to further improve predictions. Molecular docking to ensembles of structures did not outperform the best individual binding site models, but could increase the diversity of hits from virtual screens and be advantageous for GPCR targets with few known ligands. Molecular dynamics refinement resulted in moderate improvements of structural accuracy and the virtual screening performance of snapshots was either comparable to or worse than that of the raw homology models. These results provide guidelines for successful application of structure-based ligand discovery using GPCR homology models.

Dopamine D2 Receptor Agonist Binding Kinetics-Role of a Conserved Serine Residue.

The forward (kon) and reverse (koff) rate constants of drug-target interactions have important implications for therapeutic efficacy. Hence, time-resolved assays capable of measuring these binding rate constants may be informative to drug discovery efforts. Here, we used an ion channel activation assay to estimate the kons and koffs of four dopamine D2 receptor (D2R) agonists; dopamine (DA), p-tyramine, (R)- and (S)-5-OH-dipropylaminotetralin (DPAT). We further probed the role of the conserved serine S1935.42 by mutagenesis, taking advantage of the preferential interaction of (S)-, but not (R)-5-OH-DPAT with this residue. Results suggested similar koffs for the two 5-OH-DPAT enantiomers at wild-type (WT) D2R, both being slower than the koffs of DA and p-tyramine. Conversely, the kon of (S)-5-OH-DPAT was estimated to be higher than that of (R)-5-OH-DPAT, in agreement with the higher potency of the (S)-enantiomer. Furthermore, S1935.42A mutation lowered the kon of (S)-5-OH-DPAT and reduced the potency difference between the two 5-OH-DPAT enantiomers. Kinetic Kds derived from the koff and kon estimates correlated well with EC50 values for all four compounds across four orders of magnitude, strengthening the notion that our assay captured meaningful information about binding kinetics. The approach presented here may thus prove valuable for characterizing D2R agonist candidate drugs.

Entrectinib-A SARS-CoV-2 Inhibitor in Human Lung Tissue (HLT) Cells.

Since the start of the COVID-19 outbreak, pharmaceutical companies and research groups have focused on the development of vaccines and antiviral drugs against SARS-CoV-2. Here, we apply a drug repurposing strategy to identify drug candidates that are able to block the entrance of the virus into human cells. By combining virtual screening with in vitro pseudovirus assays and antiviral assays in Human Lung Tissue (HLT) cells, we identify entrectinib as a potential antiviral drug.

Electron Paramagnetic Resonance Gives Evidence for the Presence of Type 1 Gonadotropin-Releasing Hormone Receptor (GnRH-R) in Subdomains of Lipid Rafts.

This study investigated the effect of type 1 gonadotropin releasing hormone receptor (GnRH-R) localization within lipid rafts on the properties of plasma membrane (PM) nanodomain structure. Confocal microscopy revealed colocalization of PM-localized GnRH-R with GM1-enriched raft-like PM subdomains. Electron paramagnetic resonance spectroscopy (EPR) of a membrane-partitioned spin probe was then used to study PM fluidity of immortalized pituitary gonadotrope cell line αT3-1 and HEK-293 cells stably expressing GnRH-R and compared it with their corresponding controls (αT4 and HEK-293 cells). Computer-assisted interpretation of EPR spectra revealed three modes of spin probe movement reflecting the properties of three types of PM nanodomains. Domains with an intermediate order parameter (domain 2) were the most affected by the presence of the GnRH-Rs, which increased PM ordering (order parameter (S)) and rotational mobility of PM lipids (decreased rotational correlation time (τc)). Depletion of cholesterol by methyl-ß-cyclodextrin (methyl-ß-CD) inhibited agonist-induced GnRH-R internalization and intracellular Ca2+ activity and resulted in an overall reduction in PM order; an observation further supported by molecular dynamics (MD) simulations of model membrane systems. This study provides evidence that GnRH-R PM localization may be related to a subdomain of lipid rafts that has lower PM ordering, suggesting lateral heterogeneity within lipid raft domains.

How Do Molecular Dynamics Data Complement Static Structural Data of GPCRs.

G protein-coupled receptors (GPCRs) are implicated in nearly every physiological process in the human body and therefore represent an important drug targeting class. Advances in X-ray crystallography and cryo-electron microscopy (cryo-EM) have provided multiple static structures of GPCRs in complex with various signaling partners. However, GPCR functionality is largely determined by their flexibility and ability to transition between distinct structural conformations. Due to this dynamic nature, a static snapshot does not fully explain the complexity of GPCR signal transduction. Molecular dynamics (MD) simulations offer the opportunity to simulate the structural motions of biological processes at atomic resolution. Thus, this technique can incorporate the missing information on protein flexibility into experimentally solved structures. Here, we review the contribution of MD simulations to complement static structural data and to improve our understanding of GPCR physiology and pharmacology, as well as the challenges that still need to be overcome to reach the full potential of this technique.

Sharing Data from Molecular Simulations.

Given the need for modern researchers to produce open, reproducible scientific output, the lack of standards and best practices for sharing data and workflows used to produce and analyze molecular dynamics (MD) simulations has become an important issue in the field. There are now multiple well-established packages to perform molecular dynamics simulations, often highly tuned for exploiting specific classes of hardware, each with strong communities surrounding them, but with very limited interoperability/transferability options. Thus, the choice of the software package often dictates the workflow for both simulation production and analysis. The level of detail in documenting the workflows and analysis code varies greatly in published work, hindering reproducibility of the reported results and the ability for other researchers to build on these studies. An increasing number of researchers are motivated to make their data available, but many challenges remain in order to effectively share and reuse simulation data. To discuss these and other issues related to best practices in the field in general, we organized a workshop in November 2018 ( https://bioexcel.eu/events/workshop-on-sharing-data-from-molecular-simulations/ ). Here, we present a brief overview of this workshop and topics discussed. We hope this effort will spark further conversation in the MD community to pave the way toward more open, interoperable, and reproducible outputs coming from research studies using MD simulations.

Synthesis, molecular modelling studies and biological evaluation of new oxoeicosanoid receptor 1 agonists.

The oxoeicosanoid receptor 1 (OXER1) is a member of the G-protein coupled receptors (GPCR) family, and is involved in inflammatory processes and oncogenesis. As such it is an attractive target for pharmacological intervention. The present study aimed to shed light on the molecular fundaments of OXER1 modulation using chemical probes structurally related to the natural agonist 5-oxo-ETE. In a first step, 5-oxo-ETE and its closely related derivatives (5-oxo-EPE and 4-oxo-DHA) were obtained by conducting concise and high-yielding syntheses. The biological activity of obtained compounds was assessed in terms of potency (EC50) and efficacy (Emax) for arrestin recruitment. Finally, molecular modelling and simulation were used to explore binding characteristics of 5-oxo-ETE and derivatives with the aim to rationalize biological activity. Our data suggest that the tested 5-oxo-ETE derivatives (i) insert quickly into the membrane, (ii) access the receptor via transmembrane helices (TMs) 5 and 6 from the membrane side and (iii) drive potency and efficacy by differential interaction with TM5 and 7. Most importantly, we found that the methyl ester of 5-oxo-ETE (1a) showed even a higher maximum response than the natural agonist (1). In contrast, shifting the 5-oxo group into position 4 results in inactive compounds (4-oxo DHA compounds (3) and (3a)). All in all, our study provides relevant structural data that help understanding better OXER1 functionality and its modulation. The structural information presented herein will be useful for designing new lead compounds with desired signalling profiles.

Membrane cholesterol effect on the 5-HT2A receptor: Insights into the lipid-induced modulation of an antipsychotic drug target.

The serotonin 5-hydroxytryptamine 2A (5-HT2A ) receptor is a G-protein-coupled receptor (GPCR) relevant for the treatment of CNS disorders. In this regard, neuronal membrane composition in the brain plays a crucial role in the modulation of the receptor functioning. Since cholesterol is an essential component of neuronal membranes, we have studied its effect on the 5-HT2A receptor dynamics through all-atom MD simulations. We find that the presence of cholesterol in the membrane increases receptor conformational variability in most receptor segments. Importantly, detailed structural analysis indicates that conformational variability goes along with the destabilization of hydrogen bonding networks not only within the receptor but also between receptor and lipids. In addition to increased conformational variability, we also find receptor segments with reduced variability. Our analysis suggests that this increased stabilization is the result of stabilizing effects of tightly bound cholesterol molecules to the receptor surface. Our finding contributes to a better understanding of membrane-induced alterations of receptor dynamics and points to cholesterol-induced stabilizing and destabilizing effects on the conformational variability of GPCRs.

Role of palmitoylation of cysteine 415 in functional coupling CB1 receptor to Gαi2 protein.

In this study, we investigated the role of CB1 palmitoylation in modulating the functional interaction with G proteins both in the absence and presence of agonist binding. Our data show that the nonpalmitoylated CB1 receptor significantly reduced its association with Gαi2 . The agonist stimulation induced a partial dissociation of Gαi2 proteins from the wild-type receptor, while on the C415A mutant the agonist binding was not able to induce a significant dissociation of Gαi2 from the receptor. The lack of palmitoyl chain seems to hamper the ability of the receptor to functionally interact with the Gαi2 and indicate that the palmitoyl chain is responsible for the functional transmission of the agonist-induced conformational change in the receptor of the G protein. These data were further corroborated by molecular dynamics simulations. Overall these results suggest that palmitoylation of the CB1 receptor finely tunes its interaction with G proteins and serves as a targeting signal for its functional regulation. Of note, the possibility to reversibly modulate the palmitoylation of CB1 receptor may offer a coordinated process of regulation and could open new therapeutic approaches.