GPCR Dynamics: Structures in Motion

Entropy drives the ligand recognition in G-protein-coupled receptor subtypes

Achieving ligand subtype selectivity within highly homologous subtypes of G-protein-coupled receptor (GPCR) is critical yet challenging for GPCR drug discovery, primarily due to the unclear mechanism underlying ligand subtype selectivity, which hampers the rational design of subtype-selective ligands. Herein, we disclose an unusual molecular mechanism of entropy-driven ligand recognition in cannabinoid (CB) receptor subtypes, revealed through atomic-level molecular dynamics simulations, cryoelectron microscopy structure, and mutagenesis experiments. This mechanism is attributed to the distinct conformational dynamics of the receptor's orthosteric pocket, leading to variations in ligand binding entropy and consequently, differential binding affinities, which culminate in specific ligand recognition. We experimentally validated this mechanism and leveraged it to design ligands with enhanced or ablated subtype selectivity. One such ligand demonstrated favorable pharmacokinetic properties and significant efficacy in rodent inflammatory analgesic models. More importantly, it is precisely due to the high subtype selectivity obtained based on this mechanism that this ligand does not show addictive properties in animal models. Our findings elucidate the unconventional role of entropy in CB receptor subtype selectivity and suggest a strategy for rational design of ligands to achieve entropy-driven subtype selectivity for many pharmaceutically important GPCRs.

Descriptive molecular pharmacology of the δ opioid receptor (DOR): A computational study with structural approach

This work focuses on the δ receptor (DOR), a G protein-coupled receptor (GPCR) belonging to the opioid receptor group. DOR is expressed in numerous tissues, particularly within the nervous system. Our study explores computationally the receptor's interactions with various ligands, including opiates and opioid peptides. It elucidates how these interactions influence the δ receptor response, relevant in a wide range of health and pathological processes. Thus, our investigation aims to explore the significance of DOR as an incoming drug target for pain relief and neurodegenerative diseases and as a source for novel opioid non-narcotic analgesic alternatives. We analyze the receptor's structural properties and interactions using Molecular Dynamics (MD) simulations and Gaussian-accelerated MD across different functional states. To thoroughly assess the primary differences in the structural and conformational ensembles across our different simulated systems, we initiated our study with 1 μs of conventional Molecular Dynamics. The strategy was chosen to encompass the full activation cycle of GPCRs, as activation processes typically occur within this microsecond range. Following the cMD, we extended our study with an additional 100 ns of Gaussian accelerated Molecular Dynamics (GaMD) to enhance the sampling of conformational states. This simulation approach allowed us to capture a comprehensive range of dynamic interactions and conformational changes that are crucial for GPCR activation as influenced by different ligands. Our study includes comparing agonist and antagonist complexes to uncover the collective patterns of their functional states, regarding activation, blocking, and inactivation of DOR, starting from experimental data. In addition, we also explored interactions between agonist and antagonist molecules from opiate and opioid classifications to establish robust structure-activity relationships. These interactions have been systematically quantified using a Quantitative Structure-Activity Relationships (QSAR) model. This research significantly contributes to our understanding of this significant pharmacological target, which is emerging as an attractive subject for drug development.

Relevance of G protein-coupled receptor (GPCR) dynamics for receptor activation, signalling bias and allosteric modulation

G protein-coupled receptors (GPCRs) are one of the major drug targets. In recent years, computational drug design for GPCRs has mainly focused on static structures obtained through X-ray crystallography, cryogenic electron microscopy (cryo-EM) or in silico modelling as a starting point for virtual screening campaigns. However, GPCRs are highly flexible entities with the ability to adopt different conformational states that elicit different physiological responses. Including this knowledge in the drug discovery pipeline can help to tailor novel conformation-specific drugs with an improved therapeutic profile. In this review, we outline our current knowledge about GPCR dynamics that is relevant for receptor activation, signalling bias and allosteric modulation. Ultimately, we highlight new technological implementations such as time-resolved X-ray crystallography and cryo-EM as well as computational algorithms that can contribute to a more comprehensive understanding of receptor dynamics and its relevance for GPCR functionality.

Probing the energy landscape of the lipid interactions of the serotonin1A receptor

G protein-coupled receptors (GPCRs) are lipid-regulated transmembrane proteins that play a central role in cell signaling and pharmacology. Although the role of membrane lipids in GPCR function is well established, the underlying GPCR-lipid interactions have not been thermodynamically characterized due to the complexity of these interactions. In this work, we estimate the energetics and dynamics of lipid association from coarse-grain simulations of the serotonin1A receptor embedded in a complex membrane. We show that lipids bind to the receptor with varying energetics of 1-4 kT, and timescales of 1-10 μs. The most favorable energetics and longest residence times are observed for cholesterol, glycosphingolipid GM1, phosphatidylethanolamine (PE) and phosphatidylserine (PS) lipids. Multi-exponential fitting of the contact probability suggests distinct dynamic regimes, corresponding to ps, ns and μs timescales, that we correlate with the annular, intermediate and non-annular lipid sites. The timescales of lipid binding correspond to high barrier heights, despite their relatively weaker energetics. Our results highlight that GPCR-lipid interactions are driven by both thermodynamic interactions and the dynamical features of lipid binding.

Structural and Computational Insights into Dynamics and Intermediate States of Orexin 2 Receptor Signaling

Orexin 2 receptor (OX2R) is a G protein-coupled receptor (GPCR) whose activation is crucial to regulation of the sleep-wake cycle. Recently, inactive and active state structures were determined from X-ray crystallography and cryo-electron microscopy single particle analysis, and the activation mechanisms have been discussed based on these static data. GPCRs have multiscale intermediate states during activation, and insights into these dynamics and intermediate states may aid the precise control of intracellular signaling by ligands in drug discovery. Molecular dynamics (MD) simulations are used to investigate dynamics induced in response to thermal perturbations, such as structural fluctuations of main and side chains. In this study, we proposed collective motions of the TM domain during activation by performing 30 independent microsecond-scale MD simulations for various OX2R systems and applying relaxation mode analysis. The analysis results suggested that TM3 had a vertical structural movement relative to the membrane surface during activation. In addition, we extracted three characteristic amino acid residues on TM3, i.e., Q1343.32, V1423.40, and R1523.50, which exhibited large conformational fluctuations. We quantitatively evaluated the changes in their equilibrium during activation in relation to the movement of TM3. We also discuss the regulation of ligand binding recognition and intracellular signal selectivity by changes in the equilibrium of Q1343.32 and R1523.50, respectively, according to MD simulations and GPCR database. Additionally, the OX2R-Gi signaling complex is stabilized in the conformation resembling a non-canonical (NC) state, which was previously proposed as an intermediate state during activation of neurotensin 1 receptor. Insights into the dynamics and intermediate states during activation gained from this study may be useful for developing biased agonists for OX2R.

α-Synuclein and Mitochondria: Probing the Dynamics of Disordered Membrane-protein Regions Using Solid-State Nuclear Magnetic Resonance

The characterization of intrinsically disordered regions (IDRs) in membrane-associated proteins is of crucial importance to elucidate key biochemical processes, including cellular signaling, drug targeting, or the role of post-translational modifications. These protein regions pose significant challenges to powerful analytical techniques of molecular structural investigations. We here applied magic angle spinning solid-state nuclear magnetic resonance to quantitatively probe the structural dynamics of IDRs of membrane-bound α-synuclein (αS), a disordered protein whose aggregation is associated with Parkinson's disease (PD). We focused on the mitochondrial binding of αS, an interaction that has functional and pathological relevance in neuronal cells and that is considered crucial for the underlying mechanisms of PD. Transverse and longitudinal 15N relaxation revealed that the dynamical properties of IDRs of αS bound to the outer mitochondrial membrane (OMM) are different from those of the cytosolic state, thus indicating that regions generally considered not to interact with the membrane are in fact affected by the spatial proximity with the lipid bilayer. Moreover, changes in the composition of OMM that are associated with lipid dyshomeostasis in PD were found to significantly perturb the topology and dynamics of IDRs in the membrane-bound state of αS. Taken together, our data underline the importance of characterizing IDRs in membrane proteins to achieve an accurate understanding of the role that these elusive protein regions play in numerous biochemical processes occurring on cellular surfaces.

A Study on the Robustness and Stability of Explainable Deep Learning in an Imbalanced Setting: The Exploration of the Conformational Space of G Protein-Coupled Receptors

G-protein coupled receptors (GPCRs) are transmembrane proteins that transmit signals from the extracellular environment to the inside of the cells. Their ability to adopt various conformational states, which influence their function, makes them crucial in pharmacoproteomic studies. While many drugs target specific GPCR states to exert their effects-thereby regulating the protein's activity-unraveling the activation pathway remains challenging due to the multitude of intermediate transformations occurring throughout this process, and intrinsically influencing the dynamics of the receptors. In this context, computational modeling, particularly molecular dynamics (MD) simulations, may offer valuable insights into the dynamics and energetics of GPCR transformations, especially when combined with machine learning (ML) methods and techniques for achieving model interpretability for knowledge generation. The current study builds upon previous work in which the layer relevance propagation (LRP) technique was employed to interpret the predictions in a multi-class classification problem concerning the conformational states of the β2-adrenergic (β2AR) receptor from MD simulations. Here, we address the challenges posed by class imbalance and extend previous analyses by evaluating the robustness and stability of deep learning (DL)-based predictions under different imbalance mitigation techniques. By meticulously evaluating explainability and imbalance strategies, we aim to produce reliable and robust insights.

Design of a water-soluble transmembrane receptor kinase with intact molecular function by QTY code

Membrane proteins are critical to biological processes and central to life sciences and modern medicine. However, membrane proteins are notoriously challenging to study, mainly owing to difficulties dictated by their highly hydrophobic nature. Previously, we reported QTY code, which is a simple method for designing water-soluble membrane proteins. Here, we apply QTY code to a transmembrane receptor, histidine kinase CpxA, to render it completely water-soluble. The designed CpxAQTY exhibits expected biophysical properties and highly preserved native molecular function, including the activities of (i) autokinase, (ii) phosphotransferase, (iii) phosphatase, and (iv) signaling receptor, involving a water-solubilized transmembrane domain. We probe the principles underlying the balance of structural stability and activity in the water-solubilized transmembrane domain. Computational approaches suggest that an extensive and dynamic hydrogen-bond network introduced by QTY code and its flexibility may play an important role. Our successful functional preservation further substantiates the robustness and comprehensiveness of QTY code.

Orphan receptor GPR88 as a potential therapeutic target for CNS disorders - an in silico approach

The G-protein-coupled receptors are a part of the largest and most physiologically relevant family of membrane proteins. One-third of the medications, now on the market, target the GPCR receptor family, which is one of the most important therapeutic targets for many disorders. In the reported work, we have focussed on orphan GPR88 receptor which is a part of the GPCR protein family and a potential target for central nervous system disorders. GPR88 is known to show the highest expression in the striatum, which is a key region in motor control and cognitive functions. Recent studies have reported that GPR88 is activated by two agonists, 2-PCCA and RTI-13951-33. In this study, we have predicted the three-dimensional protein structure for the orphan GPR88 using the homology modeling approach. We then used shape-based screening techniques based on known agonists and structure-based virtual screening methods employing docking to uncover novel GPR88 ligands. The screened GPR88-ligand complexes were further subjected to molecular dynamics simulation studies. The selected ligands could fasten the development of novel treatments for the vast list of movement and central nervous system disorders.Communicated by Ramaswamy H. Sarma.

Molecular Determinant Underlying Selective Coupling of Primary G-Protein by Class A GPCRs

G-protein-coupled receptors (GPCRs) transmit downstream signals predominantly via G-protein pathways. However, the conformational basis of selective coupling of primary G-protein remains elusive. Histamine receptors H2R and H3R couple with Gs- or Gi-proteins respectively. Here, three cryo-EM structures of H2R-Gs and H3R-Gi complexes are presented at a global resolution of 2.6-2.7 Å. These structures reveal the unique binding pose for endogenous histamine in H3R, wherein the amino group interacts with E2065.46 of H3R instead of the conserved D1143.32 of other aminergic receptors. Furthermore, comparative analysis of the H2R-Gs and H3R-Gi complexes reveals that the structural geometry of TM5/TM6 determines the primary G-protein selectivity in histamine receptors. Machine learning (ML)-based structuromic profiling and functional analysis of class A GPCR-G-protein complexes illustrate that TM5 length, TM5 tilt, and TM6 outward movement are key determinants of the Gs and Gi/o selectivity among the whole Class A family. Collectively, the findings uncover the common structural geometry within class A GPCRs that determines the primary Gs- and Gi/o-coupling selectivity.

Phospholipid Membranes as Chemically and Functionally Tunable Materials

The sheet-like lipid bilayer is the fundamental structural component of all cell membranes. Its building blocks are phospholipids and cholesterol. Their amphiphilic structure spontaneously leads to the formation of a bilayer in aqueous environment. Lipids are not just structural elements. Individual lipid species, the lipid membrane structure, and lipid dynamics influence and regulate membrane protein function. An exciting field is emerging where the membrane-associated material properties of different bilayer systems are used in designing innovative solutions for widespread applications across various fields, such as the food industry, cosmetics, nano- and biomedicine, drug storage and delivery, biotechnology, nano- and biosensors, and computing. Here, the authors summarize what is known about how lipids determine the properties and functions of biological membranes and how this has been or can be translated into innovative applications. Based on recent progress in the understanding of membrane structure, dynamics, and physical properties, a perspective is provided on how membrane-controlled regulation of protein functions can extend current applications and even offer new applications.

Illuminating the neuropeptide Y4 receptor and its ligand pancreatic polypeptide from a structural, functional, and therapeutic perspective

The neuropeptide Y4 receptor (Y4R), a rhodopsin-like G protein-coupled receptor (GPCR) and the hormone pancreatic polypeptide (PP) are members of the neuropeptide Y family consisting of four receptors (Y1R, Y2R, Y4R, Y5R) and three highly homologous peptide ligands (neuropeptide Y, peptide YY, PP). In this family, the Y4R is of particular interest as it is the only subtype with high affinity to PP over NPY. The Y4R, as a mediator of PP signaling, has a pivotal role in appetite regulation and energy homeostasis, offering potential avenues for the treatment of metabolic disorders such as obesity. PP as anorexigenic peptide is released postprandial from the pancreas in response to food intake, induces satiety signals and contributes to hamper excessive food intake. Moreover, this system was also described to be associated with different types of cancer: overexpression of Y4R have been found in human adenocarcinoma cells, while elevated levels of PP are related to the development of pancreatic endocrine tumors. The pharmacological relevance of the Y4R advanced the search for potent and selective ligands for this receptor subtype, which will be significantly progressed through the elucidation of the active state PP-Y4R cryo-EM structure. This review summarizes the development of novel PP-derived ligands, like Obinepitide as dual Y2R/Y4R agonist in clinical trials or UR-AK86c as small hexapeptide agonist with picomolar affinity, as well as the first allosteric modulators that selectively target the Y4R, e.g. VU0506013 as potent Y4R positive allosteric modulator or (S)-VU0637120 as allosteric antagonist. Here, we provide valuable insights into the complex physiological functions of the Y4R and PP and the pharmacological relevance of the system in appetite regulation to open up new avenues for the development of tool compounds for targeted therapies with potential applications in metabolic disorders.

Ligand efficacy modulates conformational dynamics of the µ-opioid receptor

The µ-opioid receptor (µOR) is an important target for pain management1 and molecular understanding of drug action on µOR will facilitate the development of better therapeutics. Here we show, using double electron-electron resonance and single-molecule fluorescence resonance energy transfer, how ligand-specific conformational changes of µOR translate into a broad range of intrinsic efficacies at the transducer level. We identify several conformations of the cytoplasmic face of the receptor that interconvert on different timescales, including a pre-activated conformation that is capable of G-protein binding, and a fully activated conformation that markedly reduces GDP affinity within the ternary complex. Interaction of β-arrestin-1 with the μOR core binding site appears less specific and occurs with much lower affinity than binding of Gi.

Frizzleds act as dynamic pharmacological entities

The Frizzled family of transmembrane receptors (FZD1-10) belongs to the class F of G protein-coupled receptors (GPCRs). FZDs bind to and are activated by Wingless/Int1 (WNT) proteins. The WNT/FZD signaling system regulates crucial aspects of developmental biology and stem-cell regulation. Dysregulation of WNT/FZD communication can lead to developmental defects and diseases such as cancer and fibrosis. Recent insight into the activation mechanisms of FZDs has underlined that protein dynamics and conserved microswitches are essential for FZD-mediated information flow and build the basis for targeting these receptors pharmacologically. In this review, we summarize recent advances in our understanding of FZD activation, and how novel concepts merge and collide with existing dogmas in the field.

Differential Behavior of Conformational Dynamics in Active and Inactive States of Cannabinoid Receptor 1

The cannabinoid receptor CB1 is a G protein-coupled receptor that regulates critical physiological processes including pain, appetite, and cognition. Understanding the conformational dynamics of CB1 associated with transitions between inactive and active signaling states is imperative for developing targeted modulators. Using microsecond-level all-atom molecular dynamics simulations, we identified marked differences in the conformational ensembles of inactive and active CB1 states in apo conditions. The inactive state exhibited substantially increased structural heterogeneity and plasticity compared to the more rigidified active state in the absence of stabilizing ligands. Transmembrane helices TM3 and TM7 were identified as distinguishing factors modulating the state-dependent dynamics. TM7 displayed amplified fluctuations selectively in the inactive state simulations attributed to disruption of conserved electrostatic contacts anchoring it to surrounding helices in the active state. Additionally, we identified significant reorganization of key salt bridge and hydrogen bond networks known to control CB1 activation between states. For instance, a conserved D213-Y224 hydrogen bond and D184-K192 salt bridge interactions showed marked rearrangements between the states. Collectively, these findings reveal the specialized role of TM7 in directing state-dependent CB1 dynamics through electrostatic switch mechanisms. By elucidating the intrinsic enhanced flexibility of inactive CB1, this study provides valuable insights into the conformational landscape enabling functional transitions. Our perspective advances understanding of CB1 activation mechanisms and offers opportunities for structure-based drug discovery targeting the state-specific conformational dynamics of this receptor.

Constructing artificial gap junctions to mediate intercellular signal and mass transport

Natural gap junctions are a type of channel protein responsible for intercellular signalling and mass communication. However, the scope of applications for these proteins is limited as they cannot be prepared at a large scale and are unable to spontaneously insert into cell membranes in vitro. The construction of artificial gap junctions may provide an alternative strategy for preparing analogues of the natural proteins and bottom-up building blocks necessary for the synthesis of artificial cells. Here we show the construction of artificial gap junction channels from unimolecular tubular molecules consisting of alternately arranged positively and negatively charged pillar[5]arene motifs. These molecules feature a hydrophobic-hydrophilic-hydrophobic triblock structure that allows them to efficiently insert into two adjacent plasma membranes and stretch across the gap between the two membranes to form gap junctions. Similar to natural gap junction channels, the synthetic channels could mediate intercellular signal coupling and reactive oxygen species transmission, leading to cellular activity.

G protein-coupled receptors (GPCRs): advances in structures, mechanisms, and drug discovery

G protein-coupled receptors (GPCRs), the largest family of human membrane proteins and an important class of drug targets, play a role in maintaining numerous physiological processes. Agonist or antagonist, orthosteric effects or allosteric effects, and biased signaling or balanced signaling, characterize the complexity of GPCR dynamic features. In this study, we first review the structural advancements, activation mechanisms, and functional diversity of GPCRs. We then focus on GPCR drug discovery by revealing the detailed drug-target interactions and the underlying mechanisms of orthosteric drugs approved by the US Food and Drug Administration in the past five years. Particularly, an up-to-date analysis is performed on available GPCR structures complexed with synthetic small-molecule allosteric modulators to elucidate key receptor-ligand interactions and allosteric mechanisms. Finally, we highlight how the widespread GPCR-druggable allosteric sites can guide structure- or mechanism-based drug design and propose prospects of designing bitopic ligands for the future therapeutic potential of targeting this receptor family.

Exploring biased activation characteristics by molecular dynamics simulation and machine learning for the μ-opioid receptor

Biased ligands selectively activating specific downstream signaling pathways (termed as biased activation) exhibit significant therapeutic potential. However, the conformational characteristics revealed are very limited for the biased activation, which is not conducive to biased drug development. Motivated by the issue, we combine extensive accelerated molecular dynamics simulations and an interpretable deep learning model to probe the biased activation features for two complex systems constructed by the inactive μOR and two different biased agonists (G-protein-biased agonist TRV130 and β-arrestin-biased agonist endomorphin2). The results indicate that TRV130 binds deeper into the receptor core compared to endomorphin2, located between W2936.48 and D1142.50, and forms hydrogen bonding with D1142.50, while endomorphin2 binds above W2936.48. The G protein-biased agonist induces greater outward movements of the TM6 intracellular end, forming a typical active conformation, while the β-arrestin-biased agonist leads to a smaller extent of outward movements of TM6. Compared with TRV130, endomorphin2 causes more pronounced inward movements of the TM7 intracellular end and more complex conformational changes of H8 and ICL1. In addition, important residues determining the two different biased activation states were further identified by using an interpretable deep learning classification model, including some common biased activation residues across Class A GPCRs like some key residues on the TM2 extracellular end, ECL2, TM5 intracellular end, TM6 intracellular end, and TM7 intracellular end, and some specific important residues of ICL3 for μOR. The observations will provide valuable information for understanding the biased activation mechanism for GPCRs.

Determinants of Neutral Antagonism and Inverse Agonism in the β2-Adrenergic Receptor

Free-energy profiles for the activation/deactivation of the β2-adrenergic receptor (ADRB2) with neutral antagonist and inverse agonist ligands have been determined with well-tempered multiple-walker (MW) metadynamics simulations. The inverse agonists carazolol and ICI118551 clearly favor single inactive conformational minima in both the binary and ternary ligand-receptor-G-protein complexes, in accord with the inverse-agonist activity of the ligands. The behavior of neutral antagonists is more complex, as they seem also to affect the recruitment of the G-protein. The results are analyzed in terms of the conformational states of the well-known microswitches that have been proposed as indicators of receptor activity.

Molecular dynamics-based identification of binding pathways and two distinct high-affinity sites for succinate in succinate receptor 1/GPR91

SUCNR1 is an auto- and paracrine sensor of the metabolic stress signal succinate. Using unsupervised molecular dynamics (MD) simulations (170.400 ns) and mutagenesis across human, mouse, and rat SUCNR1, we characterize how a five-arginine motif around the extracellular pole of TM-VI determines the initial capture of succinate in the extracellular vestibule (ECV) to either stay or move down to the orthosteric site. Metadynamics demonstrate low-energy succinate binding in both sites, with an energy barrier corresponding to an intermediate stage during which succinate, with an associated water cluster, unlocks the hydrogen-bond-stabilized conformationally constrained extracellular loop (ECL)-2b. Importantly, simultaneous binding of two succinate molecules through either a "sequential" or "bypassing" mode is a frequent endpoint. The mono-carboxylate NF-56-EJ40 antagonist enters SUCNR1 between TM-I and -II and does not unlock ECL-2b. It is proposed that occupancy of both high-affinity sites is required for selective activation of SUCNR1 by high local succinate concentrations.

A Bright Future? A Perspective on Class C GPCR Based Genetically Encoded Biosensors

One of the major challenges in molecular neuroscience today is to accurately monitor neurotransmitters, neuromodulators, peptides, and various other biomolecules in the brain with high temporal and spatial resolution. Only a comprehensive understanding of neuromodulator dynamics, their release probability, and spatial distribution will unravel their ultimate role in cognition and behavior. This Perspective offers an overview of potential design strategies for class C GPCR-based biosensors. It briefly highlights current applications of GPCR-based biosensors, with a primary focus on class C GPCRs and their unique structural characteristics compared with other GPCR subfamilies. The discussion offers insights into plausible future design approaches for biosensor development targeting members of this specific GPCR subfamily. It is important to note that, at this stage, we are contemplating possibilities rather than presenting a concrete guide, as the pipeline is still under development.

State-dependent dynamics of extramembrane domains in the β2 -adrenergic receptor

G protein-coupled receptors (GPCRs) are membrane-bound signaling proteins that play an essential role in cellular signaling processes. Due to their intrinsic function of transmitting internal signals in response to external cues, these receptors are adapted to be highly dynamic in nature. The β2 -adrenergic receptor (β2 AR) is a representative member of the family that has been extensively analyzed in terms of its structure and activation. Although the structure of the transmembrane domain has been characterized in the different functional states of the receptor, the conformational dynamics of the extramembrane domains, especially the intrinsically disordered regions are still emerging. In this study, we analyze the state-dependent dynamics of extramembrane domains of β2 AR using atomistic molecular dynamics simulations. We introduce a parameter, the residue excess dynamics that allows us to better quantify receptor dynamics. Using this measure, we show that the dynamics of the extramembrane domains are sensitive to the receptor state. Interestingly, the ligand-bound intermediateR ' state shows the maximal dynamics compared to either the active R*G or inactive R states. Ligand binding appears to be correlated with high residue excess dynamics that are dampened upon G protein coupling. The intracellular loop-3 (ICL3) domain has a tendency to flip towards the membrane upon ligand binding, which could contribute to receptor "priming." We highlight an important ICL1-helix-8 interplay that is broken in the ligand-bound state but is retained in the active state. Overall, our study highlights the importance of characterizing the functional dynamics of the GPCR loop domains.

Stereoselective recognition of morphine enantiomers by -opioid receptor

Stereospecific recognition of chiral molecules plays a crucial role in biological systems. The μ-opioid receptor (MOR) exhibits binding affinity towards (-)-morphine, a well-established gold standard in pain management, while it shows minimal binding affinity for the (+)-morphine enantiomer, resulting in a lack of analgesic activity. Understanding how MOR stereoselectively recognizes morphine enantiomers has remained a puzzle in neuroscience and pharmacology for over half-a-century due to the lack of direct observation techniques. To unravel this mystery, we constructed the binding and unbinding processes of morphine enantiomers with MOR via molecular dynamics simulations to investigate the thermodynamics and kinetics governing MOR's stereoselective recognition of morphine enantiomers. Our findings reveal that the binding of (-)-morphine stabilizes MOR in its activated state, exhibiting a deep energy well and a prolonged residence time. In contrast, (+)-morphine fails to sustain the activation state of MOR. Furthermore, the results suggest that specific residues, namely D1142.50 and D1473.32, are deprotonated in the active state of MOR bound to (-)-morphine. This work highlights that the selectivity in molecular recognition goes beyond binding affinities, extending into the realm of residence time.

Exploring Novel Antidepressants Targeting G Protein-Coupled Receptors and Key Membrane Receptors Based on Molecular Structures

Major Depressive Disorder (MDD) is a complex mental disorder that involves alterations in signal transmission across multiple scales and structural abnormalities. The development of effective antidepressants (ADs) has been hindered by the dominance of monoamine hypothesis, resulting in slow progress. Traditional ADs have undesirable traits like delayed onset of action, limited efficacy, and severe side effects. Recently, two categories of fast-acting antidepressant compounds have surfaced, dissociative anesthetics S-ketamine and its metabolites, as well as psychedelics such as lysergic acid diethylamide (LSD). This has led to structural research and drug development of the receptors that they target. This review provides breakthroughs and achievements in the structure of depression-related receptors and novel ADs based on these. Cryo-electron microscopy (cryo-EM) has enabled researchers to identify the structures of membrane receptors, including the N-methyl-D-aspartate receptor (NMDAR) and the 5-hydroxytryptamine 2A (5-HT2A) receptor. These high-resolution structures can be used for the development of novel ADs using virtual drug screening (VDS). Moreover, the unique antidepressant effects of 5-HT1A receptors in various brain regions, and the pivotal roles of the α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid receptor (AMPAR) and tyrosine kinase receptor 2 (TrkB) in regulating synaptic plasticity, emphasize their potential as therapeutic targets. Using structural information, a series of highly selective ADs were designed based on the different role of receptors in MDD. These molecules have the favorable characteristics of rapid onset and low adverse drug reactions. This review offers researchers guidance and a methodological framework for the structure-based design of ADs.

Fine-tuning activation specificity of G-protein-coupled receptors via automated path searching

Physics-based simulation methods can grant atomistic insights into the molecular origin of the function of biomolecules. However, the potential of such approaches has been hindered by their low efficiency, including in the design of selective agonists where simulations of myriad protein-ligand combinations are necessary. Here, we describe an automated input-free path searching protocol that offers (within 14 d using Graphics Processing Unit servers) a minimum free energy path (MFEP) defined in high-dimension configurational space for activating sphingosine-1-phosphate receptors (S1PRs) by arbitrary ligands. The free energy distributions along the MFEP for four distinct ligands and three S1PRs reached a remarkable agreement with Bioluminescence Resonance Energy Transfer (BRET) measurements of G-protein dissociation. In particular, the revealed transition state structures pointed out toward two S1PR3 residues F263/I284, that dictate the preference of existing agonists CBP307 and BAF312 on S1PR1/5. Swapping these residues between S1PR1 and S1PR3 reversed their response to the two agonists in BRET assays. These results inspired us to design improved agonists with both strong polar head and bulky hydrophobic tail for higher selectivity on S1PR1. Through merely three in silico iterations, our tool predicted a unique compound scaffold. BRET assays confirmed that both chiral forms activate S1PR1 at nanomolar concentration, 1 to 2 orders of magnitude less than those for S1PR3/5. Collectively, these results signify the promise of our approach in fine agonist design for G-protein-coupled receptors.

Extrahelical Binding Site for a 1H-Imidazo[4,5-c]quinolin-4-amine A3 Adenosine Receptor Positive Allosteric Modulator on Helix 8 and Distal Portions of Transmembrane Domains 1 and 7

This study describes the localization and computational prediction of a binding site for the A3 adenosine receptor (A3AR) positive allosteric modulator 2-cyclohexyl-1H-imidazo[4,5-c]quinolin-4-(3,4-dichlorophenyl)amine (LUF6000). The work reveals an extrahelical lipid-facing binding pocket disparate from the orthosteric binding site that encompasses transmembrane domain (TMD) 1, TMD7, and Helix (H) 8, which was predicted by molecular modeling and validated by mutagenesis. According to the model, the nearly planar 1H-imidazo[4,5-c]quinolinamine ring system lies parallel to the transmembrane segments, inserted into an aromatic cage formed by π-π stacking interactions with the side chains of Y2847.55 in TMD7 and Y2938.54 in H8 and by π-NH bonding between Y2847.55 and the exocyclic amine. The 2-cyclohexyl group is positioned "upward" within a small hydrophobic subpocket created by residues in TMDs 1 and 7, while the 3,4-dichlorophenyl group extends toward the lipid interface. An H-bond between the N-1 amine of the heterocycle and the carbonyl of G291.49 further stabilizes the interaction. Molecular dynamics simulations predicted two metastable intermediates, one resembling a pose determined by molecular docking and a second involving transient interactions with Y2938.54; in simulations, each of these intermediates converges into the final bound state. Structure-activity-relationships for replacement of either of the identified exocyclic or endocyclic amines with heteroatoms lacking H-bond donating ability were consistent with the hypothetical pose. Thus, we characterized an allosteric pocket for 1H-imidazo[4,5-c]quinolin-4-amines that is consistent with data generated by orthogonal methods, which will aid in the rational design of improved A3AR positive allosteric modulators. SIGNIFICANCE STATEMENT: Orthosteric A3AR agonists have advanced in clinical trials for inflammatory conditions, liver diseases, and cancer. Thus, the clinical appeal of selective receptor activation could extend to allosteric enhancers, which would induce site- and time-specific activation in the affected tissue. By identifying the allosteric site for known positive allosteric modulators, structure-based drug discovery modalities can be enabled to enhance the pharmacological properties of the 1H-imidazo[4,5-c]quinolin-4-amine class of A3AR positive allosteric modulators.

Molecular mechanism of GPCR spatial organization at the plasma membrane

G-protein-coupled receptors (GPCRs) mediate many critical physiological processes. Their spatial organization in plasma membrane (PM) domains is believed to encode signaling specificity and efficiency. However, the existence of domains and, crucially, the mechanism of formation of such putative domains remain elusive. Here, live-cell imaging (corrected for topography-induced imaging artifacts) conclusively established the existence of PM domains for GPCRs. Paradoxically, energetic coupling to extremely shallow PM curvature (<1 µm-1) emerged as the dominant, necessary and sufficient molecular mechanism of GPCR spatiotemporal organization. Experiments with different GPCRs, H-Ras, Piezo1 and epidermal growth factor receptor, suggest that the mechanism is general, yet protein specific, and can be regulated by ligands. These findings delineate a new spatiomechanical molecular mechanism that can transduce to domain-based signaling any mechanical or chemical stimulus that affects the morphology of the PM and suggest innovative therapeutic strategies targeting cellular shape.

AI-driven GPCR analysis, engineering, and targeting

This article investigates the role of recent advances in Artificial Intelligence (AI) to revolutionise the study of G protein-coupled receptors (GPCRs). AI has been applied to many areas of GPCR research, including the application of machine learning (ML) in GPCR classification, prediction of GPCR activation levels, modelling GPCR 3D structures and interactions, understanding G-protein selectivity, aiding elucidation of GPCRs structures, and drug design. Despite progress, challenges in predicting GPCR structures and addressing the complex nature of GPCRs remain, providing avenues for future research and development.

G protein-biased LPAR1 agonism of prototypic antidepressants: Implication in the identification of novel therapeutic target for depression

Prototypic antidepressants, such as tricyclic/tetracyclic antidepressants (TCAs), have multiple pharmacological properties and have been considered to be more effective than newer antidepressants, such as selective serotonin reuptake inhibitors, in treating severe depression. However, the clinical contribution of non-monoaminergic effects of TCAs remains elusive. In this study, we discovered that amitriptyline, a typical TCA, directly binds to the lysophosphatidic acid receptor 1 (LPAR1), a G protein-coupled receptor, and activates downstream G protein signaling, while exerting a little effect on β-arrestin recruitment. This suggests that amitriptyline acts as a G protein-biased agonist of LPAR1. This biased agonism was specific to TCAs and was not observed with other antidepressants. LPAR1 was found to be involved in the behavioral effects of amitriptyline. Notably, long-term infusion of mouse hippocampus with the potent G protein-biased LPAR agonist OMPT, but not the non-biased agonist LPA, induced antidepressant-like behavior, indicating that G protein-biased agonism might be necessary for the antidepressant-like effects. Furthermore, RNA-seq analysis revealed that LPA and OMPT have opposite patterns of gene expression changes in the hippocampus. Pathway analysis indicated that long-term treatment with OMPT activated LPAR1 downstream signaling (Rho and MAPK), whereas LPA suppressed LPAR1 signaling. Our findings provide insights into the mechanisms underlying the non-monoaminergic antidepressant effects of TCAs and identify the G protein-biased agonism of LPAR1 as a promising target for the development of novel antidepressants.

Computational drug development for membrane protein targets

The application of computational biology in drug development for membrane protein targets has experienced a boost from recent developments in deep learning-driven structure prediction, increased speed and resolution of structure elucidation, machine learning structure-based design and the evaluation of big data. Recent protein structure predictions based on machine learning tools have delivered surprisingly reliable results for water-soluble and membrane proteins but have limitations for development of drugs that target membrane proteins. Structural transitions of membrane proteins have a central role during transmembrane signaling and are often influenced by therapeutic compounds. Resolving the structural and functional basis of dynamic transmembrane signaling networks, especially within the native membrane or cellular environment, remains a central challenge for drug development. Tackling this challenge will require an interplay between experimental and computational tools, such as super-resolution optical microscopy for quantification of the molecular interactions of cellular signaling networks and their modulation by potential drugs, cryo-electron microscopy for determination of the structural transitions of proteins in native cell membranes and entire cells, and computational tools for data analysis and prediction of the structure and function of cellular signaling networks, as well as generation of promising drug candidates.

Biased Signaling in Mutated Variants of β2-Adrenergic Receptor: Insights from Molecular Dynamics Simulations

The molecular basis of receptor bias in G protein-coupled receptors (GPCRs) caused by mutations that preferentially activate specific intracellular transducers over others remains poorly understood. Two experimentally identified biased variants of β2-adrenergic receptors (β2AR), a prototypical GPCR, are a triple mutant (T68F, Y132A, and Y219A) and a single mutant (Y219A); the former bias the receptor toward the β-arrestin pathway by disfavoring G protein engagement, while the latter induces G protein signaling explicitly due to selection against GPCR kinases (GRKs) that phosphorylate the receptor as a prerequisite of β-arrestin binding. Though rigorous characterizations have revealed functional implications of these mutations, the atomistic origin of the observed transducer selectivity is not clear. In this study, we investigated the allosteric mechanism of receptor bias in β2AR using microseconds of all-atom Gaussian accelerated molecular dynamics (GaMD) simulations. Our observations reveal distinct rearrangements in transmembrane helices, intracellular loop 3, and critical residues R1313.50 and Y3267.53 in the conserved motifs D(E)RY and NPxxY for the mutant receptors, leading to their specific transducer interactions. Moreover, partial dissociation of G protein from the receptor core is observed in the simulations of the triple mutant in contrast to the single mutant and wild-type receptor. The reorganization of allosteric communications from the extracellular agonist BI-167107 to the intracellular receptor-transducer interfaces drives the conformational rearrangements responsible for receptor bias in the single and triple mutants. The molecular insights into receptor bias of β2AR presented here could improve the understanding of biased signaling in GPCRs, potentially opening new avenues for designing novel therapeutics with fewer side-effects and superior efficacy.

GPCR-IPL score: multilevel featurization of GPCR-ligand interaction patterns and prediction of ligand functions from selectivity to biased activation

G-protein-coupled receptors (GPCRs) mediate diverse cell signaling cascades after recognizing extracellular ligands. Despite the successful history of known GPCR drugs, a lack of mechanistic insight into GPCR challenges both the deorphanization of some GPCRs and optimization of the structure-activity relationship of their ligands. Notably, replacing a small substituent on a GPCR ligand can significantly alter extracellular GPCR-ligand interaction patterns and motion of transmembrane helices in turn to occur post-binding events of the ligand. In this study, we designed 3D multilevel features to describe the extracellular interaction patterns. Subsequently, these 3D features were utilized to predict the post-binding events that result from conformational dynamics from the extracellular to intracellular areas. To understand the adaptability of GPCR ligands, we collected the conformational information of flexible residues during binding and performed molecular featurization on a broad range of GPCR-ligand complexes. As a result, we developed GPCR-ligand interaction patterns, binding pockets, and ligand features as score (GPCR-IPL score) for predicting the functional selectivity of GPCR ligands (agonism versus antagonism), using the multilevel features of (1) zoomed-out 'residue level' (for flexible transmembrane helices of GPCRs), (2) zoomed-in 'pocket level' (for sophisticated mode of action) and (3) 'atom level' (for the conformational adaptability of GPCR ligands). GPCR-IPL score demonstrated reliable performance, achieving area under the receiver operating characteristic of 0.938 and area under the precision-recall curve of 0.907 (available in gpcr-ipl-score.onrender.com). Furthermore, we used the molecular features to predict the biased activation of downstream signaling (Gi/o, Gq/11, Gs and β-arrestin) as well as the functional selectivity. The resulting models are interpreted and applied to out-of-set validation with three scenarios including the identification of a new MRGPRX antagonist.

Conformational variants of the ternary complex of C5a, C5aR1, and G-protein

The complement component fragment 5a (C5a) binds and activates two complement receptors like C5aR1 and C5aR2, which play a significant role in orchestrating the proinflammatory function of C5a in tissues through the recruitment of heterotrimeric G-proteins and β-arrestins. Dysregulation of the complement induces excessive production of C5a, which triggers aberrant activation of the C5a-C5aR1-G-protein and C5a-C5aR2-β-arrestin signalling axes in tissues, contributing to the pathology of numerous immune-inflammatory diseases. Thus, understanding the interaction of C5a with C5aR1 and C5aR2, as well as the interaction of G-protein and β-arrestins, respectively, with C5a-C5aR1 and C5a-C5aR2, holds tremendous therapeutic value. In the absence of structural data, we have previously elaborated the binary complexes of C5a-C5aR1 and C5a-C5aR2, as well as the ternary complex of C5a-C5aR2-β-arrestin1, in highly refined model structures. While our ternary model complex of C5a-C5aR1-G-protein was in progress, two cryo-electron microscopy-based ternary structural complexes of C5aR1 were made available by others. However, it is observed that the interaction of the crucial NT-peptide of C5aR1 with C5a, including the portion of the G⍺i-subunit that harbors the switch-I region, is not fully resolved in both complexes. The current study addresses the issues and provides two highly refined alternative model ternary complexes of C5a-C5aR1-G-protein. The study highlights the conformational heterogeneity in C5aR1 by comparing the two conformational variants of the model ternary complex in the context of C5a-C5aR2-β-arrestin1 for further devising methods and molecules targeting both surface and intracellular C5aR1/C5aR2 for effectively mitigating the proinflammatory role of C5a in various disease settings.Communicated by Ramaswamy H. Sarma.

Development of 2-Aminotetralin-Type Serotonin 5-HT1 Agonists: Molecular Determinants for Selective Binding and Signaling at 5-HT1A, 5-HT1B, 5-HT1D, and 5-HT1F Receptors

The serotonin (5-hydroxytryptamine, 5-HT) 5-HT1 G-protein coupled receptor subtypes (5-HT1A/1B/1D/1E/1F) share a high sequence homology, confounding development of subtype-specific ligands. This study used a 5-HT1 structure-based ligand design approach to develop subtype-selective ligands using a 5-substituted-2-aminotetralin (5-SAT) chemotype, leveraging results from pharmacological, molecular modeling, and mutagenesis studies to delineate molecular determinants for 5-SAT binding and function at 5-HT1 subtypes. 5-SATs demonstrated high affinity (Ki ≤ 25 nM) and at least 50-fold stereoselective preference ([2S] > [2R]) at 5-HT1A, 5-HT1B, and 5-HT1D receptors but essentially nil affinity (Ki > 1 μM) at 5-HT1F receptors. The 5-SATs tested were agonists with varying degrees of potency and efficacy, depending on chemotype substitution and 5-HT1 receptor subtype. Models were built from the 5-HT1A (cryo-EM), 5-HT1B (crystal), and 5-HT1D (cryo-EM) structures, and 5-SATs underwent docking studies with up to 1 μs molecular dynamics simulations. 5-SAT interactions observed at positions 3.33, 5.38, 5.42, 5.43, and 7.39 of 5-HT1 subtypes were confirmed with point mutation experiments. Additional 5-SATs were designed and synthesized to exploit experimental and computational results, yielding a new full efficacy 5-HT1A agonist with 100-fold selectivity over 5-HT1B/1D receptors. The results presented lay the foundation for the development of additional 5-HT1 subtype selective ligands for drug discovery purposes.

Development of a new class of potent and highly selective G protein-coupled receptor kinase 5 inhibitors and structural insight from crystal structures of inhibitor complexes

G protein-coupled receptor kinase 5 (GRK5) is an important drug development target for heart failure, cardiac hypertrophy, and cancer. We have designed and developed a new class of highly selective, potent, and non-covalent GRK5 inhibitors. One of the inhibitors displayed GRK5 IC50 value of 10 nM and exhibited >100,000-fold selectivity over GRK2. The X-ray structure of a ketoamide-derived inhibitor-bound GRK5 showed the formation of a hemithioketal intermediate with active site Cys474 in the GRK5 active site and provided new insights into the ligand-binding site interactions responsible for high selectivity. The current studies serve as an important guide to therapeutic GRK5 inhibitor drug development.

Characterizing conformational states in GPCR structures using machine learning

G protein-coupled receptors (GPCRs) play a pivotal role in signal transduction and represent attractive targets for drug development. Recent advances in structural biology have provided insights into GPCR conformational states, which are critical for understanding their signaling pathways and facilitating structure-based drug discovery. In this study, we introduce a machine learning approach for conformational state annotation of GPCRs. We represent GPCR conformations as high-dimensional feature vectors, incorporating information about amino acid residue pairs involved in the activation pathway. Using a dataset of GPCR conformations in inactive and active states obtained through molecular dynamics simulations, we trained machine learning models to distinguish between inactive-like and active-like conformations. The developed model provides interpretable predictions and can be used for the large-scale analysis of molecular dynamics trajectories of GPCRs.

Trajectory Statistical Learning of the Potential Mean of Force and Diffusion Coefficient from Molecular Dynamics Simulations

Central to studying the conformational changes of a complex protein is understanding the dynamics and energetics involved. Phenomenologically, structural dynamics can be formulated using an overdamped Langevin model along an observable, e.g., the distance between two residues in the protein. The Langevin model is specified by the deterministic force (the potential of mean force, PMF) and stochastic force (characterized by the diffusion coefficient, D). It is therefore of great interest to be able to extract both PMF and D from an observable time series but under the same computational framework. Here, we approach this challenge in molecular dynamics (MD) simulations by treating it as a missing-data Bayesian estimation problem. An important distinction in our methodology is that the entire MD trajectory, as opposed to the individual data elements, is used as the statistical variable in Bayesian imputation. This idea is implemented through an eigen-decomposition procedure for a time-symmetrized Fokker-Planck equation, followed by maximizing the likelihood for parameter estimation. The mathematical expressions for the functional derivatives used in learning PMF and D also provide new physical insights for the manner by which the information on both the deterministic and stochastic forces is encoded in the dynamics data. An all-atom MD simulation of a nontrivial biomolecule case is used to illustrate the application of this approach. We show that, interestingly, the results of trajectory statistical learning can motivate new order parameters for an improved description of the kinetic bottlenecks in conformational changes. Complementing purely data-driven or black-box methods, this work underscores the advantages of physics-based machine learning in gaining chemical insights from quantitative parameter estimation.

The Pivotal Distinction between Antagonists' and Agonists' Binding into Dopamine D4 Receptor-MD and FMO/PIEDA Studies

The dopamine D4 receptor (D4R) is a promising therapeutic target in widespread diseases, and the search for novel agonists and antagonists appears to be clinically relevant. The mechanism of binding to the receptor (R) for antagonists and agonists varies. In the present study, we conducted an in-depth computational study, teasing out key similarities and differences in binding modes, complex dynamics, and binding energies for D4R agonists and antagonists. The dynamic network method was applied to investigate the communication paths between the ligand (L) and G-protein binding site (GBS) of human D4R. Finally, the fragment molecular orbitals with pair interaction energy decomposition analysis (FMO/PIEDA) scheme was used to estimate the binding energies of L-R complexes. We found that a strong salt bridge with D3.32 initiates the inhibition of the dopamine D4 receptor. This interaction also occurs in the binding of agonists, but the change in the receptor conformation to the active state starts with interaction with cysteine C3.36. Such a mechanism may arise in the case of agonists unable to form a hydrogen bond with the serine S5.46, considered, so far, to be crucial in the activation of GPCRs. The energy calculations using the FMO/PIEDA method indicate that antagonists show higher residue occupancy of the receptor binding site than agonists, suggesting they could form relatively more stable complexes. Additionally, antagonists were characterized by repulsive interactions with S5.46 distinguishing them from agonists.

A peroxidase-derived ligand that induces Fusarium graminearum Ste2 receptor-dependent chemotropism

Introduction

The fungal G protein-coupled receptors Ste2 and Ste3 are vital in mediating directional hyphal growth of the agricultural pathogen Fusarium graminearum towards wheat plants. This chemotropism is induced by a catalytic product of peroxidases secreted by the wheat. Currently, the identity of this product, and the substrate it is generated from, are not known.

Methods and results

We provide evidence that a peroxidase substrate is derived from F. graminearum conidia and report a simple method to extract and purify the FgSte2-activating ligand for analyses by mass spectrometry. The mass spectra arising from t he ligand extract are characteristic of a 400 Da carbohydrate moiety. Consistent with this type of molecule, glycosidase treatment of F. graminearum conidia prior to peroxidase treatment significantly reduced the amount of ligand extracted. Interestingly, availability of the peroxidase substrate appears to depend on the presence of both FgSte2 and FgSte3, as knockout of one or the other reduces the chemotropism-inducing effect of the extracts.

Conclusions

While further characterization is necessary, identification of the F. graminearum-derived peroxidase substrate and the FgSte2-activating ligand will unearth deeper insights into the intricate mechanisms that underlie fungal pathogenesis in cereal crops, unveiling novel avenues for inhibitory interventions.

Osmotic stress studies of G-protein-coupled receptor rhodopsin activation

We summarize and critically review osmotic stress studies of the G-protein-coupled receptor rhodopsin. Although small amounts of structural water are present in these receptors, the effect of bulk water on their function remains uncertain. Studies of the influences of osmotic stress on the GPCR archetype rhodopsin have given insights into the functional role of water in receptor activation. Experimental work has discovered that osmolytes shift the metarhodopsin equilibrium after photoactivation, either to the active or inactive conformations according to their molar mass. At least 80 water molecules are found to enter rhodopsin in the transition to the photoreceptor active state. We infer that this movement of water is both necessary and sufficient for receptor activation. If the water influx is prevented, e.g., by large polymer osmolytes or by dehydration, then the receptor functional transition is back shifted. These findings imply a new paradigm in which rhodopsin becomes solvent swollen in the activation mechanism. Water thus acts as an allosteric modulator of function for rhodopsin-like receptors in lipid membranes.

A single-domain intrabody targeting the follicle-stimulating hormone receptor impacts FSH-induced G protein-dependent signalling

Intracellular variable fragments of heavy-chain antibody from camelids (intra-VHH) have been successfully used as chaperones to solve the 3D structure of active G protein-coupled receptors bound to their transducers. However, their effect on signalling has been poorly explored, although they may provide a better understanding of the relationships between receptor conformation and activity. Here, we isolated and characterized iPRC1, the first intra-VHH recognizing a member of the large glycoprotein hormone receptor family, the follicle-stimulating hormone receptor (FSHR). This intra-VHH recognizes the FSHR third intracellular loop and decreases cAMP production in response to FSH, without altering Gαs recruitment. Hence, iPRC1 behaves as an allosteric modulator and provides a new tool to complete structure/activity studies performed thus far on this receptor.

Key aspects of modern GPCR drug discovery

G-protein-coupled receptors (GPCRs) are the largest and most versatile cell surface receptor family with a broad repertoire of ligands and functions. We've learned an enormous amount about discovering drugs of this receptor class since the first GPCR was cloned and expressed in 1986, such that it's now well-recognized that GPCRs are the most successful target class for approved drugs. Here we take the reader through a GPCR drug discovery journey from target to the clinic, highlighting the key learnings, best practices, challenges, trends and insights on discovering drugs that ultimately modulate GPCR function therapeutically in patients. The future of GPCR drug discovery is inspiring, with more desirable drug mechanisms and new technologies enabling the delivery of better and more successful drugs.

Tracking conformational transitions of the gonadotropin hormone receptors in a bilayer of (SDPC) poly-unsaturated lipids from all-atom molecular dynamics simulations

Glycoprotein hormone receptors [thyrotropin (TSHR), luteinizing hormone/chorionic gonadotropin (LHCGR), and follicle stimulating hormone (FSHR) receptors] are rhodopsin-like G protein-coupled receptors. These receptors display common structural features including a prominent extracellular domain with leucine-rich repeats (LRR) stabilized by β-sheets and a long and flexible loop known as the hinge region (HR), and a transmembrane (TM) domain with seven α-helices interconnected by intra- and extracellular loops. Binding of the ligand to the LRR resembles a hand coupling transversally to the α- and β-subunits of the hormone, with the thumb being the HR. The structure of the FSH-FSHR complex suggests an activation mechanism in which Y335 at the HR binds into a pocket between the α- and β-chains of the hormone, leading to an adjustment of the extracellular loops. In this study, we performed molecular dynamics (MD) simulations to identify the conformational changes of the FSHR and LHCGR. We set up a FSHR structure as predicted by AlphaFold (AF-P23945); for the LHCGR structure we took the cryo-electron microscopy structure for the active state (PDB:7FII) as initial coordinates. Specifically, the flexibility of the HR domain and the correlated motions of the LRR and TM domain were analyzed. From the conformational changes of the LRR, TM domain, and HR we explored the conformational landscape by means of MD trajectories in all-atom approximation, including a membrane of polyunsaturated phospholipids. The distances and procedures here defined may be useful to propose reaction coordinates to describe diverse processes, such as the active-to-inactive transition, and to identify intermediaries suited for allosteric regulation and biased binding to cellular transducers in a selective activation strategy.

Protein-Protein Docking Approach to GPCR Oligomerization

G-protein-coupled receptors (GPCRs), the largest family of human membrane proteins, play a crucial role in cellular control and are the target of approximately one-third of all drugs on the market. Targeting these complexes with selectivity or formulating small molecules capable of modulating receptor-receptor interactions could potentially offer novel avenues for drug discovery, fostering the development of more refined and safer pharmacotherapies. Due to the lack of experimentally derived X-ray crystallography spectra of GPCR oligomers, there is growing evidence supporting the development of new in silico approaches for predicting GPCR self-assembling structures. The significance of GPCR oligomerization, the challenges in modeling these structures, and the potential of protein-protein docking algorithms to address these challenges are discussed. The study also underscores the use of various software solutions for modeling GPCR oligomeric structures and presents practical cases where these techniques have been successfully applied.

Assessing Protein-Protein Docking Protocols: Case Studies of G-Protein-Coupled Receptor Interactions

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.

Palmitoylation of the Glucagon-like Peptide-1 Receptor Modulates Cholesterol Interactions at the Receptor-Lipid Microenvironment

The G protein-coupled receptor (GPCR) superfamily of cell surface receptors has been shown to be functionally modulated by post-translational modifications. The glucagon-like peptide receptor-1 (GLP-1R), which is a drug target in diabetes and obesity, undergoes agonist-dependent palmitoyl tail conjugation. The palmitoylation in the C-terminal domain of GLP-1R has been suggested to modulate the receptor-lipid microenvironment. In this work, we have performed coarse-grain molecular dynamics simulations of palmitoylated and nonpalmitoylated GLP-1R to analyze the differential receptor-lipid interactions. Interestingly, the placement and dynamics of the C-terminal domain of GLP-1R are found to be directly dependent on the palmitoyl tail. We observe that both cholesterol and phospholipids interact with the receptor but display differential interactions in the presence and absence of the palmitoyl tail. We characterize important cholesterol-binding sites and validate sites that have been previously reported in experimentally resolved structures of the receptor. We show that the receptor acts like a conduit for cholesterol flip-flop by stabilizing cholesterol in the membrane core. Taken together, our work represents an important step in understanding the molecular effects of lipid modifications in GPCRs.

Molecular determinants of ligand efficacy and potency in GPCR signaling

Heterotrimeric guanine nucleotide-binding protein (G protein)-coupled receptors (GPCRs) bind to extracellular ligands and drugs and modulate intracellular responses through conformational changes. Despite their importance as drug targets, the molecular origins of pharmacological properties such as efficacy (maximum signaling response) and potency (the ligand concentration at half-maximal response) remain poorly understood for any ligand-receptor-signaling system. We used the prototypical adrenaline-β2 adrenergic receptor-G protein system to reveal how specific receptor residues decode and translate the information encoded in a ligand to mediate a signaling response. We present a data science framework to integrate pharmacological and structural data to uncover structural changes and allosteric networks relevant for ligand pharmacology. These methods can be tailored to study any ligand-receptor-signaling system, and the principles open possibilities for designing orthosteric and allosteric compounds with defined signaling properties.

Understanding the molecular mechanisms of odorant binding and activation of the human OR52 family

Structural and mechanistic studies on human odorant receptors (ORs), key in olfactory signaling, are challenging because of their low surface expression in heterologous cells. The recent structure of OR51E2 bound to propionate provided molecular insight into odorant recognition, but the lack of an inactive OR structure limited understanding of the activation mechanism of ORs upon odorant binding. Here, we determined the cryo-electron microscopy structures of consensus OR52 (OR52cs), a representative of the OR52 family, in the ligand-free (apo) and octanoate-bound states. The apo structure of OR52cs reveals a large opening between transmembrane helices (TMs) 5 and 6. A comparison between the apo and active structures of OR52cs demonstrates the inward and outward movements of the extracellular and intracellular segments of TM6, respectively. These results, combined with molecular dynamics simulations and signaling assays, shed light on the molecular mechanisms of odorant binding and activation of the OR52 family.

Functional Flow Cytometry to Predict PD-L1 Conformational Changes

The programmed cell death protein 1/programmed cell death protein ligand 1 (PD-1/PD-L1) axis is one of the most widely recognized targets for cancer immunotherapy. Importantly, PD-L1 conformational changes can hinder target binding when living cells are used. Antibody affinity, equilibrium binding, association and dissociation rates, and other affinity-related constants are fundamental to ensure target saturation. Here, PD-L1 changes in conformation and their potential impact on PD-L1 function and mutation are explored. Specifically, we present detailed flow cytometry procedures to analyze PD-L1 reactivity in myeloid-derived suppressor cells (MDSCs). This approach can also be used to study the contribution of protein conformational changes in living cells. © 2023 Wiley Periodicals LLC. Basic Protocol 1: Sample preparation for PD-L1+ myeloid-derived suppressor cells detection by flow cytometry Basic Protocol 2: Protocol preparation, sample acquisition, and gating strategy for flow cytometric screening of PD-L1+ myeloid-derived suppressor cells in patients with lung cancer Support Protocol 1: Bioinformatic tools for the analysis of flow cytometric data.

Pharmacologically targeting intracellular allosteric sites of GPCRs for drug discovery

G-protein-coupled receptors (GPCRs) are a family of cell surface proteins that can sense a variety of extracellular stimuli and mediate multiple signaling transduction pathways involved in human physiology. Recent advances in GPCR structural biology have revealed a relatively conserved intracellular allosteric site in multiple GPCRs, which can be utilized to modulate receptors from the inside. This novel intracellular site partially overlaps with the G-protein and β-arrestin coupling sites, providing a novel avenue for biological intervention. Here, we review evidence available for GPCR structures complexed with intracellular small-molecule allosteric modulators, elucidating drug-target interactions and allosteric mechanisms. Moreover, we highlight the potential of intracellular allosteric modulators in achieving biased signaling, which provides insights into biased allosteric mechanisms.