What can we learn from molecular dynamics simulations for GPCR drug design?

Molecular Dynamics as a Tool for Virtual Ligand Screening

Rational drug design is essential for new drugs to emerge, especially when the structure of a target protein or nucleic acid is known. To that purpose, high-throughput virtual ligand screening campaigns aim at discovering computationally new binding molecules or fragments to modulate particular biomolecular interactions or biological activities, related to a disease process. The structure-based virtual ligand screening process primarily relies on docking methods which allow predicting the binding of a molecule to a biological target structure with a correct conformation and the best possible affinity. The docking method itself is not sufficient as it suffers from several and crucial limitations (lack of full protein flexibility information, no solvation and ion effects, poor scoring functions, and unreliable molecular affinity estimation).At the interface of computer techniques and drug discovery, molecular dynamics (MD) allows introducing protein flexibility before or after a docking protocol, refining the structure of protein-drug complexes in the presence of water, ions, and even in membrane-like environments, describing more precisely the temporal evolution of the biological complex and ranking these complexes with more accurate binding energy calculations. In this chapter, we describe the up-to-date MD, which plays the role of supporting tools in the virtual ligand screening (VS) process.Without a doubt, using docking in combination with MD is an attractive approach in structure-based drug discovery protocols nowadays. It has proved its efficiency through many examples in the literature and is a powerful method to significantly reduce the amount of required wet experimentations (Tarcsay et al, J Chem Inf Model 53:2990-2999, 2013; Barakat et al, PLoS One 7:e51329, 2012; De Vivo et al, J Med Chem 59:4035-4061, 2016; Durrant, McCammon, BMC Biol 9:71-79, 2011; Galeazzi, Curr Comput Aided Drug Des 5:225-240, 2009; Hospital et al, Adv Appl Bioinforma Chem 8:37-47, 2015; Jiang et al, Molecules 20:12769-12786, 2015; Kundu et al, J Mol Graph Model 61:160-174, 2015; Mirza et al, J Mol Graph Model 66:99-107, 2016; Moroy et al, Future Med Chem 7:2317-2331, 2015; Naresh et al, J Mol Graph Model 61:272-280, 2015; Nichols et al, J Chem Inf Model 51:1439-1446, 2011; Nichols et al, Methods Mol Biol 819:93-103, 2012; Okimoto et al, PLoS Comput Biol 5:e1000528, 2009; Rodriguez-Bussey et al, Biopolymers 105:35-42, 2016; Sliwoski et al, Pharmacol Rev 66:334-395, 2014).

Studying allosteric regulation of chemokines and antagonists using a nanoscale hCCR3 receptor sensor

CC chemokine receptor-3 (hCCR3), a G protein-coupled receptor (GPCR) expressed predominantly on eosinophils, is an important drug target. However, it was unclear how chemokine ligands, activators and antagonists recognize hCCR3, and quantitative measurements of hCCR3 inhibition or activation were rare. This study constructed a nanogold receptor sensor using hCCR3 as the molecular recognition element and horseradish peroxidase as the signal amplifier. We quantified the kinetic antagonism between chemokines and hCCR3 before and after adding hCCR3 antagonists. A molecular docking study was carried out to investigate how hCCR3 and its ligands work. The study results indicate chemokines interact with hCCR3 at low concentrations, and reversible hCCR3 inhibitors solely inhibit hCCR3, not CCLs. Moreover, a quantitative evaluation of hCCR3 chemokine activators and their antagonists was carried out using a directed weighted network. This offers a novel approach to quantitatively evaluate chemokine-receptor activation and antagonism together. This research could potentially offer new insights into the mechanisms of action of chemokines and drug screening.

Molecular Determinants of EphA2 and EphB2 Antagonism Enable the Design of Ligands with Improved Selectivity

With the aim of identifying novel antagonists selective for the EphA receptor family, a combined experimental and computational approach was taken to investigate the molecular basis of the recognition between a prototypical Eph-ephrin antagonist (UniPR1447) and two representative receptors of the EphA and EphB subfamilies, namely, EphA2 and EphB2 receptors. The conformational free-energy surface (FES) of the binding state of UniPR1447 within the ligand binding domain of EphA2 and EphB2, reconstructed from molecular dynamics (MD) simulations performed on the microsecond time scale, was exploited to drive the design and synthesis of a novel antagonist selective for EphA2 over the EphB2 receptor. The availability of compounds with this pharmacological profile will help discriminate the importance of these two receptors in the insurgence and progression of cancer.

Identification of a β-arrestin-biased negative allosteric modulator for the β2-adrenergic receptor

Catecholamine-stimulated β2-adrenergic receptor (β2AR) signaling via the canonical Gs-adenylyl cyclase-cAMP-PKA pathway regulates numerous physiological functions, including the therapeutic effects of exogenous β-agonists in the treatment of airway disease. β2AR signaling is tightly regulated by GRKs and β-arrestins, which together promote β2AR desensitization and internalization as well as downstream signaling, often antithetical to the canonical pathway. Thus, the ability to bias β2AR signaling toward the Gs pathway while avoiding β-arrestin-mediated effects may provide a strategy to improve the functional consequences of β2AR activation. Since attempts to develop Gs-biased agonists and allosteric modulators for the β2AR have been largely unsuccessful, here we screened small molecule libraries for allosteric modulators that selectively inhibit β-arrestin recruitment to the receptor. This screen identified several compounds that met this profile, and, of these, a difluorophenyl quinazoline (DFPQ) derivative was found to be a selective negative allosteric modulator of β-arrestin recruitment to the β2AR while having no effect on β2AR coupling to Gs. DFPQ effectively inhibits agonist-promoted phosphorylation and internalization of the β2AR and protects against the functional desensitization of β-agonist mediated regulation in cell and tissue models. The effects of DFPQ were also specific to the β2AR with minimal effects on the β1AR. Modeling, mutagenesis, and medicinal chemistry studies support DFPQ derivatives binding to an intracellular membrane-facing region of the β2AR, including residues within transmembrane domains 3 and 4 and intracellular loop 2. DFPQ thus represents a class of biased allosteric modulators that targets an allosteric site of the β2AR.

Unbinding Kinetics of Muscarinic M3 Receptor Antagonists Explained by Metadynamics Simulations

The residence time (RT), the time for which a drug remains bound to its biological target, is a critical parameter for drug design. The prediction of this key kinetic property has been proven to be challenging and computationally demanding in the framework of atomistic simulations. In the present work, we setup and applied two distinct metadynamics protocols to estimate the RTs of muscarinic M3 receptor antagonists. In the first method, derived from the conformational flooding approach, the kinetics of unbinding is retrieved from a physics-based parameter known as the acceleration factor α (i.e., the running average over time of the potential deposited in the bound state). Such an approach is expected to recover the absolute RT value for a compound of interest. In the second method, known as the t_META-D approach, a qualitative estimation of the RT is given by the time of simulation required to drive the ligand from the binding site to the solvent bulk. This approach has been developed to reproduce the change of experimental RTs for compounds targeting the same target. Our analysis shows that both computational protocols are able to rank compounds in agreement with their experimental RTs. Quantitative structure-kinetics relationship (SKR) models can be identified and employed to predict the impact of a chemical modification on the experimental RT once a calibration study has been performed.

Thermodynamic architecture and conformational plasticity of GPCRs

G-protein-coupled receptors (GPCRs) are ubiquitous integral membrane proteins involved in diverse cellular signaling processes. Here, we carry out a large-scale ensemble thermodynamic study of 45 ligand-free GPCRs employing a structure-based statistical mechanical framework. We find that multiple partially structured states co-exist in the GPCR native ensemble, with the TM helices 1, 6 and 7 displaying varied folding status, and shaping the conformational landscape. Strongly coupled residues are anisotropically distributed, accounting for only 13% of the residues, illustrating that a large number of residues are inherently dynamic. Active-state GPCRs are characterized by reduced conformational heterogeneity with altered coupling-patterns distributed throughout the structural scaffold. In silico alanine-scanning mutagenesis reveals that extra- and intra-cellular faces of GPCRs are coupled thermodynamically, highlighting an exquisite structural specialization and the fluid nature of the intramolecular interaction network. The ensemble-based perturbation methodology presented here lays the foundation for understanding allosteric mechanisms and the effects of disease-causing mutations in GCPRs.

Self-docking and cross-docking simulations of G protein-coupled receptor-ligand complexes: Impact of ligand type and receptor activation state

G protein-coupled receptors (GPCR) are the largest family of cell surface receptors in vertebrates. Their abundance and role in nearly all physiological systems make GPCR the largest protein family targeted for development of pharmaceuticals. Ligand discovery aimed at identification of chemical tools and drug leads is aided by molecular docking simulations that allow critical analysis of the potential interactions between small molecules and proteins in resulting complexes. However, blind assessments of ligand pose quality and affinity prediction have thus far not provided broadly generalizable performance expectations for docking into experimentally-characterized GPCR targets. Likewise, the relative importance of receptor activation state and ligand function differences have also not been systematically assessed. This study compares performance when docking ligands of varied function into varied GPCR activation states in the absence of extensive resampling of the input GPCR structure, and only limited sidechain flexibility after ligand placement. Simulations were performed using 37 experimental structures of 11 Class A GPCR crystallized in multiple activation states (giving rise to 37 self-docking and 68 cross docking simulations). Our results show that one specific subset of cross-docking simulations gave results of similar quality to self-docking. Median ligand RMSD values for top-scored poses were 1.2 Å and 2.0 Å for self-docking and State_Match/Function_Match cross-docking, respectively. The distributions of ligand RMSD values were not statistically different for these two conditions, according to a Kolmogorov-Smirnov test. Therefore, docking performance against GPCR targets can be estimated in advance based on docking target structure activation states, with higher accuracy expected when docking agonists into active state structures and inverse agonists or antagonists into inactive state structures. Receptor conformational sampling in advance of docking or receptor conformational adjustment after docking are more likely to produce substantial improvements for other pairings of receptor activation state and ligand function.

Mechanistic Understanding from Molecular Dynamics in Pharmaceutical Research 2: Lipid Membrane in Drug Design

We review the use of molecular dynamics (MD) simulation as a drug design tool in the context of the role that the lipid membrane can play in drug action, i.e., the interaction between candidate drug molecules and lipid membranes. In the standard "lock and key" paradigm, only the interaction between the drug and a specific active site of a specific protein is considered; the environment in which the drug acts is, from a biophysical perspective, far more complex than this. The possible mechanisms though which a drug can be designed to tinker with physiological processes are significantly broader than merely fitting to a single active site of a single protein. In this paper, we focus on the role of the lipid membrane, arguably the most important element outside the proteins themselves, as a case study. We discuss work that has been carried out, using MD simulation, concerning the transfection of drugs through membranes that act as biological barriers in the path of the drugs, the behavior of drug molecules within membranes, how their collective behavior can affect the structure and properties of the membrane and, finally, the role lipid membranes, to which the vast majority of drug target proteins are associated, can play in mediating the interaction between drug and target protein. This review paper is the second in a two-part series covering MD simulation as a tool in pharmaceutical research; both are designed as pedagogical review papers aimed at both pharmaceutical scientists interested in exploring how the tool of MD simulation can be applied to their research and computational scientists interested in exploring the possibility of a pharmaceutical context for their research.

Constituents of buriti oil (Mauritia flexuosa L.) like inhibitors of the SARS-Coronavirus main peptidase: an investigation by docking and molecular dynamics

Statistics show alarming numbers of infected and killed in the world, caused by the Covid-19 pandemic, which still doesn't have a specific treatment and effective in combating all efforts to seek treatments and medications against this disease. Natural products are of relevant interest in the search for new drugs. Thus, Buriti oil (Mauritia flexuosa L.) is a natural product extracted from the fruit of the palm and is quite common in the legal Amazon region, Brazil. In the present work, the anti-Covid-19 biological activity of some constituents of Buriti oil was investigated using in silico methods of Molecular Docking and Molecular Dynamics Simulations. The main results of Molecular Docking revealed favorable interaction energies in the formation of the 2GTB peptidase complex (main peptidase of SARS-CoV) with the 13-cis-β-carotene ligands (ΔGbind = -10.23Kcal mol-1), 9-cis -β-carotene (ΔGbind = -9.82Kcal mol-1), and α-carotene (ΔGbind = -8.34Kcal mol-1). Molecular Dynamics simulations demonstrated considerable interaction for these ligands with emphasis on α-carotene. Such theoretical results encourage and enable a direction for experimental studies in vitro and in vivo, essential in the development of new drugs with enzymatic inhibitory action for Covid-19.Communicated by Ramaswamy H. Sarma.

Biomolecular Modeling and Simulation: A Prospering Multidisciplinary Field

We reassess progress in the field of biomolecular modeling and simulation, following up on our perspective published in 2011. By reviewing metrics for the field's productivity and providing examples of success, we underscore the productive phase of the field, whose short-term expectations were overestimated and long-term effects underestimated. Such successes include prediction of structures and mechanisms; generation of new insights into biomolecular activity; and thriving collaborations between modeling and experimentation, including experiments driven by modeling. We also discuss the impact of field exercises and web games on the field's progress. Overall, we note tremendous success by the biomolecular modeling community in utilization of computer power; improvement in force fields; and development and application of new algorithms, notably machine learning and artificial intelligence. The combined advances are enhancing the accuracy andscope of modeling and simulation, establishing an exemplary discipline where experiment and theory or simulations are full partners.

Investigating the role of N-terminal domain in phosphodiesterase 4B-inhibition by molecular dynamics simulation

Phosphodiesterase 4B (PDE4B) is a potential therapeutic target for the inflammatory respiratory diseases such as congestive obstructive pulmonary disease (COPD) and asthma. The sequence identity of ∼88% with its isoform PDE4D is the key barrier in developing selective PDE4B inhibitors which may help to overcome associated side effects. Despite high sequence identity, both isoforms differ in few residues present in N-terminal (UCR2) and C-terminal (CR3) involved in catalytic site formation. Previously, we designed and tested specific PDE4B inhibitors considering N-terminal residues as a part of the catalytic cavity. In continuation, current work thoroughly presents an MD simulation-based analysis of N-terminal residues and their role in ligand binding. The various parameters viz. root mean square deviation (RMSD), radius of gyration (Rg), root mean square fluctuation (RMSF), principal component analysis (PCA), dynamical cross-correlation matrix (DCCM) analysis, secondary structure analysis and residue interaction mapping were investigated to establish rational. Results showed that UCR2 reduced RMSF values for the metal binding pocket (31.5 ± 11 to 13.12 ± 6 Ų) and the substrate-binding pocket (38.8 ± 32 to 17.3 ± 11 Ų). UCR2 enhanced anti-correlated motion at the active site region that led to the improved ligand-binding affinity of PDE4B from -24.57 ± 3 to -35.54 ± 2 kcal/mol. Further, the atomic-level analysis indicated that T-π and π-π interactions between inhibitors and residues are vital forces that regulate inhibitor association to PDE4B with high affinity. In conclusion, UCR2, the N-terminal domain, embraces the dynamics of PDE4B active site and stabilizes PDE4B inhibitor interactions. Therefore the N-terminal domain needs to be considered while designing next-generation, selective PDE4B-inhibitors as potential anti-inflammatory drugs. Communicated by Ramaswamy H. Sarma.

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.

In silico studies of the inhibition mechanism of dengue with papain

Dengue virus is becoming a major global disease; the envelope protein is the major target for vaccine development against Dengue. Nowadays, the attention has focused on developing inhibitors based on Papain is a promising target for treating Dengue. In the present work, the theoretical studies of E-protein(Cys74-Glu79;Lys110)…Papain(Cys25, Asn175 and His159) complexes are analysed by Density Functional Theory (M06-2X/cc-pVDZ) method. Among the E-protein(Cys74-Glu79;Lys110)…Papain(Cys25, Asn175 and Hys159) complexes, E-protein(Glu76)…Papain(Cys25) complex has the highest interaction value of -352.22 kcal/mol. Moreover, the natural bond orbital analysis also supports the above results. The 100 ns Molecular Dynamics simulation reveals that, E-protein(Ala54-Ile129)…Papain(Cys25) complex had the lowest root mean square deviation value of 1 Å compared to the E-protein(Ala54-Ile129)… Papain(Asn175 & His159) complexes. The salt bridge formation between the Asp103 and Lys110 residues are the important stabilizing factor in E-protein(Ala54-Ile129)…Papain(Cys25) complex. This result can extend our knowledge of the functional behaviour of Papain and provides structural insight to target Envelope protein as forthcoming drug targets in Dengue.

Molecular Mechanism of S1P Binding and Activation of the S1P1 Receptor

Sphingosine-1-phosphate (S1P) is a lipidic mediator in mammals that functions either as a second messenger or as a ligand. In the latter case, it is transported by its HDL-associated apoM carrier and circulated in blood where it binds to specific S1P receptors on cell membranes and induces downstream reactions. Although S1P signaling pathways are essential for many biological processes, they are poorly understood at the molecular level. Here, the solved crystal structures of the S1P₁ receptor were used to evaluate molecular dynamics (MD) simulations to generate greater detailed molecular insights into the mechanism of S1P signaling. The MD simulations provided observations at the coarse-grained and atomic levels indicating that S1P may access the receptor binding pocket directly from solvents. Lifting of the bulky N-terminal cap region of the receptor precedes initial S1P binding. Glu121^3.29 guides S1P penetration, and together with Arg292^7.34 is responsible for the stabilization of S1P in the binding pocket, which is consistent with experimental predictions. The complete binding of S1P is followed by receptor activation, wherein Trp269^6.48 moves toward the transmembrane helix (TM) 7, resulting in the formation of an enhanced hydrogen bond network in the lower region of TM7. The distance between TM3 and TM6 is subsequently increased, resulting in the opening of the intracellular binding pocket that enables G protein binding. Further analysis of the force distribution network in the receptor yielded a detailed molecular understanding of the signal transmission network that is activated upon agonist binding.

A Chemosensory GPCR as a Potential Target to Control the Root-Knot Nematode Meloidogyne incognita Parasitism in Plants

Root-knot nematodes (RKN), from the Meloidogyne genus, have a worldwide distribution and cause severe economic damage to many life-sustaining crops. Because of their lack of specificity and danger to the environment, most chemical nematicides have been banned from use. Thus, there is a great need for new and safe compounds to control RKN. Such research involves identifying beforehand the nematode proteins essential to the invasion. Since G protein-coupled receptors GPCRs are the target of a large number of drugs, we have focused our research on the identification of putative nematode GPCRs such as those capable of controlling the movement of the parasite towards (or within) its host. A datamining procedure applied to the genome of Meloidogyne incognita allowed us to identify a GPCR, belonging to the neuropeptide GPCR family that can serve as a target to carry out a virtual screening campaign. We reconstructed a 3D model of this receptor by homology modeling and validated it through extensive molecular dynamics simulations. This model was used for large scale molecular dockings which produced a filtered limited set of putative antagonists for this GPCR. Preliminary experiments using these selected molecules allowed the identification of an active compound, namely C260-2124, from the ChemDiv provider, which can serve as a starting point for further investigations.

Route to Prolonged Residence Time at the Histamine H1 Receptor: Growing from Desloratadine to Rupatadine

Drug–target binding kinetics are an important predictor of in vivo drug efficacy, yet the relationship between ligand structures and their binding kinetics is often poorly understood. We show that both rupatadine (1) and desloratadine (2) have a long residence time at the histamine H₁ receptor (H₁R). Through development of a [³H]levocetirizine radiolabel, we find that the residence time of 1 exceeds that of 2 more than 10-fold. This was further explored with 22 synthesized rupatadine and desloratadine analogues. Methylene-linked cycloaliphatic or β-branched substitutions of desloratadine increase the residence time at the H₁R, conveying a longer duration of receptor antagonism. However, cycloaliphatic substituents directly attached to the piperidine amine (i.e., lacking the spacer) have decreased binding affinity and residence time compared to their methylene-linked structural analogues. Guided by docking studies, steric constraints within the binding pocket are hypothesized to explain the observed differences in affinity and binding kinetics between analogues.

Enhancing Drug Residence Time by Shielding of Intra-Protein Hydrogen Bonds: A Case Study on CCR2 Antagonists

The target residence time (RT) for a given ligand is one of the important parameters that have to be optimized during drug design. It is well established that shielding the receptor-ligand hydrogen bond (H-bond) interactions from water has been one of the factors in increasing ligand RT. Building on this foundation, here we report that shielding an intra-protein H-bond, which confers rigidity to the binding pocket and which is not directly involved in drug-receptor interactions, can strongly influence RT for CCR2 antagonists. Based on our recently solved CCR2 structure with MK-0812 and molecular dynamics (MD) simulations, we show that the RT for this and structurally related ligands is directly dependent on the shielding of the Tyr120-Glu291 H-bond from the water. If solvated this H-bond is often broken, making the binding pocket flexible and leading to shorter RT.

Aqueous solution interactions with sex hormone-binding globulin and estradiol: a theoretical investigation

Sex hormone-binding globulin (SHBG) is a binding protein that regulates the availability of steroid hormones in the plasma. Although best known as a steroid carrier, recent studies have associated SHBG in modulating behavioral aspects related to sexual receptivity. Among steroids, estradiol (17β-estradiol, oestradiol or E2), documented as the most active endogenous female hormone, exerts important physiological roles in both reproductive and non-reproductive functions. In this framework, we employed molecular dynamics (MD) and docking techniques for quantifying the interaction energy between a complex aqueous solution, composed by different salts, SHBG and E2. As glucose concentration resembles measured levels in diabetes, special emphasis was devoted to analyzing the interaction energy between this carbohydrate, SHBG and E2 molecules. The calculations revealed remarkable interaction energy between glucose and SHBG surface. Surprisingly, a movement of solute components toward SHBG was observed, yielding clusters surrounding the protein. The high energy and short distance between glucose and SHBG suggests a possible scenario in favor of a detainment state between the sugar and the protein. In this context, we found that glucose clustering does not insert modification on binding site area nor over binding energy SHBG-E2 complex, in spite of protein superficial area increment. The calculations also point to a more pronounced interaction between E2 and glucose, considering the hormone immersed in the solution. In summary, our findings contribute to a better comprehension of both SHBG and E2 interplay with aqueous solution components.

Decoding Corticotropin-Releasing Factor Receptor Type 1 Crystal Structures

The structural analysis of class B G protein-coupled receptors (GPCR), cell surface proteins responding to peptide hormones, has until recently been restricted to the extracellular domain (ECD). Cor-ticotropin-releasing factor receptor type 1 (CRF1R) is a class B receptor mediating stress response and also considered a drug target for depression and anxiety. Here we report the crystal structure of the trans-membrane domain of human CRF1R in complex with the small-molecule antagonist CP-376395 in a hex-agonal setting with translational non-crystallographic symmetry. Molecular dynamics and metadynamics simulations on this novel structure and the existing TMD structure for CRF1R provides insight as to how the small molecule ligand gains access to the induced-fit allosteric binding site with implications for the observed selectivity against CRF2R. Furthermore, molecular dynamics simulations performed using a full-length receptor model point to key interactions between the ECD and extracellular loop 3 of the TMD providing insight into the full inactive state of multidomain class B GPCRs.

Better agonist for the opioid receptors

This commentary highlights the recent work published in journal Nature on the structural based discovery of novel analgesic compounds for opioid receptors with minimal effects. Manglik et al. selectively targeted the Gi based μOR pathway instead of the β-arrestin pathway of the opioids. The computational screening of millions of compounds showed a list of several competent ligands. From these ligands they synthesized the compounds with the best docking score, which were further optimized by adding side residues for better interaction with the μOR. A promising compound, PZM21, was a selective agonist of μOR. It has better analgesic properties with minimal side effects of respiratory depression and constipation. This work is a step towards better drug designing and synthesizing in terms of efficacy, specificity with least side effects of targeted GPCR proteins present in the human proteome.

Exploring the mechanism of F282L mutation-caused constitutive activity of GPCR by a computational study

G-protein-coupled receptors (GPCRs) are important drug targets and generally activated by ligands. However, some experiments found that GPCRs also give rise to constitutive activity through some mutations (viz., CAM), which are usually associated with different kinds of diseases. However, the mechanisms of CAMs and their roles in interactions with drug-ligands are unclear in experiments. Herein, we used microsecond molecular dynamics simulations to study the effect of one important F282L mutation on β2AR in order to address the questions above. With the aid of principle component and correlation analysis, our results revealed that the F282L mutation could increase the instability of the overall structure, increase the dramatic fluctuations of NPxxY and extracellular loops, and decrease restraint of the helices through weakening interhelical H-bonding and correlations between residues, which could partly contribute to the constitutive activity reported by the experiments. The observations from the protein structure network (PSN) analysis indicate that the mutant exhibits less information flow than the wild β2AR and weakens the role of TM5 and TM6 in the signal transmission, but it enhances the impact of TM3 on the orthosteric pathway and TM4 on the allosteric one. In addition, the results from the virtual screening reveal that the mutant prefers to select agonists rather than antagonists, similar to the active state but opposite of the inactive state, further confirming that the F282L mutation advances the activation of β2AR. Our observations provide valuable information for understanding the mechanism of the mutation-caused constitutive activity of GPCR and related drug-design.

Computational Methods Used in Hit-to-Lead and Lead Optimization Stages of Structure-Based Drug Discovery

GPCR modeling approaches are widely used in the hit-to-lead (H2L) and lead optimization (LO) stages of drug discovery. The aims of these modeling approaches are to predict the 3D structures of the receptor-ligand complexes, to explore the key interactions between the receptor and the ligand and to utilize these insights in the design of new molecules with improved binding, selectivity or other pharmacological properties. In this book chapter, we present a brief survey of key computational approaches integrated with hierarchical GPCR modeling protocol (HGMP) used in hit-to-lead (H2L) and in lead optimization (LO) stages of structure-based drug discovery (SBDD). We outline the differences in modeling strategies used in H2L and LO of SBDD and illustrate how these tools have been applied in three drug discovery projects.

Molecular Dynamics as a Tool for Virtual Ligand Screening

Rational drug design is essential for new drugs to emerge, especially when the structure of a target protein or catalytic enzyme is known experimentally. To that purpose, high-throughput virtual ligand screening campaigns aim at discovering computationally new binding molecules or fragments to inhibit a particular protein interaction or biological activity. The virtual ligand screening process often relies on docking methods which allow predicting the binding of a molecule into a biological target structure with a correct conformation and the best possible affinity. The docking method itself is not sufficient as it suffers from several and crucial limitations (lack of protein flexibility information, no solvation effects, poor scoring functions, and unreliable molecular affinity estimation).At the interface of computer techniques and drug discovery, molecular dynamics (MD) allows introducing protein flexibility before or after a docking protocol, refining the structure of protein-drug complexes in the presence of water, ions and even in membrane-like environments, and ranking complexes with more accurate binding energy calculations. In this chapter we describe the up-to-date MD protocols that are mandatory supporting tools in the virtual ligand screening (VS) process. Using docking in combination with MD is one of the best computer-aided drug design protocols nowadays. It has proved its efficiency through many examples, described below.

GPCR Homology Model Generation for Lead Optimization

The vast increase of recently solved GPCR X-ray structures forms the basis for GPCR homology modeling to atomistic accuracy. Nowadays, homology models can be employed for GPCR-ligand optimization and have been reported as invaluable tools for drug design in the last few years. Elucidation of the complex GPCR pharmacology and the associated GPCR conformations made clear that different homology models have to be constructed for different activation states of the GPCRs. Therefore, templates have to be chosen accordingly to their sequence homology as well as to their activation state. The subsequent ligand placement is nontrivial, as some recent X-ray structures show very unusual ligand binding sites and solvent involvement, expanding the space of the putative ligand binding site from the generic retinal binding pocket to the whole receptor. In the present study, a workflow is presented starting from the selection of the target sequence, guiding through the GPCR modeling process, and finishing with ligand placement and pose validation.

In Silico Studies Targeting G-protein Coupled Receptors for Drug Research Against Parkinson's Disease

Parkinson's Disease (PD) is a long-term neurodegenerative brain disorder that mainly affects the motor system. The causes are still unknown, and even though currently there is no cure, several therapeutic options are available to manage its symptoms. The development of novel antiparkinsonian agents and an understanding of their proper and optimal use are, indeed, highly demanding. For the last decades, L-3,4-DihydrOxyPhenylAlanine or levodopa (L-DOPA) has been the gold-standard therapy for the symptomatic treatment of motor dysfunctions associated to PD. However, the development of dyskinesias and motor fluctuations (wearing-off and on-off phenomena) associated with long-term L-DOPA replacement therapy have limited its antiparkinsonian efficacy. The investigation for non-dopaminergic therapies has been largely explored as an attempt to counteract the motor side effects associated with dopamine replacement therapy. Being one of the largest cell membrane protein families, G-Protein-Coupled Receptors (GPCRs) have become a relevant target for drug discovery focused on a wide range of therapeutic areas, including Central Nervous System (CNS) diseases. The modulation of specific GPCRs potentially implicated in PD, excluding dopamine receptors, may provide promising non-dopaminergic therapeutic alternatives for symptomatic treatment of PD. In this review, we focused on the impact of specific GPCR subclasses, including dopamine receptors, adenosine receptors, muscarinic acetylcholine receptors, metabotropic glutamate receptors, and 5-hydroxytryptamine receptors, on the pathophysiology of PD and the importance of structure- and ligand-based in silico approaches for the development of small molecules to target these receptors.

Activation-Induced Conformational Changes of Dopamine D3 Receptor Promote the Formation of the Internal Water Channel

The atomic-level dopamine activation mechanism for transmitting extracellular ligand binding events through transmembrane helices to the cytoplasmic G protein remains unclear. In the present study, the complete dopamine D3 receptor (D3R), with a homology-modeled N-terminus, was constructed to dock different ligands to simulate conformational alterations in the receptor’s active and inactive forms during microsecond-timescale molecular dynamic simulations. In agonist-bound systems, the D3R N-terminus formed a “lid-like” structure and lay flat on the binding site opening, whereas in antagonist and inverse agonist-bound systems, the N-terminus exposed the binding cavity. Receptor activation was characterized using the different molecular switch residue distances, and G protein-binding site volumes. A continuous water pathway was observed only in the dopamine-G_αi-bound system. In the inactive D3Rs, water entry was hindered by the hydrophobic layers. Finally, a complete activation mechanism of D3R was proposed. Upon agonist binding, the “lid-like” conformation of the N-terminus induces a series of molecular switches to increase the volume of the D3R cytoplasmic binding part for G protein association. Meanwhile, water enters the transmembrane region inducing molecular switches to assist in opening the hydrophobic layers to form a continuous water channel, which is crucial for maintaining a fully active conformation for signal transduction.

Binding Kinetics and Pathways of Ligands to GPCRs

Previously, drugs were developed focusing on target affinity and selectivity. However, it is becoming evident that the drug-target residence time, related to the off-rate, is an important parameter for successful drug development. The residence time influences both the on-rate and overall effectiveness of drugs. Furthermore, ligand binding is now appreciated to be a multistep process because metastable and/or intermediate binding sites in the extracellular region have been identified. In this review, we summarize experimental ligand-binding data for G-protein-coupled receptors (GPCRs), and their binding pathways, analyzed by molecular dynamics (MD). The kinetics of drug binding to GPCRs are complex and depend on several factors, including charge distribution on the receptor surface, ligand-receptor interactions in the binding channel and the binding site, or solvation.

Insights into the function of opioid receptors from molecular dynamics simulations of available crystal structures

The opioid receptors are key targets in the treatment of acute and chronic pain, and the development of novel analgesics with reduced side effects is crucial in the search for more effective medications. The crystal structures of opioid receptors have provided a wealth of knowledge on many aspects of opioid receptor pharmacology and function, including ligand binding poses, location of the sodium allosteric binding site, conformational changes associated with activation and putative dimeric interfaces. These crystal structures also offer a starting point for molecular dynamics (MD) simulations to capture one aspect of drug design that static structures cannot resolve, namely protein dynamics. With the increase in computing power, MD simulations of crystal structures have become an influential tool in understanding the function of GPCRs in general. Here, we discuss lessons learned from MD simulations of opioid receptor crystal structures with reference to (i) the binding pathway of sodium to its crystallographic allosteric site, (ii) the dynamics of ligand-receptor and receptor-receptor interactions, both at the ligand- and G protein-binding sites, (iii) the binding pathway and binding pose of novel ligands, and (iv) opioid receptor oligomerization.

LINKED ARTICLES

This article is part of a themed section on Emerging Areas of Opioid Pharmacology. To view the other articles in this section visit http://onlinelibrary.wiley.com/doi/10.1111/bph.v175.14/issuetoc.

Homology-based Modeling of Rhodopsin-like Family Members in the Inactive State: Structural Analysis and Deduction of Tips for Modeling and Optimization

Modeling G-Protein Coupled Receptors (GPCRs) is an emergent field of research, since utility of high-quality models in receptor structure-based strategies might facilitate the discovery of interesting drug candidates. The findings from a quantitative analysis of eighteen resolved structures of rhodopsin family "A" receptors crystallized with antagonists and 153 pairs of structures are described. A strategy termed endeca-amino acids fragmentation was used to analyze the structures models aiming to detect the relationship between sequence identity and Root Mean Square Deviation (RMSD) at each trans-membrane-domain. Moreover, we have applied the leave-one-out strategy to study the shiftiness likelihood of the helices. The type of correlation between sequence identity and RMSD was studied using the aforementioned set receptors as representatives of membrane proteins and 98 serine proteases with 4753 pairs of structures as representatives of globular proteins. Data analysis using fragmentation strategy revealed that there is some extent of correlation between sequence identity and global RMSD of 11AA width windows. However, spatial conservation is not always close to the endoplasmic side as was reported before. A comparative study with globular proteins shows that GPCRs have higher standard deviation and higher slope in the graph with correlation between sequence identity and RMSD. The extracted information disclosed in this paper could be incorporated in the modeling protocols while using technique for model optimization and refinement.

Applications of computer-aided approaches in the development of hepatitis C antiviral agents

INTRODUCTION

Hepatitis C virus (HCV) is a global health problem that causes several chronic life-threatening liver diseases. The numbers of people affected by HCV are rising annually. Since 2011, the FDA has approved several anti-HCV drugs; while many other promising HCV drugs are currently in late clinical trials. Areas covered: This review discusses the applications of different computational approaches in HCV drug design. Expert opinion: Molecular docking and virtual screening approaches have emerged as a low-cost tool to screen large databases and identify potential small-molecule hits against HCV targets. Ligand-based approaches are useful for filtering-out compounds with rich physicochemical properties to inhibit HCV targets. Molecular dynamics (MD) remains a useful tool in optimizing the ligand-protein complexes and understand the ligand binding modes and drug resistance mechanisms in HCV. Despite their varied roles, the application of in-silico approaches in HCV drug design is still in its infancy. A more mature application should aim at modelling the whole HCV replicon in its active form and help to identify new effective druggable sites within the replicon system. With more technological advancements, the roles of computer-aided methods are only going to increase several folds in the development of next-generation HCV drugs.

Molecular Modelling Approaches for the Analysis of Histamine Receptors and Their Interaction with Ligands

Several experimental techniques to analyse histamine receptors are available, e.g. pharmacological characterisation of known or new compounds by different types of assays or mutagenesis studies. To obtain insights into the histamine receptors on a molecular and structural level, crystal structures have to be determined and molecular modelling studies have to be performed. It is widely accepted to generate homology models of the receptor of interest based on an appropriate crystal structure as a template and to refine the resulting models by molecular dynamic simulations. A lot of modelling techniques, e.g. docking, QSAR or interaction fingerprint methods, are used to predict binding modes of ligands and pharmacological data, e.g. affinity or even efficacy. However, within the last years, molecular dynamic simulations got more and more important: First of all, molecular dynamic simulations are very helpful to refine the binding mode of a ligand to a histamine receptor, obtained by docking studies. Furthermore, with increasing computational performance it got possible to simulate complete binding pathways of ions or ligands from the aqueous extracellular phase into the allosteric or orthosteric binding pocket of histamine receptors.

GPCRs from fusarium graminearum detection, modeling and virtual screening - the search for new routes to control head blight disease

BACKGOUND

Fusarium graminearum (FG) is one of the major cereal infecting pathogens causing high economic losses worldwide and resulting in adverse effects on human and animal health. Therefore, the development of new fungicides against FG is an important issue to reduce cereal infection and economic impact. In the strategy for developing new fungicides, a critical step is the identification of new targets against which innovative chemicals weapons can be designed. As several G-protein coupled receptors (GPCRs) are implicated in signaling pathways critical for the fungi development and survival, such proteins could be valuable efficient targets to reduce Fusarium growth and therefore to prevent food contamination.

RESULTS

In this study, GPCRs were predicted in the FG proteome using a manually curated pipeline dedicated to the identification of GPCRs. Based on several successive filters, the most appropriate GPCR candidate target for developing new fungicides was selected. Searching for new compounds blocking this particular target requires the knowledge of its 3D-structure. As no experimental X-Ray structure of the selected protein was available, a 3D model was built by homology modeling. The model quality and stability was checked by 100 ns of molecular dynamics simulations. Two stable conformations representative of the conformational families of the protein were extracted from the 100 ns simulation and were used for an ensemble docking campaign. The model quality and stability was checked by 100 ns of molecular dynamics simulations previously to the virtual screening step. The virtual screening step comprised the exploration of a chemical library with 11,000 compounds that were docked to the GPCR model. Among these compounds, we selected the ten top-ranked nontoxic molecules proposed to be experimentally tested to validate the in silico simulation.

CONCLUSIONS

This study provides an integrated process merging genomics, structural bioinformatics and drug design for proposing innovative solutions to a world wide threat to grain producers and consumers.

Membrane-Mediated Oligomerization of G Protein Coupled Receptors and Its Implications for GPCR Function

The dimerization or even oligomerization of G protein coupled receptors (GPCRs) causes ongoing, controversial debates about its functional role and the coupled biophysical, biochemical or biomedical implications. A continously growing number of studies hints to a relation between oligomerization and function of GPCRs and strengthens the assumption that receptor assembly plays a key role in the regulation of protein function. Additionally, progress in the structural analysis of GPCR-G protein and GPCR-ligand interactions allows to distinguish between actively functional and non-signaling complexes. Recent findings further suggest that the surrounding membrane, i.e., its lipid composition may modulate the preferred dimerization interface and as a result the abundance of distinct dimeric conformations. In this review, the association of GPCRs and the role of the membrane in oligomerization will be discussed. An overview of the different reported oligomeric interfaces is provided and their capability for signaling discussed. The currently available data is summarized with regard to the formation of GPCR oligomers, their structures and dependency on the membrane microenvironment as well as the coupling of oligomerization to receptor function.

Impact, determination and prediction of drug-receptor residence times for GPCRs

The residence time of a ligand on a GPCR of interest has become an optimization parameter in many examples in drug design. Long residence times can counterbalance unfavorable pharmacokinetic parameters, contributing to compound safety, and short residence times can be a tool to avoid target related side effects. Unlike the prediction and interpretation of the structure-activity relationship (SAR) of a ligand class on a receptor, the understanding and prediction of the structure-kinetics relationship (SKR) is much more demanding. Experimental and computational approaches are described, which serve to either rationalize SKR or to predict the kinetic parameters such as on-rates and off-rates.

How to Choose the Suitable Template for Homology Modelling of GPCRs: 5-HT7 Receptor as a Test Case

G protein-coupled receptors (GPCRs) are a super-family of membrane proteins that attract great pharmaceutical interest due to their involvement in almost every physiological activity, including extracellular stimuli, neurotransmission, and hormone regulation. Currently, structural information on many GPCRs is mainly obtained by the techniques of computer modelling in general and by homology modelling in particular. Based on a quantitative analysis of eighteen antagonist-bound, resolved structures of rhodopsin family "A" receptors - also used as templates to build 153 homology models - it was concluded that a higher sequence identity between two receptors does not guarantee a lower RMSD between their structures, especially when their pair-wise sequence identity (within trans-membrane domain and/or in binding pocket) lies between 25 % and 40 %. This study suggests that we should consider all template receptors having a sequence identity ≤50 % with the query receptor. In fact, most of the GPCRs, compared to the currently available resolved structures of GPCRs, fall within this range and lack a correlation between structure and sequence. When testing suitability for structure-based drug design, it was found that choosing as a template the most similar resolved protein, based on sequence resemblance only, led to unsound results in many cases. Molecular docking analyses were carried out, and enrichment factors as well as attrition rates were utilized as criteria for assessing suitability for structure-based drug design.

Molecular dynamic study of MlaC protein in Gram-negative bacteria: conformational flexibility, solvent effect and protein-phospholipid binding

The composition of the outer membrane in Gram-negative bacteria is asymmetric, with the lipopolysaccharides found in the outer leaflet and phospholipids in the inner leaflet. The MlaC protein transfers phospholipids from the outer to inner membrane to maintain such lipid asymmetry in the Mla pathway. In this work, we have performed molecular dynamics simulations on apo and phospholipid-bound systems to study the dynamical properties of MlaC. Our simulations show that the phospholipid forms hydrophobic interactions with the protein. Residues surrounding the entrance of the binding site exhibit correlated motions to control the site opening and closing. Lipid binding leads to increase of the binding pocket volume and precludes entry of the water molecules. However, in the absence of the phospholipid, water molecules can freely move in and out of the binding site when the pocket is open. Dehydration occurs when the pocket closes. This study provides dynamic information of the MlaC protein and may facilitate the design of antibiotics against the Mla pathway of Gram-negative bacteria.

pMD-Membrane: A Method for Ligand Binding Site Identification in Membrane-Bound Proteins

Probe-based or mixed solvent molecular dynamics simulation is a useful approach for the identification and characterization of druggable sites in drug targets. However, thus far the method has been applied only to soluble proteins. A major reason for this is the potential effect of the probe molecules on membrane structure. We have developed a technique to overcome this limitation that entails modification of force field parameters to reduce a few pairwise non-bonded interactions between selected atoms of the probe molecules and bilayer lipids. We used the resulting technique, termed pMD-membrane, to identify allosteric ligand binding sites on the G12D and G13D oncogenic mutants of the K-Ras protein bound to a negatively charged lipid bilayer. In addition, we show that differences in probe occupancy can be used to quantify changes in the accessibility of druggable sites due to conformational changes induced by membrane binding or mutation.

GPCR structure, function, drug discovery and crystallography: report from Academia-Industry International Conference (UK Royal Society) Chicheley Hall, 1-2 September 2014

G-protein coupled receptors (GPCRs) are the targets of over half of all prescribed drugs today. The UniProt database has records for about 800 proteins classified as GPCRs, but drugs have only been developed against 50 of these. Thus, there is huge potential in terms of the number of targets for new therapies to be designed. Several breakthroughs in GPCRs biased pharmacology, structural biology, modelling and scoring have resulted in a resurgence of interest in GPCRs as drug targets. Therefore, an international conference, sponsored by the Royal Society, with world-renowned researchers from industry and academia was recently held to discuss recent progress and highlight key areas of future research needed to accelerate GPCR drug discovery. Several key points emerged. Firstly, structures for all three major classes of GPCRs have now been solved and there is increasing coverage across the GPCR phylogenetic tree. This is likely to be substantially enhanced with data from x-ray free electron sources as they move beyond proof of concept. Secondly, the concept of biased signalling or functional selectivity is likely to be prevalent in many GPCRs, and this presents exciting new opportunities for selectivity and the control of side effects, especially when combined with increasing data regarding allosteric modulation. Thirdly, there will almost certainly be some GPCRs that will remain difficult targets because they exhibit complex ligand dependencies and have many metastable states rendering them difficult to resolve by crystallographic methods. Subtle effects within the packing of the transmembrane helices are likely to mask and contribute to this aspect, which may play a role in species dependent behaviour. This is particularly important because it has ramifications for how we interpret pre-clinical data. In summary, collaborative efforts between industry and academia have delivered significant progress in terms of structure and understanding of GPCRs and will be essential for resolving problems associated with the more difficult targets in the future.