Structural basis of tolvaptan binding to the vasopressin V2 receptor

The vasopressin V2 receptor (V2R) is a validated therapeutic target for autosomal dominant polycystic kidney disease (ADPKD), with tolvaptan being the first FDA-approved antagonist. Herein, we used Gaussian accelerated molecular dynamics simulations to investigate the spontaneous binding of tolvaptan to both active and inactive V2R conformations at the atomic-level. Overall, the binding process consists of two stages. Tolvaptan binds initially to extracellular loops 2 and 3 (ECL2/3) before overcoming an energy barrier to enter the pocket. Our simulations result highlighted key residues (e.g., R181, Y205, F287, F178) involved in this process, which were experimentally confirmed by site-directed mutagenesis. This work provides structural insights into tolvaptan-V2R interactions, potentially aiding the design of novel antagonists for V2R and other G protein-coupled receptors.

Therapeutic potential of vasopressin in the treatment of neurological disorders

Vasopressin (VP) is a nonapeptide made of nine amino acids synthesized by the hypothalamus and released by the pituitary gland. VP acts as a neurohormone, neuropeptide and neuromodulator and plays an important role in the regulation of water balance, osmolarity, blood pressure, body temperature, stress response, emotional challenges, etc. Traditionally VP is known to regulate the osmolarity and tonicity. VP and its receptors are widely expressed in the various region of the brain including cortex, hippocampus, basal forebrain, amygdala, etc. VP has been shown to modulate the behavior, stress response, circadian rhythm, cerebral blood flow, learning and memory, etc. The potential role of VP in the regulation of these neurological functions have suggested the therapeutic importance of VP and its analogues in the management of neurological disorders. Further, different VP analogues have been developed across the world with different pharmacotherapeutic potential. In the present work authors highlighted the therapeutic potential of VP and its analogues in the treatment and management of various neurological disorders.

Extended Metadynamics Protocol for Binding/Unbinding Free Energies of Peptide Ligands to Class A G-Protein-Coupled Receptors

A metadynamics protocol is presented to characterize the binding and unbinding of peptide ligands to class A G-protein-coupled receptors (GPCRs). The protocol expands on the one previously presented for binding and unbinding small-molecule ligands to class A GPCRs and accounts for the more demanding nature of the peptide binding-unbinding process. It applies to almost all class A GPCRs. Exemplary simulations are described for subtypes Y1R, Y2R, and Y4R of the neuropeptide Y receptor family, vasopressin binding to the vasopressin V2 receptor (V2R), and oxytocin binding to the oxytocin receptor (OTR). Binding free energies and the positions of alternative binding sites are presented and, where possible, compared with the experiment.

[3H]UR-JG102-A Radiolabeled Cyclic Peptide with High Affinity and Excellent Selectivity for the Neuropeptide Y Y4 Receptor

The family of human neuropeptide Y receptors (YRs) comprises four subtypes (Y1R, Y2R, Y4R, and Y5R) that are involved in the regulation of numerous physiological processes. Until now, Y4R binding studies have been predominantly performed in hypotonic sodium-free buffers using 125I-labeled derivatives of the endogenous YR agonists pancreatic polypeptide or peptide YY. A few tritium-labeled Y4R ligands have been reported; however, when used in buffers containing sodium at a physiological concentration, their Y4R affinities are insufficient. Based on the cyclic hexapeptide UR-AK86C, we developed a new tritium-labeled Y4R radioligand ([3H]UR-JG102, [3H]20). In sodium-free buffer, [3H]20 exhibits a very low Y4R dissociation constant (Kd 0.012 nM). In sodium-containing buffer (137 mM Na+), the Y4R affinity is lower (Kd 0.11 nM) but still considerably higher compared to previously reported tritiated Y4R ligands. Therefore, [3H]20 represents a useful tool compound for the determination of Y4R binding affinities under physiological-like conditions.

General Metadynamics Protocol To Simulate Activation/Deactivation of Class A GPCRs: Proof of Principle for the Serotonin Receptor

We present a generally applicable metadynamics protocol for characterizing the activation free-energy profiles of class A G-protein coupled receptors and a proof-of-principle study for the 5HT_1A-receptor. The almost universal A¹⁰⁰ activation index, which depends on five inter-helix distances, is used as the single collective variable in well-tempered multiple-walker metadynamics simulations. Here, we show free-energy profiles for the serotonin receptor as binary (apo-receptor + G-protein-α-subunit and receptor + ligand) and ternary complexes with two prototypical orthosteric ligands: the full agonist serotonin and the partial agonist aripiprazole. Our results are not only compatible with previously reported experimental and computational data, but they also allow differences between active and inactive conformations to be determined in unprecedented atomic detail, and with respect to the so-called microswitches that have been suggested as determinants of activation, giving insight into their role in the activation mechanism.

In-silico study of the interactions between acylated glucagon like-peptide-1 analogues and the native receptor

We have performed a series of multiple molecular dynamics (MD) simulations of glucagon-like peptide-1 (GLP-1) and acylated GLP-1 analogues in complex with the endogenous receptor (GLP-1R) to obtain a molecular understanding of how fatty acid (FA) chain structure, acylation position on the peptide, and presence of a linker affect the binding. MD simulations were analysed to extract heatmaps of receptor-peptide interaction patterns and to determine the free energy of binding using the molecular mechanics Poisson-Boltzmann surface area (MM-PBSA) approach. The extracted free energies from MM-PBSA calculations are in qualitative agreement with experimentally determined potencies. Furthermore, the interaction patterns seen in the receptor-GLP-1 complex simulations resemble previously reported binding interactions validating the simulations. Analysing the receptor-GLP-1 analogue complex simulations, we found that the major differences between the systems stem from FA interactions and positioning of acylation in the peptide. Hydrophobic interactions between the FA chain and a hydrophobic patch on the extracellular domain contribute significantly to the binding affinity. Acylation on Lys26 resulted in noticeably more interactions between the FA chain and the extracellular domain hydrophobic patch than found for acylation on Lys34 and Lys38, respectively. The presence of a charged linker between the peptide and FA chain can potentially stabilise the complex by forming hydrogen bonds to arginine residues in the linker region between the extracellular domain and the transmembrane domain. A molecular understanding of the fatty acid structure and its effect on binding provides important insights into designing acylated agonists for GLP-1R.Communicated by Ramaswamy H. Sarma.

Prediction of molecular interactions and physicochemical properties relevant for vasopressin V2 receptor antagonism

We have developed two ligand- and receptor-based computational approaches to study the physicochemical properties relevant to the biological activity of vasopressin V2 receptor (V2R) antagonist and eventually to predict the expected binding mode to V2R. The obtained quantitative structure activity relationship (QSAR) model showed a correlation of the antagonist activity with the hydration energy (E_H2O), the polarizability (P), and the calculated partial charge on atom N7 (q6) of the common substructure. The first two descriptors showed a positive contribution to antagonist activity, while the third one had a negative contribution. V2R was modeled and further relaxed on a 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocoline (POPC) membrane by molecular dynamics simulations. The receptor antagonist complexes were guessed by molecular docking, and the stability of the most relevant structures was also evaluated by molecular dynamics simulations. As a result, amino acid residues Q96, W99, F105, K116, F178, A194, F307, and M311 were identified with the probably most relevant antagonist-receptor interactions on the studied complexes. The proposed QSAR model could explain the molecular properties relevant to the antagonist activity. The contributions to the antagonist-receptor interaction appeared also in agreement with the binding mode of the complexes obtained by molecular docking and molecular dynamics. These models will be used in further studies to look for new V2R potential antagonist molecules.

Conformational selection of vasopressin upon V1a receptor binding

The neuropeptide vasopressin (VP) and its three G protein-coupled receptors (V_1aR, V_1bR and V₂R) are of high interest in a wide array of drug discovery programs. V_1aR is of particular importance due to its cardiovascular functions and diverse roles in the central nervous system. The structure–activity relationships underpinning ligand-receptor interactions remain however largely unclear, hindering rational drug design. This is not least due to the high structural flexibility of VP in its free as well as receptor-bound states. In this work, we developed a novel approach to reveal features of conformational selectivity upon VP-V_1aR complex formation. We employed virtual screening strategies to probe VP’s conformational space for transiently adopted structures that favor binding to V_1aR. To this end, we dissected the VP conformational space into three sub-ensembles, each containing distinct structural sets for VP’s three-residue C-terminal tail. We validated the computational results with experimental nuclear magnetic resonance (NMR) data and docked each sub-ensemble to V_1aR. We observed that the conformation of VP’s three-residue tail significantly modulated the complex dissociation constants. Solvent-exposed and proline trans-configured VP tail conformations bound to the receptor with three-fold enhanced affinities compared to compacted or cis-configured conformations. The solvent-exposed and more flexible structures facilitated unique interaction patterns between VP and V_1aR transmembrane helices 3, 4, and 6 which led to high binding energies. The presented “virtual conformational space screening” approach, integrated with NMR spectroscopy, thus enabled identification and characterization of a conformational selection-type complex formation mechanism that confers novel perspectives on targeting the VP-V_1aR interactions at the level of the encounter complex – an aspect that opens novel research avenues for understanding the functionality of the evolutionary selected conformational properties of VP, as well as guidance for ligand design strategies to provide more potent and selective VP analogues.

The prediction of protein-ligand unbinding for modern drug discovery

INTRODUCTION

Drug-target thermodynamic and kinetic information have perennially important roles in drug design. The prediction of protein-ligand unbinding, which can provide important kinetic information, in experiments continues to face great challenges. Uncovering protein-ligand unbinding through molecular dynamics simulations has become efficient and inexpensive with the progress and enhancement of computing power and sampling methods.

AREAS COVERED

In this review, various sampling methods for protein-ligand unbinding and their basic principles are firstly briefly introduced. Then, their applications in predicting aspects of protein-ligand unbinding, including unbinding pathways, dissociation rate constants, residence time and binding affinity, are discussed.

EXPERT OPINION

Although various sampling methods have been successfully applied in numerous systems, they still have shortcomings and deficiencies. Most enhanced sampling methods require researchers to possess a wealth of prior knowledge of collective variables or reaction coordinates. In addition, most systems studied at present are relatively simple, and the study of complex systems in real drug research remains greatly challenging. Through the combination of machine learning and enhanced sampling methods, prediction accuracy can be further improved, and some problems encountered in complex systems also may be solved.

Metadynamics-Based Approaches for Modeling the Hypoxia-Inducible Factor 2α Ligand Binding Process

Several methods based on enhanced-sampling molecular dynamics have been proposed for studying ligand binding processes. Here, we developed a protocol that combines the advantages of steered molecular dynamics (SMD) and metadynamics. While SMD is proposed for investigating possible unbinding pathways of the ligand and identifying the preferred one, metadynamics, with the path collective variable (PCV) formalism, is suggested to explore the binding processes along the pathway defined on the basis of SMD, by using only two CVs. We applied our approach to the study of binding of two known ligands to the hypoxia-inducible factor 2α, where the buried binding cavity makes simulation of the process a challenging task. Our approach allowed identification of the preferred entrance pathway for each ligand, highlighted the features of the bound and intermediate states in the free-energy surface, and provided a binding affinity scale in agreement with experimental data. Therefore, it seems to be a suitable tool for elucidating ligand binding processes of similar complex systems.

Thermodynamics and Kinetics of Drug-Target Binding by Molecular Simulation

Computational studies play an increasingly important role in chemistry and biophysics, mainly thanks to improvements in hardware and algorithms. In drug discovery and development, computational studies can reduce the costs and risks of bringing a new medicine to market. Computational simulations are mainly used to optimize promising new compounds by estimating their binding affinity to proteins. This is challenging due to the complexity of the simulated system. To assess the present and future value of simulation for drug discovery, we review key applications of advanced methods for sampling complex free-energy landscapes at near nonergodicity conditions and for estimating the rate coefficients of very slow processes of pharmacological interest. We outline the statistical mechanics and computational background behind this research, including methods such as steered molecular dynamics and metadynamics. We review recent applications to pharmacology and drug discovery and discuss possible guidelines for the practitioner. Recent trends in machine learning are also briefly discussed. Thanks to the rapid development of methods for characterizing and quantifying rare events, simulation's role in drug discovery is likely to expand, making it a valuable complement to experimental and clinical approaches.

Capturing Peptide-GPCR Interactions and Their Dynamics

Many biological functions of peptides are mediated through G protein-coupled receptors (GPCRs). Upon ligand binding, GPCRs undergo conformational changes that facilitate the binding and activation of multiple effectors. GPCRs regulate nearly all physiological processes and are a favorite pharmacological target. In particular, drugs are sought after that elicit the recruitment of selected effectors only (biased ligands). Understanding how ligands bind to GPCRs and which conformational changes they induce is a fundamental step toward the development of more efficient and specific drugs. Moreover, it is emerging that the dynamic of the ligand–receptor interaction contributes to the specificity of both ligand recognition and effector recruitment, an aspect that is missing in structural snapshots from crystallography. We describe here biochemical and biophysical techniques to address ligand–receptor interactions in their structural and dynamic aspects, which include mutagenesis, crosslinking, spectroscopic techniques, and mass-spectrometry profiling. With a main focus on peptide receptors, we present methods to unveil the ligand–receptor contact interface and methods that address conformational changes both in the ligand and the GPCR. The presented studies highlight a wide structural heterogeneity among peptide receptors, reveal distinct structural changes occurring during ligand binding and a surprisingly high dynamics of the ligand–GPCR complexes.

Agonist Binding and G Protein Coupling in Histamine H2 Receptor: A Molecular Dynamics Study

The histamine H2 receptor (H₂R) plays an important role in the regulation of gastric acid secretion. Therefore, it is a main drug target for the treatment of gastroesophageal reflux or peptic ulcer disease. However, there is as of yet no 3D-structural information available hampering a mechanistic understanding of H₂R. Therefore, we created a model of the histamine-H₂R-G_s complex based on the structure of the ternary complex of the β2-adrenoceptor and investigated the conformational stability of this active GPCR conformation. Since the physiologically relevant motions with respect to ligand binding and conformational changes of GPCRs can only partly be assessed on the timescale of conventional MD (cMD) simulations, we also applied metadynamics and Gaussian accelerated molecular dynamics (GaMD) simulations. A multiple walker metadynamics simulation in combination with cMD was applied for the determination of the histamine binding mode. The preferential binding pose detected is in good agreement with previous data from site directed mutagenesis and provides a basis for rational ligand design. Inspection of the H₂R-G_s interface reveals a network of polar interactions that may contribute to H₂R coupling selectivity. The cMD and GaMD simulations demonstrate that the active conformation is retained on a μs-timescale in the ternary histamine-H₂R-G_s complex and in a truncated complex that contains only G_s helix α5 instead of the entire G protein. In contrast, histamine alone is unable to stabilize the active conformation, which is in line with previous studies of other GPCRs.

A combined activation mechanism for the glucagon receptor

We report on a combined activation mechanism for a class B G-protein-coupled receptor (GPCR), the glucagon receptor. By computing the conformational free-energy landscape associated with the activation of the receptor-agonist complex and comparing it with that obtained with the ternary complex (receptor-agonist-G protein) we show that the agonist stabilizes the receptor in a preactivated complex, which is then fully activated upon binding of the G protein. The proposed mechanism contrasts with the generally assumed GPCR activation mechanism, which proceeds through an opening of the intracellular region allosterically elicited by the binding of the agonist. The mechanism found here is consistent with electron cryo-microscopy structural data and might be general for class B GPCRs. It also helps us to understand the mode of action of the numerous allosteric antagonists of this important drug target.

Ligand-Binding Calculations with Metadynamics

All-atom molecular dynamics simulations can capture the dynamic degrees of freedom that characterize molecular recognition, the knowledge of which constitutes the cornerstone of rational approaches to drug design and optimization. In particular, enhanced sampling algorithms, such as metadynamics, are powerful tools to dramatically reduce the computational cost required for a mechanistic description of the binding process. Here, we describe the essential details characterizing these simulation strategies, focusing on the critical step of identifying suitable reaction coordinates, as well as on the different analysis algorithms to estimate binding affinity and residence times. We conclude with a survey of published applications that provides explicit examples of successful simulations for several targets.

Understanding Ligand Binding Selectivity in a Prototypical GPCR Family

Adenosine receptors are involved in many pathological conditions and are thus promising drug targets. However, developing drugs that target this GPCR subfamily is a challenging task. A number of drug candidates fail due to lack of selectivity which results in unwanted side effects. The extensive structural similarity of adenosine receptors complicates the design of selective ligands. The problem of selective targeting is a general concern in GPCRs, and in this respect adenosine receptors are a prototypical example. Here we use enhanced sampling simulations to decipher the determinants of selectivity of ligands in A_2a and A₁ adenosine receptors. Our model shows how small differences in the binding pocket and in the water network around the ligand can be leveraged to achieve selectivity.

A Metadynamics-Based Protocol for the Determination of GPCR-Ligand Binding Modes

G protein-coupled receptors (GPCRs) are a main drug target and therefore a hot topic in pharmaceutical research. One important prerequisite to understand how a certain ligand affects a GPCR is precise knowledge about its binding mode and the specific underlying interactions. If no crystal structure of the respective complex is available, computational methods can be used to deduce the binding site. One of them are metadynamics simulations which have the advantage of an enhanced sampling compared to conventional molecular dynamics simulations. However, the enhanced sampling of higher-energy states hampers identification of the preferred binding mode. Here, we present a novel protocol based on clustering of multiple walker metadynamics simulations which allows identifying the preferential binding mode from such conformational ensembles. We tested this strategy for three different model systems namely the histamine H₁ receptor in combination with its physiological ligand histamine, as well as the β2 adrenoceptor with its agonist adrenaline and its antagonist alprenolol. For all three systems, the proposed protocol was able to reproduce the correct binding mode known from the literature suggesting that the approach can more generally be applied to the prediction of GPCR ligand binding in future.

Binding of histamine to the H1 receptor-a molecular dynamics study

Binding of histamine to the G-protein coupled histamine H₁ receptor plays an important role in the context of allergic reactions; however, no crystal structure of the resulting complex is available yet. To deduce the histamine binding site, we performed unbiased molecular dynamics (MD) simulations on a microsecond time scale, which allowed to monitor one binding event, in which particularly the residues of the extracellular loop 2 were involved in the initial recognition process. The final histamine binding pose in the orthosteric pocket is characterized by interactions with Asp107^3.32, Tyr108^3.33, Thr194^5.43, Asn198^5.46, Trp428^6.48, Tyr431^6.51, Phe432^6.52, and Phe435^6.55, which is in agreement with existing mutational data. The conformational stability of the obtained complex structure was subsequently confirmed in 2 μs equilibrium MD simulations, and a metadynamics simulation proved that the detected binding site represents an energy minimum. A complementary investigation of a D107A mutant, which has experimentally been shown to abolish ligand binding, revealed that this exchange results in a significantly weaker interaction and enhanced ligand dynamics. This finding underlines the importance of the electrostatic interaction between the histamine ammonium group and the side chain of Asp107^3.32 for histamine binding.

Full rescue of an inactive olfactory receptor mutant by elimination of an allosteric ligand-gating site

Ligand-gating has recently been proposed as a novel mechanism to regulate olfactory receptor sensitivity. TAAR13c, the zebrafish olfactory receptor activated by the death-associated odor cadaverine, appears to possess an allosteric binding site for cadaverine, which was assumed to block progress of the ligand towards the internal orthosteric binding-and-activation site. Here we have challenged the suggested gating mechanism by modeling the entry tunnel for the ligand as well as the ligand path inside the receptor. We report an entry tunnel, whose opening is blocked by occupation of the external binding site by cadaverine, confirming the hypothesized gating mechanism. A multistep docking algorithm suggested a plausible path for cadaverine from the allosteric to the orthosteric binding-and-activation site. Furthermore we have combined a gain-of-function gating site mutation and a loss-of-function internal binding site mutation in one recombinant receptor. This receptor had almost wildtype ligand affinities, consistent with modeling results that showed localized effects for each mutation. A novel mutation of the suggested gating site resulted in increased receptor ligand affinity. In summary both the experimental and the modeling results provide further evidence for the proposed gating mechanism, which surprisingly exhibits pronounced similarity to processes described for some metabotropic neurotransmitter receptors.

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.

The Molecular Mechanism Underlying Ligand Binding to the Membrane-Embedded Site of a G-Protein-Coupled Receptor

The crystal structure of P2Y₁ receptor (P2Y₁R), a class A GPCR, revealed a special extra-helical site for its antagonist, BPTU, which locates in-between the membrane and the protein. However, due to the limitation of crystallization experiments, the membrane was mimicked by use of detergents, and the information related to the binding of BPTU to the receptor in the membrane environment is rather limited. In the present work, we conducted a total of ∼7.5 μs all-atom simulations in explicit solvent using conventional molecular dynamics and multiple enhanced sampling methods, with models of BPTU and a POPC bilayer, both in the absence and presence of P2Y₁R. Our simulations revealed that BPTU prefers partitioning into the interface of polar/lipophilic region of the lipid bilayer before associating with the receptor. Then, it interacts with the second extracellular loop of the receptor and reaches the binding site through the lipid-receptor interface. In addition, by use of funnel-metadynamics simulations which efficiently enhance the sampling of bound and unbound states, we provide a statistically accurate description of the underlying binding free energy landscape. The calculated absolute ligand-receptor binding affinity is in excellent agreement with the experimental data (Δ G_{b0_theo} = -11.5 kcal mol^-1, Δ G_{b0_exp}= -11.7 kcal mol^-1). Our study broadens the view of the current experimental/theoretical models and our understanding of the protein-ligand recognition mechanism in the lipid environment. The strategy used in this work is potentially applicable to investigate ligands association/dissociation with other membrane-embedded sites, allowing identification of compounds targeting membrane receptors of pharmacological interest.

Multiple Binding Sites Contribute to the Mechanism of Mixed Agonistic and Positive Allosteric Modulators of the Cannabinoid CB1 Receptor

The cannabinoid CB1 receptor (CB1R) is an abundant metabotropic G-protein-coupled receptor that has been difficult to address therapeutically because of CNS side effects exerted by orthosteric drug candidates. Recent efforts have focused on developing allosteric modulators that target CB1R. Compounds from the recently discovered class of mixed agonistic and positive allosteric modulators (Ago-PAMs) based on 2-phenylindoles have shown promising functional and binding properties as CB1R ligands. Here, we identify binding modes of both the CP 55,940 agonist and GAT228, a 2-phenylindole allosteric modulator, by using our metadynamics simulation protocol, and quantify their affinity and cooperativity by atomistic simulations. We demonstrate the involvement of multiple adjunct binding sites in the Ago-PAM characteristics of the 2-phenylindole modulators and explain their ability to compete with orthosteric agonists at higher concentrations. We validate these results experimentally by showing the contribution of multiple sites on the allosteric binding of ZCZ011, another homologous member of the class, together with the orthosteric agonist.

G-Protein coupled receptors: answers from simulations

Molecular-dynamics (MD) simulations are playing an increasingly important role in research into the modes of action of G-protein coupled receptors (GPCRs). In this field, MD simulations are unusually important as, because of the difficult experimental situation, they often offer the only opportunity to determine structural and mechanistic features in atomistic detail. Modern combinations of soft- and hardware have made MD simulations a powerful tool in GPCR research. This is important because GPCRs are targeted by approximately half of the drugs on the market, so that computer-aided drug design plays a major role in GPCR research.

An Efficient Metadynamics-Based Protocol To Model the Binding Affinity and the Transition State Ensemble of G-Protein-Coupled Receptor Ligands

A generally applicable metadynamics scheme for predicting the free energy profile of ligand binding to G-protein-coupled receptors (GPCRs) is described. A common and effective collective variable (CV) has been defined using the ideally placed and highly conserved Trp6.48 as a reference point for ligand-GPCR distance measurement and the common orientation of GPCRs in the cell membrane. Using this single CV together with well-tempered multiple-walker metadynamics with a funnel-like boundary allows an efficient exploration of the entire ligand binding path from the extracellular medium to the orthosteric binding site, including vestibule and intermediate sites. The protocol can be used with X-ray structures or high-quality homology models (based on a high-quality template and after thorough refinement) for the receptor and is universally applicable to agonists, antagonists, and partial and reverse agonists. The root-mean-square error (RMSE) in predicted binding free energies for 12 diverse ligands in five receptors (a total of 23 data points) is surprisingly small (less than 1 kcal mol^-1). The RMSEs for simulations that use receptor X-ray structures and homology models are very similar.

Investigating allosteric effects on the functional dynamics of β2-adrenergic ternary complexes with enhanced-sampling simulations

Signalling by G-protein coupled receptors usually occurs via ternary complexes formed under cooperative binding between the receptor, a ligand and an intracellular binding partner (a G-protein or β-arrestin). While a global rational for allosteric effects in ternary complexes would be of great help in designing ligands with specific effects, the paucity of structural data for ternary complexes with β-arrestin, together with the intrinsic difficulty of characterizing the dynamics involved in the allosteric coupling, have hindered the efforts to devise such a model. Here we have used enhanced-sampling atomistic molecular-dynamics simulations to investigate the dynamics and complex formation mechanisms of both β-arrestin- and Gs-complexes with the β2-adrenergic receptor (ADRB2) in its apo-form and in the presence of four small ligands that exert different allosteric effects. Our results suggest that the structure and dynamics of arrestin-ADRB2 complexes depend strongly on the nature of the small ligands. The complexes exhibit a variety of different coupling orientations in terms of the depth of the finger loop in the receptor and activation states of ADRB2. The simulations also allow us to characterize the cooperativity between the ligand and intracellular binding partner (IBP). Based on the complete and consistent results, we propose an experimentally testable extended ternary complex model, where direction of the cooperative effect between ligand and IBP (positive or negative) and its magnitude are predicted to be a characteristic of the ligand signaling bias. This paves the avenue to the rational design of ligands with specific functional effects.

Metadynamics for Perspective Drug Design: Computationally Driven Synthesis of New Protein-Protein Interaction Inhibitors Targeting the EphA2 Receptor

Metadynamics (META-D) is emerging as a powerful method for the computation of the multidimensional free-energy surface (FES) describing the protein-ligand binding process. Herein, the FES of unbinding of the antagonist N-(3α-hydroxy-5β-cholan-24-oyl)-l-β-homotryptophan (UniPR129) from its EphA2 receptor was reconstructed by META-D simulations. The characterization of the free-energy minima identified on this FES proposes a binding mode fully consistent with previously reported and new structure-activity relationship data. To validate this binding mode, new N-(3α-hydroxy-5β-cholan-24-oyl)-l-β-homotryptophan derivatives were designed, synthesized, and tested for their ability to displace ephrin-A1 from the EphA2 receptor. Among them, two antagonists, namely compounds 21 and 22, displayed high affinity versus the EphA2 receptor and resulted endowed with better physicochemical and pharmacokinetic properties than the parent compound. These findings highlight the importance of free-energy calculations in drug design, confirming that META-D simulations can be used to successfully design novel bioactive compounds.

Intermolecular cascade annulations of N-(arylsulfonyl)acrylamides with dual C(sp3)–H bonds: divergent access to indanes and pyrrolidin-2-ones

A new divergent cascade annulation reaction of N-(arylsulfonyl)acrylamides and dual alkyl C(sp³)–H bonds for selectively producing indanes and pyrrolidin-2-ones is presented.