Recent progress in the study of G protein-coupled receptors with molecular dynamics computer simulations

Deep Learning Dynamic Allostery of G-Protein-Coupled Receptors

G-protein-coupled receptors (GPCRs) make up the largest superfamily of human membrane proteins and represent primary targets of ∼1/3 of currently marketed drugs. Allosteric modulators have emerged as more selective drug candidates compared with orthosteric agonists and antagonists. However, many X-ray and cryo-EM structures of GPCRs resolved so far exhibit negligible differences upon the binding of positive and negative allosteric modulators (PAMs and NAMs). The mechanism of dynamic allosteric modulation in GPCRs remains unclear. In this work, we have systematically mapped dynamic changes in free energy landscapes of GPCRs upon binding of allosteric modulators using the Gaussian accelerated molecular dynamics (GaMD), deep learning (DL), and free energy prOfiling Workflow (GLOW). GaMD simulations were performed for a total of 66 μs on 44 GPCR systems in the presence and absence of the modulator. DL and free energy calculations revealed significantly reduced dynamic fluctuations and conformational space of GPCRs upon modulator binding. While the modulator-free GPCRs often sampled multiple low-energy conformational states, the NAMs and PAMs confined the inactive and active agonist-G-protein-bound GPCRs, respectively, to mostly only one specific conformation for signaling. Such cooperative effects were significantly reduced for binding of the selective modulators to "non-cognate" receptor subtypes. Therefore, GPCR allostery exhibits a dynamic "conformational selection" mechanism. In the absence of available modulator-bound structures as for most current GPCRs, it is critical to use a structural ensemble of representative GPCR conformations rather than a single structure for compound docking ("ensemble docking"), which will potentially improve structure-based design of novel allosteric drugs of GPCRs.

Understanding the Rhodopsin Worldview Through Atomic Force Microscopy (AFM): Structure, Stability, and Activity Studies

Rhodopsin is a G protein-coupled receptor (GPCR) present in the rod outer segment (ROS) of photoreceptor cells that initiates the phototransduction cascade required for scotopic vision. Due to the remarkable advancements in technological tools, the chemistry of rhodopsin has begun to unravel especially over the past few decades, but mostly at the ensemble scale. Atomic force microscopy (AFM) is a tool capable of providing critical information from a single-molecule point of view. In this regard, to bolster our understanding of rhodopsin at the nanoscale level, AFM-based imaging, force spectroscopy, and nano-indentation techniques were employed on ROS disc membranes containing rhodopsin, isolated from vertebrate species both in normal and diseased states. These AFM studies on samples from native retinal tissue have provided fundamental insights into the structure and function of rhodopsin under normal and dysfunctional states. We review here the findings from these AFM studies that provide important insights on the supramolecular organization of rhodopsin within the membrane and factors that contribute to this organization, the molecular interactions stabilizing the structure of the receptor and factors that can modify those interactions, and the mechanism underlying constitutive activity in the receptor that can cause disease.

Deep Learning Dynamic Allostery of G-Protein-Coupled Receptors

G-protein-coupled receptors (GPCRs) are the largest superfamily of human membrane proteins and represent primary targets of ~ 1/3 of currently marketed drugs. Allosteric modulators have emerged as more selective drug candidates compared with orthosteric agonists and antagonists. However, many X-ray and cryo-EM structures of GPCRs resolved so far exhibit negligible differences upon binding of positive and negative allosteric modulators (PAMs and NAMs). Mechanism of dynamic allosteric modulation in GPCRs remains unclear. In this work, we have systematically mapped dynamic changes in free energy landscapes of GPCRs upon binding of allosteric modulators using the Gaussian accelerated molecular dynamics (GaMD), Deep Learning (DL) and free energy prOfiling Workflow (GLOW). A total of 18 available high-resolution experimental structures of allosteric modulator-bound class A and B GPCRs were collected for simulations. A number of 8 computational models were generated to examine selectivity of the modulators by changing their target receptors to different subtypes. All-atom GaMD simulations were performed for a total of 66 μs on 44 GPCR systems in the presence/absence of the modulator. DL and free energy calculations revealed significantly reduced conformational space of GPCRs upon modulator binding. While the modulator-free GPCRs often sampled multiple low-energy conformational states, the NAMs and PAMs confined the inactive and active agonist-G protein-bound GPCRs, respectively, to mostly only one specific conformation for signaling. Such cooperative effects were significantly reduced for binding of the selective modulators to "non-cognate" receptor subtypes in the computational models. Therefore, comprehensive DL of extensive GaMD simulations has revealed a general dynamic mechanism of GPCR allostery, which will greatly facilitate rational design of selective allosteric drugs of GPCRs.

Deep Learning Dynamic Allostery of G-Protein-Coupled Receptors

G-protein-coupled receptors (GPCRs) are the largest superfamily of human membrane proteins and represent primary targets of ~1/3 of currently marketed drugs. Allosteric modulators have emerged as more selective drug candidates compared with orthosteric agonists and antagonists. However, many X-ray and cryo-EM structures of GPCRs resolved so far exhibit negligible differences upon binding of positive and negative allosteric modulators (PAMs and NAMs). Mechanism of dynamic allosteric modulation in GPCRs remains unclear. In this work, we have systematically mapped dynamic changes in free energy landscapes of GPCRs upon binding of allosteric modulators using the Gaussian accelerated molecular dynamics (GaMD), Deep Learning (DL) and free energy prOfiling Workflow (GLOW). A total of 18 available high-resolution experimental structures of allosteric modulator-bound class A and B GPCRs were collected for simulations. A number of 8 computational models were generated to examine selectivity of the modulators by changing their target receptors to different subtypes. All-atom GaMD simulations were performed for a total of 66 μs on 44 GPCR systems in the presence/absence of the modulator. DL and free energy calculations revealed significantly reduced conformational space of GPCRs upon modulator binding. While the modulator-free GPCRs often sampled multiple low-energy conformational states, the NAMs and PAMs confined the inactive and active agonist-G protein-bound GPCRs, respectively, to mostly only one specific conformation for signaling. Such cooperative effects were significantly reduced for binding of the selective modulators to "non-cognate" receptor subtypes in the computational models. Therefore, comprehensive DL of extensive GaMD simulations has revealed a general dynamic mechanism of GPCR allostery, which will greatly facilitate rational design of selective allosteric drugs of GPCRs.

Modeling the Dynamics of Protein-Protein Interfaces, How and Why?

Protein-protein assemblies act as a key component in numerous cellular processes. Their accurate modeling at the atomic level remains a challenge for structural biology. To address this challenge, several docking and a handful of deep learning methodologies focus on modeling protein-protein interfaces. Although the outcome of these methods has been assessed using static reference structures, more and more data point to the fact that the interaction stability and specificity is encoded in the dynamics of these interfaces. Therefore, this dynamics information must be taken into account when modeling and assessing protein interactions at the atomistic scale. Expanding on this, our review initially focuses on the recent computational strategies aiming at investigating protein-protein interfaces in a dynamic fashion using enhanced sampling, multi-scale modeling, and experimental data integration. Then, we discuss how interface dynamics report on the function of protein assemblies in globular complexes, in fuzzy complexes containing intrinsically disordered proteins, as well as in active complexes, where chemical reactions take place across the protein-protein interface.

Development of enhanced conformational sampling methods to probe the activation landscape of GPCRs

G protein-coupled receptors (GPCRs) make up the largest superfamily of integral membrane proteins and play critical signal transduction roles in many physiological processes. Developments in molecular biology, biophysical, biochemical, pharmacological, and computational techniques aimed at these important therapeutic targets are beginning to provide unprecedented details on the structural as well as functional basis of their pleiotropic signaling mediated by G proteins, β arrestins, and other transducers. This pleiotropy presents a pharmacological challenge as the same ligand-receptor interaction can cause a therapeutic effect as well as an undesirable on-target side-effect through different downstream pathways. GPCRs don't function as simple binary on-off switches but as finely tuned shape-shifting machines described by conformational ensembles, where unique subsets of conformations may be responsible for specific signaling cascades. X-ray crystallography and more recently cryo-electron microscopy are providing snapshots of some of these functionally-important receptor conformations bound to ligands and/or transducers, which are being utilized by computational methods to describe the dynamic conformational energy landscape of GPCRs. In this chapter, we review the progress in computational conformational sampling methods based on molecular dynamics and discrete sampling approaches that have been successful in complementing biophysical and biochemical studies on these receptors in terms of their activation mechanisms, allosteric effects, actions of biased ligands, and effects of pathological mutations. Some of the sampled simulation time scales are beginning to approach receptor activation time scales. The list of conformational sampling methods and example uses discussed is not exhaustive but includes representative examples that have pushed the limits of classical molecular dynamics and discrete sampling methods to describe the activation energy landscape of GPCRs.

Computational Investigations on the Binding Mode of Ligands for the Cannabinoid-Activated G Protein-Coupled Receptor GPR18

GPR18 is an orphan G protein-coupled receptor (GPCR) expressed in cells of the immune system. It is activated by the cannabinoid receptor (CB) agonist ∆⁹-tetrahydrocannabinol (THC). Several further lipids have been proposed to act as GPR18 agonists, but these results still require unambiguous confirmation. In the present study, we constructed a homology model of the human GPR18 based on an ensemble of three GPCR crystal structures to investigate the binding modes of the agonist THC and the recently reported antagonists which feature an imidazothiazinone core to which a (substituted) phenyl ring is connected via a lipophilic linker. Docking and molecular dynamics simulation studies were performed. As a result, a hydrophobic binding pocket is predicted to accommodate the imidazothiazinone core, while the terminal phenyl ring projects towards an aromatic pocket. Hydrophobic interaction of Cys251 with substituents on the phenyl ring could explain the high potency of the most potent derivatives. Molecular dynamics simulation studies suggest that the binding of imidazothiazinone antagonists stabilizes transmembrane regions TM1, TM6 and TM7 of the receptor through a salt bridge between Asp118 and Lys133. The agonist THC is presumed to bind differently to GPR18 than to the distantly related CB receptors. This study provides insights into the binding mode of GPR18 agonists and antagonists which will facilitate future drug design for this promising potential drug target.

Quantum Mechanical and Molecular Mechanics Modeling of Membrane-Embedded Rhodopsins

Computational chemistry provides versatile methods for studying the properties and functioning of biological systems at different levels of precision and at different time scales. The aim of this article is to review the computational methodologies that are applicable to rhodopsins as archetypes for photoactive membrane proteins that are of great importance both in nature and in modern technologies. For each class of computational techniques, from methods that use quantum mechanics for simulating rhodopsin photophysics to less-accurate coarse-grained methodologies used for long-scale protein dynamics, we consider possible applications and the main directions for improvement.

Molecular Dynamics Simulations of Adenosine Receptors: Advances, Applications and Trends

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Adenosine receptors (ARs) are transmembrane proteins that belong to the G protein-coupled receptors (GPCRs) superfamily and mediate the biological functions of adenosine. To date, four AR subtypes are known, namely A1, A2A, A2B and A3 that exhibit different signaling pathways, tissue localization, and mechanisms of activation. Moreover, the widespread ARs and their implication in numerous physiological and pathophysiological conditions had made them pivotal therapeutic targets for developing clinically effective agents.

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The crystallographic success in identifying the 3D crystal structures of A2A and A1 ARs has dramatically enriched our understanding of their structural and functional properties such as ligand binding and signal transduction. This, in turn, has provided a structural basis for a larger contribution of computational methods, particularly molecular dynamics (MD) simulations, toward further investigation of their molecular properties and designing bioactive ligands with therapeutic potential. MD simulation has been proved to be an invaluable tool in investigating ARs and providing answers to some critical questions. For example, MD has been applied in studying ARs in terms of ligand-receptor interactions, molecular recognition, allosteric modulations, dimerization, and mechanisms of activation, collectively aiding in the design of subtype selective ligands.

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In this review, we focused on the advances and different applications of MD simulations utilized to study the structural and functional aspects of ARs that can foster the structure-based design of drug candidates. In addition, relevant literature was briefly discussed which establishes a starting point for future advances in the field of drug discovery to this pivotal group of drug targets.

Interaction of POPC, DPPC, and POPE with the μ opioid receptor: A coarse-grained molecular dynamics study

The μ opioid receptor (μOR), which is part of the G protein-coupled receptors family, is a membrane protein that is modulated by its lipid environment. In the present work, we model μOR in three different membrane systems: POPC (1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine), POPE (1-palmitoyl-2-oleoyl-sn-glycero-3-phosphoethanolamine), and DPPC (1, 2-dipalmitoyl-sn-glycero-3-phosphocholine) through 45 μs molecular dynamics (MD) simulations at the coarse-grained level. Our theoretical studies provide new insights to the lipid-induced modulation of the receptor. Particularly, to characterize how μOR interacts with each lipid, we analyze the tilt of the protein, the number of contacts occurring between the lipids and each amino acid of the receptor, and the μOR-lipid interface described as a network graph. We also analyze the variations in the number and the nature of the protein contacts that are induced by the lipid structure. We show that POPC interacts preferentially with helix 1 (H1) and helices H5-H6, POPE, with H5-H6 and H6-H7, and DPPC, with H4 and H6. We demonstrate how each of the three lipids shape the structure of the μOR.

Recent advances in computational studies of GPCR-G protein interactions

Protein-protein interactions are key in cellular signaling. G protein-coupled receptors (GPCRs), the largest superfamily of human membrane proteins, are able to transduce extracellular signals (e.g., hormones and neurotransmitters) to intracellular proteins, in particular the G proteins. Since GPCRs serve as primary targets of ~1/3 of currently marketed drugs, it is important to understand mechanisms of GPCR signaling in order to design selective and potent drug molecules. This chapter focuses on recent advances in computational studies of the GPCR-G protein interactions using bioinformatics, protein-protein docking and molecular dynamics simulation approaches.

A Streamlined, General Approach for Computing Ligand Binding Free Energies and Its Application to GPCR-Bound Cholesterol

The theory of receptor-ligand binding equilibria has long been well-established in biochemistry, and was primarily constructed to describe dilute aqueous solutions. Accordingly, few computational approaches have been developed for making quantitative predictions of binding probabilities in environments other than dilute isotropic solution. Existing techniques, ranging from simple automated docking procedures to sophisticated thermodynamics-based methods, have been developed with soluble proteins in mind. Biologically and pharmacologically relevant protein-ligand interactions often occur in complex environments, including lamellar phases like membranes and crowded, nondilute solutions. Here, we revisit the theoretical bases of ligand binding equilibria, avoiding overly specific assumptions that are nearly always made when describing receptor-ligand binding. Building on this formalism, we extend the asymptotically exact Alchemical Free Energy Perturbation technique to quantifying occupancies of sites on proteins in a complex bulk, including phase-separated, anisotropic, or nondilute solutions, using a thermodynamically consistent and easily generalized approach that resolves several ambiguities of current frameworks. To incorporate the complex bulk without overcomplicating the overall thermodynamic cycle, we simplify the common approach for ligand restraints by using a single distance-from-bound-configuration (DBC) ligand restraint during AFEP decoupling from protein. DBC restraints should be generalizable to binding modes of most small molecules, even those with strong orientational dependence. We apply this approach to compute the likelihood that membrane cholesterol binds to known crystallographic sites on three GPCRs (β₂-adrenergic, 5HT-2B, and μ-opioid) at a range of concentrations. Nonideality of cholesterol in a binary cholesterol:phosphatidylcholine (POPC) bilayer is characterized and consistently incorporated into the interpretation. We find that the three sites exhibit very different affinities for cholesterol: The site on the adrenergic receptor is predicted to be high affinity, with 50% occupancy for 1:10⁹ CHOL:POPC mixtures. The sites on the 5HT-2B and μ-opioid receptor are predicted to be lower affinity, with 50% occupancy for 1:10³ CHOL:POPC and 1:10² CHOL:POPC, respectively. These results could not have been predicted from the crystal structures alone.

Binding, Thermodynamics, and Selectivity of a Non-peptide Antagonist to the Melanocortin-4 Receptor

The melanocortin-4 receptor (MC4R) is a potential drug target for treatment of obesity, anxiety, depression, and sexual dysfunction. Crystal structures for MC4R are not yet available, which has hindered successful structure-based drug design. Using microsecond-scale molecular-dynamics simulations, we have investigated selective binding of the non-peptide antagonist MCL0129 to a homology model of human MC4R (hMC4R). This approach revealed that, at the end of a multi-step binding process, MCL0129 spontaneously adopts a binding mode in which it blocks the agonistic-binding site. This binding mode was confirmed in subsequent metadynamics simulations, which gave an affinity for human hMC4R that matches the experimentally determined value. Extending our simulations of MCL0129 binding to hMC1R and hMC3R, we find that receptor subtype selectivity for hMC4R depends on few amino acids located in various structural elements of the receptor. These insights may support rational drug design targeting the melanocortin systems.

Mechanism of the G-protein mimetic nanobody binding to a muscarinic G-protein-coupled receptor

Protein-protein binding is key in cellular signaling processes. Molecular dynamics (MD) simulations of protein-protein binding, however, are challenging due to limited timescales. In particular, binding of the medically important G-protein-coupled receptors (GPCRs) with intracellular signaling proteins has not been simulated with MD to date. Here, we report a successful simulation of the binding of a G-protein mimetic nanobody to the M₂ muscarinic GPCR using the robust Gaussian accelerated MD (GaMD) method. Through long-timescale GaMD simulations over 4,500 ns, the nanobody was observed to bind the receptor intracellular G-protein-coupling site, with a minimum rmsd of 2.48 Å in the nanobody core domain compared with the X-ray structure. Binding of the nanobody allosterically closed the orthosteric ligand-binding pocket, being consistent with the recent experimental finding. In the absence of nanobody binding, the receptor orthosteric pocket sampled open and fully open conformations. The GaMD simulations revealed two low-energy intermediate states during nanobody binding to the M₂ receptor. The flexible receptor intracellular loops contribute remarkable electrostatic, polar, and hydrophobic residue interactions in recognition and binding of the nanobody. These simulations provided important insights into the mechanism of GPCR-nanobody binding and demonstrated the applicability of GaMD in modeling dynamic protein-protein interactions.

Capturing the multiscale dynamics of membrane protein complexes with all-atom, mixed-resolution, and coarse-grained models

The structures and dynamics of protein complexes are often challenging to model in heterogeneous environments such as biological membranes. Herein, we meet this fundamental challenge at attainable cost with all-atom, mixed-resolution, and coarse-grained models of vital membrane proteins. We systematically simulated five complex models formed by two distinct G protein-coupled receptors (GPCRs) in the lipid-bilayer membrane on the ns-to-μs timescales. These models, which suggest the swinging motion of an intracellular loop, for the first time, provide the molecular details for the regulatory role of such a loop. For the models at different resolutions, we observed consistent structural stability but various levels of speed-ups in protein dynamics. The mixed-resolution and coarse-grained models show two and four times faster protein diffusion than the all-atom models, in addition to a 4- and 400-fold speed-up in the simulation performance. Furthermore, by elucidating the strengths and challenges of combining all-atom models with reduced resolution models, this study can serve as a guide to simulating other complex systems in heterogeneous environments efficiently.

Quantum mechanics implementation in drug-design workflows: does it really help?

The pharmaceutical industry is progressively operating in an era where development costs are constantly under pressure, higher percentages of drugs are demanded, and the drug-discovery process is a trial-and-error run. The profit that flows in with the discovery of new drugs has always been the motivation for the industry to keep up the pace and keep abreast with the endless demand for medicines. The process of finding a molecule that binds to the target protein using in silico tools has made computational chemistry a valuable tool in drug discovery in both academic research and pharmaceutical industry. However, the complexity of many protein-ligand interactions challenges the accuracy and efficiency of the commonly used empirical methods. The usefulness of quantum mechanics (QM) in drug-protein interaction cannot be overemphasized; however, this approach has little significance in some empirical methods. In this review, we discuss recent developments in, and application of, QM to medically relevant biomolecules. We critically discuss the different types of QM-based methods and their proposed application to incorporating them into drug-design and -discovery workflows while trying to answer a critical question: are QM-based methods of real help in drug-design and -discovery research and industry?

Using molecular dynamics for the refinement of atomistic models of GPCRs by homology modeling

Despite GPCRs sharing a common seven helix bundle, analysis of the diverse crystallographic structures available reveal specific features that might be relevant for ligand design. Despite the number of crystallographic structures of GPCRs steadily increasing, there are still challenges that hamper the availability of new structures. In the absence of a crystallographic structure, homology modeling remains one of the important techniques for constructing 3D models of proteins. In the present study we investigated the use of molecular dynamics simulations for the refinement of GPCRs models constructed by homology modeling. Specifically, we investigated the relevance of template selection, ligand inclusion as well as the length of the simulation on the quality of the GPCRs models constructed. For this purpose we chose the crystallographic structure of the rat muscarinic M3 receptor as reference and constructed diverse atomistic models by homology modeling, using different templates. Specifically, templates used in the present work include the human muscarinic M2; the more distant human histamine H1 and the even more distant bovine rhodopsin as shown in the GPCRs phylogenetic tree. We also investigated the use or not of a ligand in the refinement process. Hence, we conducted the refinement process of the M3 model using the M2 muscarinic as template with tiotropium or NMS docked in the orthosteric site and compared with the results obtained with a model refined without any ligand bound.

Understanding the molecular basis of agonist/antagonist mechanism of human mu opioid receptor through gaussian accelerated molecular dynamics method

The most powerful analgesic and addictive properties of opiate alkaloids are mediated by the μ opioid receptor (MOR). The MOR has been extensively investigated as a drug target in the twentieth century, with numerous compounds of varying efficacy being identified. We employed molecular dynamics and Gaussian accelerated molecular dynamics techniques to identify the binding mechanisms of MORs to BU72 (agonist) and β-funaltrexamine (antagonist). Our approach theoretically suggests that the 34 residues (Lys209–Phe221 and Ile301–Cys321) of the MORs were the key regions enabling the two compounds to bind to the active site of the MORs. When the MORs were in the holo form, the key region was in the open conformation. When the MORs were in the apo form, the key region was in the closed conformation. The key region might be responsible for the selectivity of new MOR agonists and antagonists.

Methods for the Development of In Silico GPCR Models

The Reggio group has constructed computer models of the inactive and G-protein-activated states of the cannabinoid CB1 and CB2 receptors, as well as, several orphan receptors that recognize a subset of cannabinoid compounds, including GPR55 and GPR18. These models have been used to design ligands, mutations, and covalent labeling studies. The resultant second-generation models have been used to design ligands with improved affinity, efficacy, and subtype selectivity. Herein, we provide a guide for the development of GPCR models using the most recent orphan receptor studied in our lab, GPR3. GPR3 is an orphan receptor that belongs to the Class A family of G-protein-coupled receptors. It shares high sequence similarity with GPR6, GPR12, the lysophospholipid receptors, and the cannabinoid receptors. GPR3 is predominantly expressed in mammalian brain and oocytes and it is known as a Gαs-coupled receptor activated constitutively in cells. GPR3 represents a possible target for the treatment of different pathological conditions such as Alzheimer's disease, oocyte maturation, or neuropathic pain. However, the lack of potent and selective GPR3 ligands is delaying the exploitation of this promising therapeutic target. In this context, we aim to develop a homology model that helps us to elucidate the structural determinants governing ligand-receptor interactions at GPR3. In this chapter, we detail the methods and rationale behind the construction of the GPR3 active-and inactive-state models. These homology models will enable the rational design of novel ligands, which may serve as research tools for further understanding of the biological role of GPR3.

Molecular Dynamics Methodologies for Probing Cannabinoid Ligand/Receptor Interaction

The cannabinoid type 1 and 2 G-protein-coupled receptors are currently important pharmacological targets with significant drug discovery potential. These receptors have been shown to display functional selectivity or biased agonism, a property currently thought to have substantial therapeutic potential. Although recent advances in crystallization techniques have provided a wealth of structural information about this important class of membrane-embedded proteins, these structures lack dynamical information. In order to fully understand the interplay of structure and function for this important class of proteins, complementary techniques that address the dynamical aspects of their function are required such as NMR as well as a variety of other spectroscopies. Complimentary to these experimental approaches is molecular dynamics, which has been effectively used to help unravel, at the atomic level, the dynamics of ligand binding and activation of these membrane-bound receptors. Here, we discuss and present several representative examples of the application of molecular dynamics simulations to the understanding of the signatures of ligand-binding and -biased signaling at the cannabinoid type 1 and 2 receptors.

Interplay of G Protein-Coupled Receptors with the Membrane: Insights from Supra-Atomic Coarse Grain Molecular Dynamics Simulations

G protein-coupled receptors (GPCRs) are central to many fundamental cellular signaling pathways. They transduce signals from the outside to the inside of cells in physiological processes ranging from vision to immune response. It is extremely challenging to look at them individually using conventional experimental techniques. Recently, a pseudo atomistic molecular model has emerged as a valuable tool to access information on GPCRs, more specifically on their interactions with their environment in their native cell membrane and the consequences on their supramolecular organization. This approach uses the Martini coarse grain (CG) model to describe the receptors, lipids, and solvent in molecular dynamics (MD) simulations and in enough detail to allow conserving the chemical specificity of the different molecules. The elimination of unnecessary degrees of freedom has opened up large-scale simulations of the lipid-mediated supramolecular organization of GPCRs. Here, after introducing the Martini CGMD method, we review these studies carried out on various members of the GPCR family, including rhodopsin (visual receptor), opioid receptors, adrenergic receptors, adenosine receptors, dopamine receptor, and sphingosine 1-phosphate receptor. These studies have brought to light an interesting set of novel biophysical principles. The insights range from revealing localized and heterogeneous deformations of the membrane bilayer at the surface of the protein, specific interactions of lipid molecules with individual GPCRs, to the effect of the membrane matrix on global GPCR self-assembly. The review ends with an overview of the lessons learned from the use of the CGMD method, the biophysical-chemical findings on lipid-protein interplay.

Accelerated structure-based design of chemically diverse allosteric modulators of a muscarinic G protein-coupled receptor

Design of ligands that provide receptor selectivity has emerged as a new paradigm for drug discovery of G protein-coupled receptors, and may, for certain families of receptors, only be achieved via identification of chemically diverse allosteric modulators. Here, the extracellular vestibule of the M2 muscarinic acetylcholine receptor (mAChR) is targeted for structure-based design of allosteric modulators. Accelerated molecular dynamics (aMD) simulations were performed to construct structural ensembles that account for the receptor flexibility. Compounds obtained from the National Cancer Institute (NCI) were docked to the receptor ensembles. Retrospective docking of known ligands showed that combining aMD simulations with Glide induced fit docking (IFD) provided much-improved enrichment factors, compared with the Glide virtual screening workflow. Glide IFD was thus applied in receptor ensemble docking, and 38 top-ranked NCI compounds were selected for experimental testing. In [(3)H]N-methylscopolamine radioligand dissociation assays, approximately half of the 38 lead compounds altered the radioligand dissociation rate, a hallmark of allosteric behavior. In further competition binding experiments, we identified 12 compounds with affinity of ≤30 μM. With final functional experiments on six selected compounds, we confirmed four of them as new negative allosteric modulators (NAMs) and one as positive allosteric modulator of agonist-mediated response at the M2 mAChR. Two of the NAMs showed subtype selectivity without significant effect at the M1 and M3 mAChRs. This study demonstrates an unprecedented successful structure-based approach to identify chemically diverse and selective GPCR allosteric modulators with outstanding potential for further structure-activity relationship studies.

Biological membranes

Biological membranes allow life as we know it to exist. They form cells and enable separation between the inside and outside of an organism, controlling by means of their selective permeability which substances enter and leave. By allowing gradients of ions to be created across them, membranes also enable living organisms to generate energy. In addition, they control the flow of messages between cells by sending, receiving and processing information in the form of chemical and electrical signals. This essay summarizes the structure and function of membranes and the proteins within them, and describes their role in trafficking and transport, and their involvement in health and disease. Techniques for studying membranes are also discussed.

Differential dynamics of the serotonin1A receptor in membrane bilayers of varying cholesterol content revealed by all atom molecular dynamics simulation

The serotonin1A receptor belongs to the superfamily of G protein-coupled receptors (GPCRs) and is a potential drug target in neuropsychiatric disorders. The receptor has been shown to require membrane cholesterol for its organization, dynamics and function. Although recent work suggests a close interaction of cholesterol with the receptor, the structural integrity of the serotonin1A receptor in the presence of cholesterol has not been explored. In this work, we have carried out all atom molecular dynamics simulations, totaling to 3 μs, to analyze the effect of cholesterol on the structure and dynamics of the serotonin1A receptor. Our results show that the presence of physiologically relevant concentration of membrane cholesterol alters conformational dynamics of the serotonin1A receptor and, on an average lowers conformational fluctuations. Our results show that, in general, transmembrane helix VII is most affected by the absence of membrane cholesterol. These results are in overall agreement with experimental data showing enhancement of GPCR stability in the presence of membrane cholesterol. Our results constitute a molecular level understanding of GPCR-cholesterol interaction, and represent an important step in our overall understanding of GPCR function in health and disease.

How To Minimize Artifacts in Atomistic Simulations of Membrane Proteins, Whose Crystal Structure Is Heavily Engineered: β₂-Adrenergic Receptor in the Spotlight

Atomistic molecular dynamics (MD) simulations are used extensively to elucidate membrane protein properties. These simulations are based on three-dimensional protein structures that in turn are often based on crystallography. The protein structures resolved in crystallographic studies typically do not correspond to pristine proteins, however. Instead the crystallized proteins are commonly engineered, including structural modifications (mutations, replacement of protein sequences by antibodies, bound ligands, etc.) whose impact on protein structure and dynamics is largely unknown. Here we explore this issue through atomistic MD simulations (∼5 μs in total), focusing on the β2-adrenergic receptor (β2AR) that is one of the most studied members of the G-protein coupled receptor superfamily. Starting from an inactive-state crystal structure of β2AR, we remove the many modifications in β2AR systematically one at a time, in six consecutive steps. After each step, we equilibrate the system and simulate it quite extensively. The results of this step-by-step approach highlight that the structural modifications used in crystallization can affect ligand and G-protein binding sites, packing at the transmembrane-helix interface region, and the dynamics of connecting loops in β2AR. When the results of the systematic step-by-step approach are compared to an all-at-once technique where all modifications done on β2AR are removed instantaneously at the same time, it turns out that the step-by-step method provides results that are superior in terms of maintaining protein structural stability. The results provide compelling evidence that for membrane proteins whose 3D structure is based on structural engineering, the preparation of protein structure for atomistic MD simulations is a delicate and sensitive process. The results show that most valid results are found when the structural modifications are reverted slowly, one at a time.

G-protein coupled receptors: advances in simulation and drug discovery

G-protein coupled receptors (GPCRs), the largest family of human membrane proteins, mediate cellular signaling and represent primary targets of about one third of currently marketed drugs. GPCRs undergo highly dynamic structural transitions during signal transduction, from binding of extracellular ligands to coupling with intracellular effector proteins. Molecular dynamics (MD) simulations have been utilized to investigate GPCR signaling mechanisms (such as pathways of ligand binding and receptor activation/deactivation) and to design novel small-molecule drug candidates. Future research directions point towards modeling cooperative binding of multiple orthosteric and allosteric ligands to GPCRs, GPCR oligomerization and interactions of GPCRs with different intracellular signaling proteins. Through methodological and supercomputing advances, MD simulations will continue to provide important insights into GPCR signaling mechanisms and further facilitate structure-based drug design.

Adaptive switching of interaction potentials in the time domain: an extended Lagrangian approach tailored to transmute force field to QM/MM simulations and back

An extended Lagrangian formalism that allows for a smooth transition between two different descriptions of interactions during a molecular dynamics simulation is presented. This time-adaptive method is particularly useful in the context of multiscale simulation as it provides a sound recipe to switch on demand between different hierarchical levels of theory, for instance between ab initio ("QM") and force field ("MM") descriptions of a given (sub)system in the course of a molecular dynamics simulation. The equations of motion can be integrated straightforwardly using the usual propagators, such as the Verlet algorithm. First test cases include a bath of harmonic oscillators, of which a subset is switched to a different force constant and/or equilibrium position, as well as an all-MM to QM/MM transition in a hydrogen-bonded water dimer. The method is then applied to a smectic 8AB8 liquid crystal and is shown to be able to switch dynamically a preselected 8AB8 molecule from an all-MM to a QM/MM description which involves partition boundaries through covalent bonds. These examples show that the extended Lagrangian approach is not only easy to implement into existing code but that it is also efficient and robust. The technique moreover provides easy access to a conserved energy quantity, also in cases when Nosé-Hoover chain thermostatting is used throughout dynamical switching. A simple quadratic driving potential proves to be sufficient to guarantee a smooth transition whose time scale can be easily tuned by varying the fictitious mass parameter associated with the auxiliary variable used to extend the Lagrangian. The method is general and can be applied to time-adaptive switching on demand between two different levels of theory within the framework of hybrid scale-bridging simulations.

Models for the binding channel of wild type and mutant transthyretin with glabridin

Our results indicate that additional high-occupancy hydrogen bonds were observed at the binding interface between the two dimers in V30A TTR, while stabilisation hydrophobic interactions between residues in the mutant AB loop decreased.

Molecular dynamics simulations of wild type and mutants of botulinum neurotoxin A complexed with synaptic vesicle protein 2C

Using molecular dynamics simulations, we investigate the relationship between the conformational changes of BoNT/A-RBD:SV2C-LD and the interfacial interactions.

On the modularity of the intrinsic flexibility of the µ opioid receptor: a computational study

The µ opioid receptor (µOR), the principal target to control pain, belongs to the G protein-coupled receptors (GPCRs) family, one of the most highlighted protein families due to their importance as therapeutic targets. The conformational flexibility of GPCRs is one of their essential characteristics as they take part in ligand recognition and subsequent activation or inactivation mechanisms. It is assessed that the intrinsic mechanical properties of the µOR, more specifically its particular flexibility behavior, would facilitate the accomplishment of specific biological functions, at least in their first steps, even in the absence of a ligand or any chemical species usually present in its biological environment. The study of the mechanical properties of the µOR would thus bring some indications regarding the highly efficient ability of the µOR to transduce cellular message. We therefore investigate the intrinsic flexibility of the µOR in its apo-form using all-atom Molecular Dynamics simulations at the sub-microsecond time scale. We particularly consider the µOR embedded in a simplified membrane model without specific ions, particular lipids, such as cholesterol moieties, or any other chemical species that could affect the flexibility of the µOR. Our analyses highlighted an important local effect due to the various bendability of the helices resulting in a diversity of shape and volume sizes adopted by the µOR binding site. Such property explains why the µOR can interact with ligands presenting highly diverse structural geometry. By investigating the topology of the µOR binding site, a conformational global effect is depicted: the correlation between the motional modes of the extra- and intracellular parts of µOR on one hand, along with a clear rigidity of the central µOR domain on the other hand. Our results show how the modularity of the µOR flexibility is related to its pre-ability to activate and to present a basal activity.

Structure-based simulations reveal concerted dynamics of GPCR activation

G protein-coupled receptors (GPCRs) are a vital class of proteins that transduce biological signals across the cell membrane. However, their allosteric activation mechanism is not fully understood; crystal structures of active and inactive receptors have been reported, but the functional pathway between these two states remains elusive. Here, we use structure-based (Gō-like) models to simulate activation of two GPCRs, rhodopsin and the β₂ adrenergic receptor (β₂AR). We used data-derived reaction coordinates that capture the activation mechanism for both proteins, showing that activation proceeds through quantitatively different paths in the two systems. Both reaction coordinates are determined from the dominant concerted motions in the simulations so the technique is broadly applicable. There were two surprising results. First, the main structural changes in the simulations were distributed throughout the transmembrane bundle, and not localized to the obvious areas of interest, such as the intracellular portion of Helix 6. Second, the activation (and deactivation) paths were distinctly nonmonotonic, populating states that were not simply interpolations between the inactive and active structures. These transitions also suggest a functional explanation for β₂AR's basal activity: it can proceed through a more broadly defined path during the observed transitions.

Molecular dynamics study on conformational differences between dGMP and 8-oxo-dGMP: Effects of metal ions

The modified nucleotide base 7,8-dihydro-8-oxo-guanine (8-oxo-G) is one of the major sources of spontaneous mutagenesis. Nucleotide-sanitizing enzymes, such as the MutT homolog-1 (MTH1) and nudix-type motif 5 (NUDT5), selectively remove 8-oxo-G from the cellular pool of nucleotides. Previous studies showed that, although the syn conformation generally predominates in purine nucleotides with a bulky substituent at the 8-position, 8-oxo-dGMP binds to both MTH1 and NUDT5 in the anti conformation. This study was initiated to investigate the possibility that 8-oxo-dGMP itself may adopt the anti conformation. Molecular dynamics simulations of mononucleotides (dGMP, 8-oxo-dGMP) in aqueous solution were performed. 8-oxo-dGMP adopted the anti conformation as well as the syn conformation, and the proportion of adopting the anti conformation increased in the presence of metal ions. When 8-oxo-dGMP was in the anti conformation, a metal ion was located between the oxygen atom of phosphate and the oxygen atom at the 8-position of 8-oxo-G. The types of stable anti conformations of 8-oxo-dGMP differed, depending on the ionic radii and charges of coexisting ions. These data suggested a role for metal ions, other than as cofactors for the hydrolysis of the di- and tri-phosphate forms of mononucleotides; that the metal ions help retain the anti conformation of the N-glycosidic torsion angle of 8-oxo-dGMP to promote the binding between the 8-oxo-G deoxynucleotide and the nucleotide-sanitizing enzymes.

Interactions between fengycin and model bilayers quantified by coarse-grained molecular dynamics

Bacteria, particularly of the genus Bacillus, produce a wide variety of antifungal compounds. They act by affecting the lipid bilayers of fungal membranes, causing curvature-induced strain and eventual permeabilization. One class of these, known as fengycins, has been commercialized for treating agricultural infections and shows some promise as a possible antifungal pharmaceutical. Understanding the mechanism by which fengycins damage lipid bilayers could prove useful to the future development of related antifungal treatments. In this work, we present multi-microsecond-long simulations of fengycin interacting with different lipid bilayer systems. We see fengycin aggregation and uncover a clear aggregation pattern that is partially influenced by bilayer composition. We also quantify some local bilayer perturbations caused by fengycin binding, including curvature of the lipid bilayer and local electrostatic-driven reorganization.

Free energy landscape of G-protein coupled receptors, explored by accelerated molecular dynamics

G-protein coupled receptors (GPCRs) mediate cellular responses to various hormones and neurotransmitters and are important targets for treating a wide spectrum of diseases. They are known to adopt multiple conformational states (e.g., inactive, intermediate and active) during their modulation of various cell signaling pathways. Here, the free energy landscape of GPCRs is explored using accelerated molecular dynamics (aMD) simulations as demonstrated on the M2 muscarinic receptor, a key GPCR that regulates human heart rate and contractile forces of cardiomyocytes. Free energy profiles of important structural motifs that undergo conformational transitions upon GPCR activation and allosteric signaling are analyzed in detail, including the Arg(3.50)-Glu(6.30) ionic lock, the Trp(6.48) toggle switch and the hydrogen interactions between Tyr(5.58)-Tyr(7.53).

Toward an understanding of agonist binding to human Orexin-1 and Orexin-2 receptors with G-protein-coupled receptor modeling and site-directed mutagenesis

The class A G-protein-coupled receptors (GPCRs) Orexin-1 (OX1) and Orexin-2 (OX2) are located predominantly in the brain and are linked to a range of different physiological functions, including the control of feeding, energy metabolism, modulation of neuro-endocrine function, and regulation of the sleep-wake cycle. The natural agonists for OX1 and OX2 are two neuropeptides, Orexin-A and Orexin-B, which have activity at both receptors. Site-directed mutagenesis (SDM) has been reported on both the receptors and the peptides and has provided important insight into key features responsible for agonist activity. However, the structural interpretation of how these data are linked together is still lacking. In this work, we produced and used SDM data, homology modeling followed by MD simulation, and ensemble-flexible docking to generate binding poses of the Orexin peptides in the OX receptors to rationalize the SDM data. We also developed a protein pairwise similarity comparing method (ProS) and a GPCR-likeness assessment score (GLAS) to explore the structural data generated within a molecular dynamics simulation and to help distinguish between different GPCR substates. The results demonstrate how these newly developed methods of structural assessment for GPCRs can be used to provide a working model of neuropeptide-Orexin receptor interaction.

Routine Microsecond Molecular Dynamics Simulations with AMBER on GPUs. 2. Explicit Solvent Particle Mesh Ewald

We present an implementation of explicit solvent all atom classical molecular dynamics (MD) within the AMBER program package that runs entirely on CUDA-enabled GPUs. First released publicly in April 2010 as part of version 11 of the AMBER MD package and further improved and optimized over the last two years, this implementation supports the three most widely used statistical mechanical ensembles (NVE, NVT, and NPT), uses particle mesh Ewald (PME) for the long-range electrostatics, and runs entirely on CUDA-enabled NVIDIA graphics processing units (GPUs), providing results that are statistically indistinguishable from the traditional CPU version of the software and with performance that exceeds that achievable by the CPU version of AMBER software running on all conventional CPU-based clusters and supercomputers. We briefly discuss three different precision models developed specifically for this work (SPDP, SPFP, and DPDP) and highlight the technical details of the approach as it extends beyond previously reported work [Götz et al., J. Chem. Theory Comput. 2012, DOI: 10.1021/ct200909j; Le Grand et al., Comp. Phys. Comm. 2013, DOI: 10.1016/j.cpc.2012.09.022].We highlight the substantial improvements in performance that are seen over traditional CPU-only machines and provide validation of our implementation and precision models. We also provide evidence supporting our decision to deprecate the previously described fully single precision (SPSP) model from the latest release of the AMBER software package.

Deconstructing activation events in rhodopsin

Activation of class-A G-protein-coupled receptors (GPCRs) involves large-scale reorganization of the H3/H6 interhelical network. In rhodopsin (Rh), this process is coupled to a change in the protonation state of a key residue, E134, whose exact role in activation is not well understood. Capturing this millisecond pH-dependent process is a well-appreciated challenge. We have developed a scheme combining the harmonic Fourier beads (HFB) method and constant-pH molecular dynamics with pH-based replica exchange (pH-REX) to gain insight into the structural changes that occur along the activation pathway as a function of the protonation state of E134. Our results indicate that E134 is protonated as a consequence of tilting of H6 by ca. 4.0° with respect to its initial position and simultaneous rotation by ca. 23° along its principal axis. The movement of H6 is associated with breakage of the E247-R135 and R135-E134 salt bridges and concomitant release of the E134 side chain, which results in an increase in its pKa value above physiological pH. An increase in the hydrophobicity of the environment surrounding E134 leads to further tilting and rotation of H6 and upshift of the E134 pKa. Such atomic-level information, which is not accessible through experiments, refines the earlier proposed sequential model of Rh activation (see: Zaitseva, E.; et al. Sequential Rearrangement of Interhelical Networks Upon Rhodopsin Activation in Membranes: The Meta IIa Conformational Substate . J. Am. Chem. Soc. 2010, 132, 4815) and argues that the E134 protonation switch is both a cause and a consequence of the H6 motion.

Structure-function studies with G protein-coupled receptors as a paradigm for improving drug discovery and development of therapeutics

There are a great variety of human membrane proteins, and these currently form the largest group of targets for marketed drugs. Despite the advances in drug design, however, promiscuity between drug molecules and targets often leads to undesired signaling effects, which result in unintended side effects. In this review, one family of membrane proteins - the G protein-coupled receptors (GPCRs) - is used as a model to review experimental techniques that may be used to examine the activity of membrane proteins. As these receptors are highly relevant to healthy human physiology and represent the largest family of drug targets, they represent an excellent model for membrane proteins in general. We also review experimental evidence that suggests there may be multiple ways to target a GPCR - and by extension, membrane proteins - to more effectively target unhealthy phenotypes while reducing the occurrence and severity of side effects.