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Biological Insights of the Dopaminergic Stabilizer ACR16 at the Binding Pocket of Dopamine D2 Receptor Ramin Ekhteiari Salmas, Philip Seeman, Busecan Aksoydan, Matthias Stein, Mine Yurtsever, and Serdar Durdagi ACS Chem. Neurosci., Just Accepted Manuscript • DOI: 10.1021/acschemneuro.6b00396 • Publication Date (Web): 21 Dec 2016 Downloaded from http://pubs.acs.org on December 22, 2016

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ACS Chemical Neuroscience

Biological Insights of the Dopaminergic Stabilizer ACR16 at the Binding Pocket of Dopamine D2 Receptor Ramin Ekhteiari Salmas1,*, Philip Seeman2, Busecan Aksoydan1, Matthias Stein3, Mine Yurtsever4, Serdar Durdagi1,*

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Department of Biophysics, School of Medicine, Bahcesehir University, Istanbul, Turkey Departments of Pharmacology and Psychiatry, University of Toronto, 260 Heath Street West, Unit 605, M5P 3L6, Toronto, Ontario, Canada 3 Max-Planck Institute for Dynamics of Complex Technical System, Molecular Simulations and Design Group, Sandtorstrasse 1, 39106, Magdeburg, Germany 4 Department of Chemistry, Istanbul Technical University, Istanbul, Turkey

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*Corresponding Authors: Ramin Ekhteiari Salmas, Ph.D. ([email protected]) Serdar Durdagi, Ph.D. ([email protected])

1 ACS Paragon Plus Environment

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ABSTRACT The dopamine D2 receptor (D2R) plays an important part in the human central nervous system and is considered to be a focal target of antipsychotic agents. It is structurally modeled in active and inactive states, in which homo-dimerization reaction of the D2R monomers is also applied. The ASP2314 (also known as ACR16) ligand, a D2R stabilizer, is used in tests to evaluate how dimerization and conformational changes may alter the ligand binding space and to provide information on alterations in inhibitory mechanisms upon activation. The administration of the D2R agonist ligand ACR16 [3H](+)-4-propyl-3,4,4a,5,6,10b-hexahydro-2H-naphtho[1,2-b][1,4]oxazin-9ol ((+)PHNO) revealed Ki values of 32 nM for the D2highR and 52 µM for the D2LowR. The calculated binding affinities of ACR16 with post processing molecular dynamics (MD) simulations analyses using MM/PBSA for the monomeric and homodimeric forms of the D2highR were -9.46 kcal/mol and -8.39 kcal/mol, respectively. The data suggests that the dimerization of the D2R leads negative cooperativity for ACR16 binding. The dimerization reaction of the D2highR released a free energy of -22.95 kcal/mol, which is energetically favorable. The dimerization reaction structurally and thermodynamically stabilizes the D2highR conformation, which may be due to the intermolecular forces formed between the TM4 of each monomer, and the result strongly demonstrates the dimerization essential for activation of the D2R. Keywords: Antipsychotics; Schizophrenia; Dopamine D2 Receptor; ACR16; Molecular Modeling; MM/PBSA; Molecular Docking; Molecular Dynamics (MD) Simulations


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INTRODUCTION G-protein coupled receptors (GPCRs) constitute the largest and most diverse membrane-bound proteins in eukaryotes, which expose snake-like models into the lipid membrane bilayers [1]. These cell surface receptors are attractively targeted by more than 30% of currently approved drugs [2-3]. The remarkable role that GPCRs have played in numerous human diseases has motivated researchers in academia and industry to elucidate the structural and dynamic profiles as well as mechanism of action of ligands at the binding sites of GPCRs [3-4]. These receptor proteins have the ability to receive biological signals via different ways such as photon energy, peptides, lipids, sugars, and hormones [3,5]. More than 800 human GPCR genomes have been encoded and classified into five main groups: Rhodopsin, secretin and adhesion, glutamate, and frizzled receptor 2. Furthermore, these 5 families were subdivided into subfamilies based on their sequence homology [6]. The largest family is the Rhodopsin-like family (class A), which consists of around 85% of the human GPCR genomes [7]. Despite the diverse amino acid sequences of GPCRs, these proteins share a common topology and signal transferring function [8-9]. Elucidating the tertiary structure of GPCRs using experimental techniques (i.e., X-ray crystallography, Nuclear Magnetic Resonance (NMR), and electron microscopy) is not always straightforward [10]. So far, there are limited numbers of agonist-bound crystal structures of GPCRs available in the protein data bank (PDB) and one of the main agonist-bound conformations is attributed to the β2 adrenergic receptor [11]. This fully-activated conformer significantly enhances the understanding of the activation mechanism in the design of novel GPCR agonists [11]. The current study is divided into different investigations of the dopamine D2 receptor (D2R), which is member of the GPCR family, and comprises seven-transmembrane (TM) spanning domains [12,13]. Historically, dopamine receptors, including D1- and D2-like families, perform various functions that are essential to vertebrate central nervous systems (CNS) and are the major target of antipsychotic


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drugs [14-19]. Growing evidence has indicated that all antipsychotic drugs occupy 60 – 80% of the D2R [15,20]. An initial study, which was based on the synthesis of antihistamines by H. Laborit, played a pivotal role in the enhancement of surgical analgesia [15]. Labeling of haloperidol by a radioactive compound is another earlier study (in 1975) that was focused on the detection of the binding pocket of the D2R [15]. It is beyond the scope of the current study to provide detailed overview of such findings. Numerous experimental and in silico approaches were used to investigate this receptor, which has led to a better understanding of its function and in particular the activation mechanism of the binding site [21-23]. The antipsychotic drug-designing field gains more interest together with the developments and enhanced knowledge in recent years in the X-ray structural techniques. The 3D structural information of many GPCRs is now readily available on the protein data bank (PDB) server [10]. Since dopamine receptors (DRs) are not water-soluble proteins, like other GPCRs, the solving of their tertiary structures is not easy in the membrane-bound domains [24]. Due to the challenges in obtaining highresolution crystal structures, it has been a challenge and indeed very difficult to achieve 3D structures of DRs [24]. In fact, there was not any crystal structure information about DRs until the end of 2009. After many painstaking efforts, Chien et al. [25] pioneered to solve the antagonist-bond 3D structure of the human Dopamine D3 Receptor (D3R) (PDB entry, 3PBL) in an inactive state (D3LowR), which determines major key residues of the binding cavity and extracellular (EC) loops. The D3R was found to have a high amino acid sequence identity score with the D2R target structure within the transmembrane (TM) domains (78%) and particularly in the ligand-binding cavity [10]. Furthermore, the D3R crystal structure can provide detailed information about the D2LowR structure, in which most widely used comparative models (also known as homology modeling) can be used to build the 3D models of the protein. Promising computational studies that utilized predicted 3D models of the D2R have provided necessary information about the chemical behavior of antipsychotic agents within the catalytic


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domain as well as on D2R function. Our recent published studies of the D2R structure-function relationship studies were carried out using molecular modeling approaches, which describe the mechanism and binding affinities of marketed antipsychotics into the active sites of the D2highR and D2LowR [21,22]. Using both theoretical and experimental measurements of the binding affinities of the dopamine and apomorphine, significant information about changes of binding cavity properties and negative cooperation upon dimerization reaction were observed [22]. Herein, we attempted to use pridopidine (Huntexil, formerly ACR16), which is widely used to treat Schizophrenia and considered as a potential D2R stabilizer [26-29]. The intra- and inter-molecular interactions of this ligand, formed within both varied states of the D2highR and D2lowR, were studied by experimental and computational approaches, including energetic and molecular mechanism analysis. The performed simulations and energy calculations led to improved understanding, quantitative analysis, and clarification of effects of dimerization of D2Rs and their negative cooperativity upon ligand binding. RESULTS and DISCUSSION Homology modeling methods are mainly used approaches to predict 3D structural models for target structures that are not solved yet. In these approaches, crystallographic knowledge of solved proteins can be used as templates from the same protein family. Topological similarities between known and unknown proteins are considered to play a pivotal role in homology modeling. Herein, D2highR and D2lowR models were predicted to provide information about the binding mechanism of the dopaminergic stabilizer ACR16 into the binding pockets of monomer and dimer D2Rs, in which multiple-molecular docking, MM-PBSA energetic assays and conformational analyses are implemented. Comparative Modeling and MD Simulations 3D structure of β2AR from GPCR family is considered as a known template for homology modeling. The sequence alignment suggested that the template protein is sharing sequence identities of 35%, 41% and 57% over the entire structure, the trans-membrane (TM) domains and at the binding pocket


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of the D2R, respectively. Despite the striking similarity between the D2R and the β2AR, there are still some alignment gaps in the loop domains particularly in the ECL2. These alignment gaps are handled by energy- and random-based algorithms implemented in the ROSETTA algorithm. The details of the procedures were described in our previous work [21]. The output is the coordinate files of models and their corresponding ROSETTA energy values. Finally, the 3D model that has the lowest energy is tested to ensure that it confirms to a variety of rules about standard GPCR topology that are obtained from analyses of crystal structures. The predicted D2R models, which are sharing a monomeric conformer are used to construct the homodimeric structures. As noted in the introduction section, the D2R tends to be in the form of a dimer and the dimerization can be considered to be an important and also not very well understood phenomenon in the proper physiological functions of these target proteins. The investigation of D2R dimer structures is also assisted to shed light on some principle questions of the dimerization reactions. The key point in the D2R or other GPCRs dimerization is the identification of a common TM, of which the monomers can be coupled to each other. Guo et al. [30] and Lee et al. [31] were among the first research groups to study the dimerization event of the D2Rs. An important conclusion from these works is that the critical role of TM4 in stabilization of the dimer structure. Herein, we attempted to construct the 3D model of the D2R dimer which is satisfactorily in agreement with this experimental information. This is carried out by gradually refining the initial dimeric conformer, which provides the proper correlation between our model and rhodopsin oligomer structures. Analysis of determined representative dimeric form of D2highR was shown in Figure 1 and 2. It is found that the TM4 and TM5 domains from both monomers play a major role in dimer stabilization. The amino acids, participating in the interactions between the two chains were identified, in which Ala127 and Asp131 from chain A engaged in hydrogen bonding contacts with Arg150 in chain B with a vdW complementary of 0.84 and 0.70, respectively. Other hydrogen bonding interactions were formed between “Ser147 (A) and Arg145 (B)” and “Thr165 (A) and Tyr192 (B)”. In addition, Tyr192 (A) established a hydrogen bond with Tyr192 from chain B. The IFD protocol was implemented to predict the initial conformer of the inter-


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molecular complex established between the ACR16 ligand and the D2Rs. Relatively long (100 ns) atomistic MD simulations are implemented to mimic the real structural and dynamic profiles for the dimer and monomer D2Rs in complex with ACR16 ligand that is merged with the membrane bilayers (i.e., the environment plays a critical role upon the interaction, by enhancing or decreasing the binding affinity). Figure 3, which corresponds to the protein-ligand interaction analysis of the ACR16 within the D2highR binding pockets of all possible conformers, showed the crucial amino acids with their occupation levels tightly binding to the ligand along the MD simulations. Asp114 in the active sites of the D2highR monomer and dimer is highly pronounced via the formation of polar interactions. It seems that the dimeric state interaction of Asp114 is more stable than that in the monomeric form. (Figure 4) Phe389 and Phe390 in the monomer produced their effect by establishing hydrophobic and π-π stacking interactions, while His393 of the dimer plays a central role in the formation of a hydrophobic pocket. It is also important to mention that Phe110, Phe389, and Phe390 contribute to interactions with the ligand and are crucial aromatic amino acids in the catalytic domain. Figure 5 represents the ligand-interaction diagram of the ACR16 within the binding domains of the D2lowR in monomeric and dimeric forms. The interaction occupancies of the active site amino acids that are involved in ligand interactions are calculated through MD simulations. Interestingly, the Asp114 could not produce an elevated effect in binding with the ligand in the dimer of the D2lowR, it may be due to a key change in the binding pocket along the conformational transition. Tyr408, which is located in the TM7, forms hydrogen-bonding and π-π stacking interactions with the ligand. His393 which stacks with the aromatic ring demonstrates the highest occupancy level along the simulations. The Thr112 formed hydrogen bonds with the sulfur dioxide group from its backbone atoms making this residue a key residue in the TM3. The most stable interactions that contributed to the His393 are hydrophobic and polar connections with the aromatic ring and polar nitrogen. The Asp114 in D2lowR in monomer form undergoes a water mediated interaction with the polar group of the ACR16,


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demonstrates the main role of the water molecules in forming of the hydrogen bonds into the catalytic domain. Thermodynamic Stability of Homodimerization Free energy is considered to be a major quantity in thermodynamic concepts [32]. This quantity is mainly represented as Gibbs free energy. All the trajectory frames, derived from the MD simulations were incorporated to the widely used MM-PBSA calculations. The key subcomponents of free energy were considered for dimerization of the D2R, as shown in Table 1. All the energetic terms of dimer formation were calculated with errors of less than 2.00 kcal/mol. The free energy difference of dimer and monomer forms into the hydrophobic environment were determined as -22.95 kcal/mol and were in very good agreement with the experiment. The free energy is partitioned into individual terms from electrostatic and vdW interactions. The predicted electrostatic (ΔEelec = 1722.14 ± 1.99 kcal/mol) and vdW (ΔEvdw = -115.97 ± 0.52 kcal/mol) free energy values suggested that the great contribution to change the negative free energy of dimer formation was due to the vdW interactions rather than the Coulombic forces. Unfavorable electrostatic contribution is associated with the low number of hydrogen bond interactions formed between the monomers. Solvation energy change required to form the dimer is calculated as -1650.59 ± 2.23 kcal/mol. Following this observation, solvation energy reveals much greater participation in forming of the complex rather than the other components. The solvation energy in turn is subdivided into polar and non-polar solvation terms. These terms are also important for charged interactions between the solvent and solute atoms. The other key term to note at this stage is the entropy change of the system, which is estimated to be a negative value. In consequence, the formation of the dimer had an unfavorable entropy (-TDS), which can be associated to the tight hydrophobic interactions between the chains A and B. The major role in the conformational transition of the system may lead to less rapid structural changes. It is also possible to routinely obtain the enthalpy energy change of dimer formation from the sum of the solvation and Molecular Mechanics (MM) energy components. It was found that the dimerization was favored by


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-44.42 ± 0.94 kcal/mol, in which the solvation energy had the greatest contribution to change the negative enthalpy of formation. Thermodynamics of the ACR16 into the Dimeric and Monomeric D2R Binding Pockets Free energy is a difficult term to determine for compounds such as ligands or flexible systems, which shares many low-energy conformers. MD or Monte Carlo (MC) simulations may not sufficiently sample different conformers that have major contribution to free energy difference. Thus, it may be considered to be a drawback for these kinds of simulations. In this section, we attempted to consider the thermodynamic properties of the ACR16 into the binding pockets of the D2R monomer and dimer forms. The comparison helps us to understand the effect of the homodimerization. The energetic results are summarized in Table 2. Free energy differences, required to form ACR16-monomeric and -homodimeric D2highR are determined as -9.46 kcal/mol and -8.39 kcal/mol, respectively. ACR16 revealed more elevated binding energy when associated with D2R monomers than when it was associated with D2R dimer. The results were consistent with the experiments (Figure 6) and our previous published study [22], by which it is included that the negative cooperative interactions of the ligand at dopamine D2 receptors are consistent with the D2R homodimerization mode. Both complexes comprise entropic penalties, which may be due to the explicit salt bridges and hydrogen bond interactions, formed into the binding pockets. Energy partition to individual components demonstrates that vdW and the electrostatic terms of ΔEMM together behave favorably toward binding for both systems. The ΔEvdw component that was calculated as -37.66 kcal/mol and -45.37 kcal/mol for the D2highR monomer and homodimer complexes, respectively, made a significant contribution to the formation of intra- and intermolecular interactions in the systems. One term of interest in terms of ligand binding is solvation free energy (ΔGsol) that can be used to determine the free energy change to transfer the solute from solvent to cavity. Binding of ACR16 is associated with solvation energy penalties of 43.20 kcal/mol and 22.72 kcal/mol in monomeric and dimeric D2highR states, respectively, in which the polar term is considered to have the greatest contribution rather than nonpolar interaction in both cases. The values of the total electrostatic interactions calculated for the


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monomer D2highR form (ΔGelec(tot) = 30.84 kcal/mol) is reasonably close to the total electrostatic energy of the homodimer complex (ΔGelec(tot) = 28.93 kcal/mol). Formations of the monomeric and dimeric ACR16-D2lowR complexes are given the free energy of binding of -6.93 kcal/mol and -6.16 kcal/mol, respectively, which include entropic and solvation energy penalties. Competition of ACR16 with [3H](+)PHNO The results in Figure 6 demonstrated that the same range of ACR16 concentrations blocking the binding of [3H](+)PHNO (rat striata) with Ki values of 32 nM and 52 µM for the D2highR and D2lowR, respectively. It is found that ACR16 may be reasonably selective for the D2highR [27]. Docking Simulations –Consistent of Docking Results In addition to MM-PBSA calculations, enthalpy calculations of the ACR16 are also predicted by molecular docking simulations. For this aim, representative structures (i.e., the structure that has the lowest RMSD to the average structure) from 100 ns MD simulations (2000 trajectory frames) were used as target protein structures in docking simulations and Glide/IFD, GOLD and QPLD docking approaches were applied. Figure 7 represents docking scores (GOLD/ChemScore) during docking simulations which derived 1000 poses for each active and inactive monomer and dimer D2Rs. The results were consistent with all three different docking protocols as well as with the enthalpy terms of MM-PBSA calculations and express the key-role of the entropic term in thermodynamic concept. (Table 3) Docking results are also fit very well with the experimental observation that active form of D2R has higher docking score compared to inactive form for all three docking protocols. Mechanism of the ACR16 in the D2R The ACR16 can produce its effect by interacting with the active site amino acids of the D2R. The non-bonded interactions between the ligand and protein are considered to be a major factor in shaping of the binding pocket. This compound reveals a significant shape complementary with the domain of the D2R, where it connects, as shown in Figure 3. Representative 3D position of the D2highR in complex with ACR16 is illustrated, in which the electrostatic potential map around the ligand, main amino acids of the binding cavity and the conserved hydrogen bond by Asp114 are pictured in detail.


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We concentrate to provide information, which enables us to identify the crucial amino acids that contribute in forming of explicit hydrogen bonding and hydrophobic interactions within the complexes, and also to estimate the occupation levels of the interactions along the simulations, as shown in Figures 4 and 5. In the case of the D2highR it is found that, Asp114 (TM3) is the most highly pronounced amino acid among the other active site amino acids via making polar and water-mediated interactions with the ACR16. Phe389 (TM6) and Phe390 (TM6) amino acids from TM6 contributed in forming of π-π stacking interactions with the aromatic ring of the ligand. In addition, to the above amino acids, it can be observed that Ser193 (TM5), Ser197 (TM5) and Tyr408 (TM7) interact with the ligand and form hydrogen bonds to polarize the binding space. Phe393 and Phe110 are the other main amino acids that participated in hydrophobic interaction in the dimeric complex. It seems that, Asp114 was not a conserved residue in the formation of a hydrogen bond with the ligand in D2lowR systems, as shown in Figure 5. This may be due to the gradually decreasing accessible surface area of the binding pocket along the activation, which causes the formation of tight non-bonded contacts. The D2R entirely comprises hydrophobic and polar binding pocket, formed by series of non-polar and polar amino acids, as shown in interaction diagrams. Conformational Analysis Conformational behaviors of macromolecules and small compounds are considered to be an important factor in their biological, physical, and biochemical activities. DHG R Barton (in 1950) [33] developed the advanced conformational analysis, by which the reactivity of substituted cyclohexanes has been investigated. The key term of the conformational analysis, which showed the structural stability of a system along MD simulations, is root-mean-square deviation (RMSD) calculation of atoms in respect to their starting positions. RMSD values of the Cα atoms away from the reference position, which are describing the structural flexibilities of all possible conformers of the D2R along the MD simulations, were measured. The results, shown in Figure 8, not only explore the influences of the ACR16 in the binding pocket, but also monitor the effects of activation and dimerization events on the conformation, that D2R can adopt. The monomer and dimer of the D2highR


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fluctuate with mean RMSD values of 3.08 Å and 2.82 Å, respectively, from which it is found that, the homodimer form is slightly more rigid compared to the monomer form. In the case of the D2lowR, the RMSD values of the monomer and dimer are estimated as 2.79 Å and 3.42 Å, respectively. This shows that the dimer is slightly more flexible compared to the monomer. Furthermore, in order to measure movement and conformational stability of the ligand into the active site, RMSDs of the heavy atoms for the ligand with respect to the protein and the ligand atoms, in a function of time are profiled. As shown in Figure 8, ‘Fit on Protein’ mode that represents the RMSD of the ligand when the complex was first aligned with respect to the Cα atoms demonstrates the diffusion and movement of the ligand into the binding pocket. ‘Fit on Ligand’ mode indicates the RMSD of the ligand when the complex was first aligned on the heavy atoms of the ligand; hence, this result demonstrates the conformational stability of the ligand at the active site. Mean RMSD values, which are describing the conformational changes of the ACR16 into the binding pocket of the different states of the D2R when the systems are fitted upon the ligand conformer, are determined as following: 0.95 Å (D2highR monomer), 0.75 Å (D2highR dimer), 1.06 Å (D2lowR monomer) and 0.86 Å (D2lowR dimer). The most rigid structure corresponded to the ACR16 in complex at the D2highR dimer that reveals the minimum deviation away from the initial structure. The most fluctuating structure was observed for the ACR16 within the D2lowR monomer target. The results were consistent with the experiments. The diffusion level of the ACR16 into the cavity was measured as a function of time by the RMSD calculations and was used to observe the different possible positions that the ligand can occupy along the simulations. The average values are estimated as 2.97 Å (D2highR monomer), 5.00 Å (D2lowR dimer), 6.16 Å (D2lowR monomer), and 1.76 Å (D2highR dimer). Expectedly, the ligand within the D2highR dimer revealed the lowest diffusion, which may be due to the significant intra- and inter-molecular forces formed between the ligand and the active site amino acids because of its high affinity ligand binding. Ligand Dihedral Profile Alternative conformations of small compounds commonly differ in the dihedral angles of their rotatable single bonds [34,35-39]. Some of these conformers have energetic penalty and are not found


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experimentally. It is essential to study the conformational and energetic differences between the conformations to design new compounds that adopt the desired bioactive conformation with minimum energies. In this section, the ligand dihedral analysis was performed which demonstrates the conformational changes of the rotatable bonds at the ACR16 along the MD simulations. Figure 9 illustrates 2D view of the ACR16 with color-coded rotatable bonds, in which the probability densities of the torsions are profiled for the individual rotatable bonds. Each panel of Figure 9 corresponds to one conformer of the D2R: the four rotatable bonds that define the bond of rotation are identified. The results provided with improved understanding of the influence of dimerization and conformational activation of the D2R on the torsion angles in the ACR16. Throughout the profiles, it is observed that there is a decrease in the density of torsional angles of the Dhigh2R upon dimerization around 180° and -90° (second rotatable bond), between 0° and -90° (third rotatable bond) and around 90° (forth rotatable bond). The ACR16 into D2highR monomer reveals an increase in torsion density around -90° (second rotatable bond) in comparison to the D2lowR monomer upon activation. CONCLUSION In summary, 3D structures of Dhigh2R and Dlow2R exposed monomeric and dimeric forms that were modeled based on the full active and inactive crystallographic knowledge of the β2 adrenergic receptor. Using all the models, binding affinity of the ACR16 was determined for Dhigh2R and D2lowR forms. The experimental results reveal that the ACR16 recognized Dhigh2R and D2lowR labeled by [3H](+)PHNO with Ki values of 32 nM and 52µM, respectively. Computational analyses were consistent with experimental results that suggest that dimerization reactions diminish the binding affinity of the ACR16 stabilizer ligand in interacting with the D2R. METHODS Inhibition of [3H](+)PHNO binding to dopamine D2 receptors The potency of ASP2314.HCl on the high-affinity state of D2highR was measured by competition with [3H]domperidone

and

with

[3H](+)-4-propyl-3,4,4a,5,6,10b-hexahydro-2H-naphtho[1,2-

b][1,4]oxazin-9-ol ((+)PHNO) [40,41], using rat striatal tissue and human cloned D2Long receptors.


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Rat brains (Sprague-Dawley; 275–300 g, male, 60–65 days of age, euthanized by a CO2 atmosphere at Rockland Immunochemicals, Gilbertsville, PA; Certified by the U.S. Department of Agriculture, OLANIH-assured [Office of Laboratory Animals, National Institutes of Health, Bethesda, MD]) were used in addition to D2Long clones. The competition data between ASP2314 and [3H]PHNO were analyzed using the GraphPad Prism method (GraphPad Software, La Jolla, CA). The dissociation constant, Ki, was derived utilizing the IC50%/(1 + C/Kd ) calculation [42], where C and Kd represent final molarity and the dissociation constant for [3H]PHNO, respectively. The Kd for [3H]PHNO was 0.72 ± 0.06 nM (n = 22 measurements) for rat striatal homogenate. (For further details, also see [27]). Ligand Setup The 2D structure of the ACR16 was manually sketched and minimized by a force field method (OPLS 2005) [43] in order to obtain the stable state of the structure. The ligand was then assigned at a physiological pH (pH = 7.4) by Epik [44] module implemented in the Schrodinger molecular modeling package. Atomic electrostatic potential (ESP) charges of the atoms were calculated by a semi-empirical method, Austin Model 1 (AM1) [45]. In order to obtain the lowest energy conformation of the ligand, a conformational search method was used, in which all possible conformations were generated by systematic Monte Carlo method. Homology Modeling Monomer: D2highR and D2lowR monomers were predicted using the β2-adrenergic receptor structures as crystallized in full active and inactive forms. The crystal structures of the templates were retrieved from PDB IDs: 3SN6, 3D4S [11,46]. The lysozyme fragment was removed from the system, which was inserted in the location of third intracellular loop (ICL3) along the crystallization and then the domain was artificiality modeled. The D2R sequence was retrieved from UniProtKB database (code: P14416) in FASTA format. In order to align the amino acid sequence of the template and target, the CLUSTALW [47] algorithm was used and 100 initial models were constructed by MODELER 9.14 [48]. The lowest energy structures were then selected for further analysis. Second extracellular loop (ECL2) was modeled without any template-based knowledge. Instead, the Monte Carlo method


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implemented in ROSETTA loop-modeling protocol [49] was used to generate many conformations to be examined by total energy values and experimental information as essential criteria. The superimposition of the active and inactive models illustrates the conformational changes upon activation, as shown in Figure S1 at the supplementary materials. Dimer: Dimer conformers of the D2highR and D2lowR were constructed based on conformations of the rhodopsin oligomer (PDB ID: 1N3M) [50], in which the TM4 in both monomers played a major role in dimerization stability, as suggested by Guo et al. and Lee et al. [30,31]. Both dimer and monomer systems were embedded into the membrane bilayer, and then submitted to 100 ns atomistic molecular dynamics (MD) simulations in order to relax the atomic interactions and remove steric clashes. Molecular Docking The molecular docking simulations were carried out using Induced Fit Docking (IFD) [51] and quantum mechanics-polarized ligand docking (QPLD) approaches implemented into the Schrodinger molecular modeling package as well as GOLD docking method [52]. The IFD method provides flexibility to the binding pocket (the ligand and the amino acids within 6 Å of the ligand are kept flexible during the docking simulations). This step-wise method in IFD consists of the following steps: i) all the ligands were docked into the catalytic domain of the target using Glide/Standard Precision (SP) [53], and then complexes with high docking scores were forwarded to next steps, ii) amino acids of the complexes within 6 Å of the docked ligands were minimized by Prime module of the Maestro; iii) Finally, all the ligands were re-docked into the refined target via Glide/Extra Precision (XP) docking method. In GOLD algorithm, consensus docking protocol was used to generate protein–ligand complexes with GOLD 5.3.0 software. In this respect, two docking scoring functions were combined (GoldScore, for docking; ChemScore, for re-scoring). In the genetic algorithm, the following steps are applied: (i) a population of potential binding poses at a defined binding pocket is set up at random; (ii) each member of the population is encoded as a “chromosome”, which contains information about the mapping of protein−ligand interactions; (iii) each chromosome is assigned a fitness score based on its predicted binding affinity, and the chromosomes within the


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population are ranked according to fitness; and (iv) the population of chromosomes is iteratively optimized. In this study, the following genetic algorithm parameters were used (populations size, 1000; selection pressure, 1.1; number of islands, 5; migrate, 10; mutate, 95; crossover, 95; niche size, 2; and number of operations, 107000). Default cutoff values of 2.5 Å (dH-X) for hydrogen bonds and 4.0 Å for van der Waals distance were employed. Search efficiency was set to its maximum value (200%) – exploring the search space as wide as possible – in order to increase the reliability of the docking results. In the GOLD docking algorithm, 10 active site residues (Asp114, Cys118, Ser193, Ser194, Ser197, Phe198, His393, Tyr408, Thr412, and Tyr416) are treated as flexible. The defined side chains are rotated in 10° increments and scanned over 360°. When the charge polarization that induced by the active site of the protein environment is considered, quantum mechanics (QM) modeling may give the highest level of docking accuracy. For these reasons, QPLD is also considered which uses ab initio charge calculations. Initially, Glide/extra precision (XP) docking was carried out to generate 5 poses per docked compound. These poses were submitted to QM charge calculations which uses the 6–31G*/LACVP* basis set, B3LYP density functional, and ‘Ultrafine’ SCF accuracy level. Molecular Dynamics (MD) Simulations Both dimeric and monomeric D2Rs were embedded into a 1- palmitoyl-2-oleoyl-sn-glycero-3phosphocholine (POPC) membrane bilayer, and the systems were then solvated in an orthorhombic box with layers of explicit TIP3P water molecules of 15 Å thickness, as show in Figure 1. All the atomistic MD simulations were performed by Desmond code. [54] The interactions between the atoms were calculated by OPLS 2005 force field. The particle-mesh Ewald method [55] was implemented to calculate the long-range electrostatic interactions. A cut-off radius of 12.0 Å was used for van der Waals and Coulombic interactions. Nose−Hoover thermostat [56] and Martyna−Tobias−Klein protocols [57] were used to adjust the temperature and pressure of the systems at 310 K and 1.01325 bar, respectively. Time step was assigned as 2.0 fs for all simulations. All the atoms were minimized for a maximum of 5000 iterations, and a convergence threshold of 1 kcal mol−1 Å−1 was implemented.


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Then, the proteins and water molecules were relaxed using step-wise methods, in which the systems were gradually equilibrated. Finally, 100-ns MD simulations was carried out for each system. Molecular Mechanics Poisson–Boltzmann Surface Area (MM-PBSA) Assay Binding free energy predictions have been proven as a feasible approach for studying living systems, such as protein-protein and protein-ligand interactions [32]. MM−PBSA is a straightforward method that predicts ligand binding free energy in complex with biological macromolecules [58]. Binding free energies are achieved by subtracting the individual free energies of the receptor and ligand from the free energy of the complex structure as given in equation 1: ΔGbind = Gcomplex – (Gprotein + Gligand)

(1)

The free energy of ligand binding can be explained as a composition of enthalpy (H) and entropy (S) terms as shown in equation 2: ΔGbind = ΔH - TΔS

(2)

ΔH from equation 2 can be obtained using: ΔH = ΔEMM + ΔGsol

(3)

ΔEMM describes the molecular mechanics (MM) interaction energy between the protein and the ligand, and ΔGsol is the solvation free energy. ΔEMM is expressed by (4): ΔEMM = ΔEelec + ΔEvdW

(4)

where ΔEelec and ΔEvdW defines electrostatic and van der Waals interaction energies, respectively. ΔGsol is subdivided into: ΔGsol = ΔGPB + ΔGNP

(5)

ΔGPB defines the polar solvation energy calculated by Poisson−Boltzmann (PB) methods using the PBSA module of AmberTools15 [59]. ΔGNP describes non-polar solvation energy which is predicted through estimation of the solvent-accessible surface area (SASA) as shown in equation 6: ΔGNP = γSASA + β

(6)

where the surface tension (γ) and the offset (β) have been set the standard values of 0.005420 kcal mol−1 Å−2 and −1.008 kcal mol−1, respectively. ΔGNP was estimated with the linear combinations of


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pairwise overlaps (LCPO) method [60]. In SASA calculation, a probe radius of 1.4 Å has been assigned for the solvent. MMPBSA.py, [61] a Python script was employed to carry out the calculations. ASSOCIATED CONTENT Supporting Information Superimposition of the active and inactive conformers (Figure S1) AUTHOR INFORMATION Corresponding Authors *E-mail: [email protected]

(R.E.S.)

*E-mail: [email protected]

(S.D.)

Author Contributions R.E.S., P.S. and S.D. participated in research design; R.E.S., P.S., B.A. and S.D. conducted experiments; R.E.S., P.S., M.S., M.Y. and S.D. participated in data analysis and contributed to the writing of the manuscript. Notes The authors declare no competing financial interest. REFERENCES 1. Römpler, H., Stäubert, C., Thor, D., Schulz, A., Hofreiter, M., and Schöneberg, T. (2007) G protein-coupled time travel: evolutionary aspects of GPCR research. Mol. Interv. 7, 17–25. 2. Wise, A., Gearing, K., and Rees, S. (2002) Target validation of G-protein coupled receptors. Drug Discov. Today 7, 235–246. 3. Stevens, R. C., Cherezov, V., Katritch, V., Abagyan, R., Kuhn, P., Rosen, H., and Wüthrich, K. (2013) The GPCR Network: a large-scale collaboration to determine human GPCR structure and function. Nat. Rev. Drug Discov. 12, 25–34. 4. Rajagopal, S., Rajagopal, K., and Lefkowitz, R. J. (2010) Teaching old receptors new tricks: biasing seven-transmembrane receptors. Nat. Rev. Drug Discov. 9, 373–86. 5. Li, J., Li, J., Ning, Y., Ning, Y., Hedley, W., Hedley, W., Saunders, B., Saunders, B., Chen, Y., Chen, Y., Tindill, N., Tindill, N., Hannay, T., Hannay, T., Subramaniam, S., and Subramaniam, S. (2002) The Molecule Pages database. Nature 420, 716–717. 6. Foord, S. M., Bonner, T. O. M. I., Neubig, R. R., Rosser, E. M., Pin, J., Davenport, A. P., Spedding, M., and Harmar, A. J. (2005) International Union of Pharmacology. XLVI. G Protein-Coupled Receptor List. Pharmacol. Rev. 57, 279–288. 7. Joost, P. & Methner, A. (2002) Phylogenetic analysis of 277 human G-protein-coupled receptors as a tool for the prediction of orphan receptor ligands. Genome Biol. 3, RESEARCH0063 .


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TABLES

Table 1. Energetic analysis of D2highR dimerization, as obtained by MM-PBSA calculations. Energy (kcal/mol)

D2highR–D2highR

ΔEvdw

-115.97 ± 0.52

ΔEelec

1722.14 ± 1.99

ΔEMM

1606.16 ± 1.82

ΔGPB

-1638.01 ± 2.25

ΔGNP

-12.58 ± 0.02

ΔGsol

-1650.59 ± 2.23

ΔGelec(tot)a

84.13

ΔH

-44.42 ± 0.94

-TΔS

21.47

ΔGbinding

-22.95


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Table 2. Energetic analysis of the ACR16 in complexes with monomeric and dimeric systems, as obtained by MM-PBSA calculations. Energy (kcal/mol)

D2highR

D2lowR

D2highR–D2highR

D2lowR–D2lowR

ΔEvdw

-37.66 ± 0.24

-32.80 ± 0.21

-45.37 ± 0.43

-35.99 ± 0.20

ΔEelec

-16.05 ± 1.017

-11.50 ± 0.61

2.47 ± 0.63

-19.64 ± 0.59

ΔEMM

-53.71 ± 0.96

-44.30 ± 0.75

-42.90 ± 0.90

-55.64 ± 0.60

ΔGPB

46.89 ± 0.70

31.34 ± 0.39

26.46 ± 0.76

43.84 ± 0.52

ΔGNP

-3.69 ± 0.01

-3.41 ± 0.00

-3.74 ± 0.00

-3.90 ± 0.00

ΔGsol

43.20 ± 0.70

27.92 ± 0.40

22.72 ± 0.76

39.93 ± 0.53

ΔGelec(tot)a

30.84

19.84

28.93

24.20

ΔH

-10.50 ± 0.68

-16.38 ± 0.72

-20.18 ± 0.51

-15.70 ± 0.28

-TΔS

1.04

9.45

11.79

9.54

ΔGbinding

-9.46

-6.93

-8.39

-6.16

Table 3. Docking Scores obtained by 3 different docking programs in (kcal/mol). GOLD

D2highR dimer

-6.11

D2lowR dimer

-5.21

GOLD

D2highR monomer

-5.02

D2lowR monomer

-5.05

IFD

D2highR dimer

-10.20

D2lowR dimer

-6.14

IFD

D2highR monomer

-6.60

D2lowR monomer

-5.82

QPLD

D2highR dimer

-9.43

D2lowR dimer

-6.39

QPLD

D2highR monomer

-6.92

D2lowR monomer

-6.13


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FIGURES

Figure 1. Side and top-views of initially constructed structures of homodimeric D2R embedded into the membrane bilayer, in which protein, ligand, both water and lipid atoms are rendered by cartoon, quick surface and strike models of VMD, respectively.

Figure 2. Homodimeric form of the D2R, in which the TM4 domains are highlighted as linking domains that contribute in formation of the dimeric mode. The major amino acids of the TM4 that participate at interactions between the monomers are explored.


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Figure 3. Representative conformer of the D2highR in complex with the ACR16, in which surface areas of the D2R (left) and details of the binding pocket (right) are illustrated. Electrostatic map around the ligand and the key residues participated in important interactions are pictured.

Figure 4. Protein-ligand interaction analysis of the ACR16 within the monomer (upper panel) and dimer (lower panel) of the D2highR binding pockets, in which the crucial amino acids, tightly binding to the ligand, with their occupation levels are identified along the 100 MD simulations.


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Figure 5. Ligand-interaction diagrams of the D2lowR in both dimeric (upper panel) and monomeric (lower panel) configurations.

Figure 6. Competition between ACR16 and 2 nM [3H](+)PHNO for D2 receptors in rat striatal homogenate. [27] 27 ACS Paragon Plus Environment

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Figure 7. GOLD docking scores of the monomeric and dimeric D2highR (left), and monomeric and dimeric D2lowR (right) in complexes with ACR16. (X-axis shows docking pose number and y-axis shows ChemScore docking scores; average values: -6.11 kcal/mol, D2highR dimer; -5.02 kcal/mol D2highR monomer;-5.21 kcal/mol, D2lowR dimer; -5.05 kcal/mol D2lowR monomer).

Figure 8. RMSDs of the Cα atoms of the D2R in three different configurations with respect to the starting positions. M, D, A, I, LF and PF stand for monomer, dimer, active, inactive, ligand fit and protein fit, respectively.


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Figure 9. Ligand torsion profile of the ACR16 into the binding pockets of different D2R conformers (monomeric (a) and dimeric D2highR (b); and monomeric (c) and dimeric D2lowR (d)), (the conformational change of the rotatable bonds of the ligand are highlighted).


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(top) Homodimeric form of the D2R, in which the TM4 domains are highlighted as linking domains that contribute in formation of the dimeric mode. The major amino acids of the TM4 that participate at interactions between the monomers are explored. (bottom) GOLD docking scores of the monomeric and dimeric D2highR (left), and monomeric and dimeric D2lowR (rigth) in complexes with ACR16.

153x83mm (300 x 300 DPI)

ACS Paragon Plus Environment

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Biological Insights of the Dopaminergic Stabilizer ... - ACS Publications

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