Insights into the human A1 adenosine receptor from molecular

Insights into the human A1 adenosine receptor from molecular dynamics simulation: structural study in the presence of lipid membrane Mahboubeh Mansourian, Karim Mahnam, Armin Madadkar-Sobhani, Afshin Fassihi & Lotfollah Saghaie Medicinal Chemistry Research ISSN 1054-2523 Med Chem Res DOI 10.1007/s00044-015-1409-6

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Author's personal copy MEDICINAL CHEMISTRY RESEARCH

Med Chem Res DOI 10.1007/s00044-015-1409-6

ORIGINAL RESEARCH

Insights into the human A1 adenosine receptor from molecular dynamics simulation: structural study in the presence of lipid membrane Mahboubeh Mansourian1,2 • Karim Mahnam3 • Armin Madadkar-Sobhani4,5 Afshin Fassihi2,6 • Lotfollah Saghaie2,6

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Received: 18 August 2014 / Accepted: 9 July 2015 Ó Springer Science+Business Media New York 2015

Abstract Homology modeling, molecular docking and molecular dynamics (MD) simulation methods were used to build a reliable model for A1AR (as one of the G protein-coupled receptors—GPCRs) and to explore the structural features and binding mechanism of ligands to this receptor. A model of A1AR was built and inserted in a hydrated lipid bilayer, and 20-ns MD simulation was performed to examine the stability of the best model. In this study, RG-14718 as the best A1AR agonist and bamifylline as a selective antagonist of A1AR have been docked into the active site of the A1AR. After docking, two 20-ns MD simulation was performed on the A1AR– ligand complex to explore effects of the presence of lipid

Electronic supplementary material The online version of this article (doi:10.1007/s00044-015-1409-6) contains supplementary material, which is available to authorized users.

membrane in the vicinity of the A1AR–ligand complex. At the end of the MD simulation, a change in the position and orientation of the ligand in the binding site was observed. This important observation indicated that the application of MD simulation after docking of ligands is useful. Thr270, His278 and Asn70 were crucial residues for hydrogen bonds with these ligands. Phe171, Glu172, Tyr271 and Ile274 were determined to be involved in ligand–receptor binding. The results obtained are in good agreement with most of the site-directed mutagenesis data reported by others. Our results show that molecular modeling and rational drug design for adenosine targets is a possible approach. Keywords Homology modeling Molecular docking Molecular dynamics simulation A1AR GPCR

& Mahboubeh Mansourian [email protected]

Introduction

& Karim Mahnam [email protected]

Adenosine exerts all of its effects through the interaction with adenosine receptors (ARs) which are members of the superfamily of G protein-coupled receptors (GPCRs). The ARs belong to the subfamily of rhodopsin-like receptors and include four different subtypes, referred to as A1, A2A, A2B and A3 (Londos et al., 1980). The A1 adenosine receptor (A1AR) is functionally coupled to members of the pertussis toxin-sensitive family of G proteins (Gi1, Gi2, Gi3 and G0). When activated, it regulates several membranes and intracellular proteins. Also, it inhibits adenylyl cyclase and Ca2? channels and activates K? channels, phospholipase C and phospholipase D enzymes (Lima et al., 2010). Both the third intracellular loop and the C-terminal tail of A1AR are involved in Gia coupling. It has been reported that under certain conditions, the A1AR couples via Gs to

1

Cellular and Molecular Research Center, Yasuj University of Medical Sciences, Yasuj, Iran

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Department of Medicinal Chemistry, School of Pharmacy and Isfahan Pharmaceutical Sciences Research Center, Isfahan University of Medical Sciences, Isfahan, Iran

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Biology Department, Faculty of Science, Shahrekord University, Shahrekord, Iran

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Life Sciences Department, Barcelona Supercomputing Center (BSC), 08034 Barcelona, Spain

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Department of Bioinformatics, Institute of Biochemistry and Biophysics (IBB), University of Tehran, Tehran, Iran

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Bioinformatics Research Center, Isfahan University of Medical Sciences, Isfahan, Iran

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Author's personal copy Med Chem Res

adenylyl cyclase stimulation or to Gq/11 to stimulate inositol phosphate production (Trincavelli et al., 2010). A1ARs are widely distributed in the central nervous system (CNS); high levels are present in brain, dorsal horn of spinal cord, eye, adrenal gland and atria. Intermediate levels are also found in other brain regions, skeletal muscle, liver, kidney, adipose tissue, salivary glands, esophagus, colon, antrum and testis. Lung and pancreas present low levels of the A1ARs (Trincavelli et al., 2010). Due to this wide tissue distribution, the A1AR mediates a variety of biological effects. Consequently, A1 regulation, through aid of specific and selective ligands, could be of interest for treatment of various central or peripheral disorders. A1AR antagonists could serve as cognitive enhancers, especially for alleviating symptoms of Alzheimer’s disease, whereas A1AR agonists may be useful for the treatment of hypertension, myocardial ischemia, neurodegenerative disorders and peripheral neuropathy (Giordanetto et al., 2003). The members of the GPCR superfamily share major structural and functional similarities. They all consist of seven transmembrane-spanning a-helices (TM1–7) that are connected by alternating intracellular (IL1, IL2 and IL3) and extracellular loop domains (EL1, EL2 and EL3). The orientation of the N and C terminus is also conserved across all GPCRs. The N-terminal tail is exposed to the extracellular environment, and the C-terminal tail is located in the cytosol of the cell and thought to maintain an interaction with cytosolic G proteins. Moreover, two cysteine residues (one in TM3/EL1 interface and one in EL2), which are conserved in almost all GPCRs, form an essential disulfide linkage responsible for the packing and stabilization of a restricted number of conformations of these seven TM domains. Aside from sequence variations, the various GPCRs differ mainly in the length and function of their N-terminal extracellular domain, their C-terminal intracellular domain and their intracellular loops. Each of these domains provides specific properties to these various receptor proteins. However, significant sequence homology is found within several subfamilies. The binding of agonistic ligands to the receptors causes conformational changes in the receptor and activates the G protein. In this way, the receptors transfer extracellular signals to intracellular targets (Lomize et al., 1999). Like most other transmembrane GPCRs, the A1AR crystal structure has not been obtained to date. Several studies have tried to predict the A1AR binding site. For example, Giordanetto et al. (2003) developed a 3D-model of the receptor consistent with the available site-directed mutagenesis data and reported the binding mode of the natural endogenous agonist, adenosine, and of a large set of A1 selective synthetic agonists (Giordanetto et al., 2003). Likewise, Tuccinardi et al. (2006) reported 3D models of

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the A1 and A2AAR constructed by means of a homology procedure, using bovine rhodopsin as a template. The 3D models of the whole structure of known AR subtypes have been made and described by Ivanov et al. (2007). These models have been used for molecular docking of the native agonist, adenosine, and 46 adenosine derivatives (Ivanov et al., 2007). The recently published crystal structure of the human A2A, sharing 51 % identity with human A1, in complex with the high-affinity antagonist ZM-241385, provides a new template for A1 modeling (Jaakola et al., 2008). Another prediction for the 3D structure of ARs was reported by Goddard et al. (2010). They observed the subtype selectivity using the so-called GEn SeMBLE Monte Carlo method. Analysis of the subtype-specific ligand–receptor interactions allowed identification of the major determinants of ligand selectivity by Katritch et al., (2011). Previous experimental studies demonstrate that different binding interactions of ligands to a receptor lead to different receptor conformations. Thus, there appears to be very important to understand: (1) how to construct the reliable/trustworthy 3D model of A1AR and (2) how to identify the binding sits of these ligands in complex with A1AR. RG-14718 and bamifylline have been previously studied as the best A1AR agonist and a selective A1AR antagonist, respectively (Giordanetto et al., 2003; Tomai et al., 1996). Here, we are going further to develop our knowledge about the binding of RG-14718 and bamifylline, in a manner similar to the previous work of our group on CCR5 (Shahlaei et al., 2011a, b). Bamifylline, with the IUPAC name of 8-benzyl-7-[2-[ethyl (2-hydroxyethyl) amino] ethyl]-1,3-dimethylpurine-2,6-dione, is a drug belonging to the xanthine chemical class and is a selective A1AR antagonist (Fig. 1) (Tomai et al., 1996). Substitution at adenosine N6 position in N6-substituted adenine derivatives with 1-(3-chloro-thiophen-2-ylmethyl)propyl gives the best A1AR agonist, RG-14718 (Fig. 1), with a binding constant of 0.0057 nM (Giordanetto et al., 2003). The detailed binding mechanism of RG-14718 and bamifylline with preparing structural characterization has not been studied in the literature. This encouraged us to perform a molecular modeling study on A1AR through investigating the mechanism of action of these ligands. A common deficiency shared by previously reported studies on A1AR structure/function is that the recently published crystal structure of the human A2A AR and/or the influence of lipid bilayer on its folding have not been considered by the researchers. The main goal of this study was to consider the presence of lipid bilayer on dynamical behavior of A1AR model developed. The corollary and/or additional aims of the present study were (1) to predict binding site and the type of interaction

Author's personal copy Med Chem Res Fig. 1 Molecular structures of selective A1 agonist RG-14718 (left) and selective A1 antagonist bamifylline (right)

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between A1AR and RG-14718 as the best agonist and also bamifylline as a selective antagonist; (2) to explore possible structural features and the binding mode of some ligands of A1AR by determining residues involved in the ligand recognition; and (3) to obtain a stable and reliable 3D model of A1AR signifying the main structural features of this protein. The model can then be employed to suggest more selective and also more potent A1AR agonists and antagonists. For these purposes, at first, we have carried out a 20-ns MD simulation of the developed model in lipid biomembrane, and after obtaining a stable model of protein, we scanned the binding site using the docking computational protocol to identify the binding mode of RG-14718 and bamifylline. An extra MD simulation was performed on A1AR in the presence of these ligands in lipid biomembrane.

Methods A1 model building using homology modeling method The primary sequence of the human A1AR was retrieved from UniProt, which contains 326 amino acids (UniProtKB/Swiss Prot: P30542). The crystal structure of ˚ resolution with a high A2AAR (PDB ID: 3EML) at 2.60 A sequence identity (51 %) with A1AR was selected from the protein data bank as a template model for A1AR. Basic local alignment search tool (BLAST) available at NCBI was used to find homologous proteins with known structures (as templates in the process of A1AR homology modeling) (Altschul et al., 1997). A multiple sequence alignment (MSA) was performed between the target and the template from 16 mammalian species using T-COFFEE

(Notredame et al., 2000) to acquire an alignment with higher sequence identity with the target receptor. The sequence alignment derived by MSA was used to generate homology models of A1 (Fig. S1). 3D models containing all non-hydrogen atoms were obtained automatically using MODELLER (9v7) (Sali and Blundell, 1993). Of the 1000 models generated with MODELLER, the one corresponding to the lowest value of the probability density function (pdf) and the fewest restraint violations was selected for the loop refinement stage. An ab initio method implemented in the MODELLER that has been demonstrated to predict the conformations of loop regions was used to refine some of the loops of the selected model. Inspection of the template structure revealed that loop 149–155, one of the functionally most important parts of the A2AAR, is disordered and is not present in the template PDB structure (missing residues). The observed higher conservation in TM regions, however, is responsible for the similar overall folding of the members of GPCRs. Modeling of the extracellular regions of GPCR is challenging because of limited homology with the known structures and of limitations of current loop modeling techniques (Sali and Blundell, 1993). The problematic region in the model of the A1 was EL2 (150–166), which was subjected to ab initio loop modeling procedure (Fisher et al., 2000). A discrete optimized protein energy (DOPE) was used to assess the energy and the quality of the 10,000 models generated (Shen and Sali, 2006). The root mean square deviations (RMSDs) of the models relative to the template were calculated using MODELLER. The stereochemical quality assessment of the final selected model and the Ramachandran plot were accessed by PROCHECK software (Laskowski et al., 1993). Environment profile of the final developed model

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was checked using Verify 3D (Structure Evaluation Server). (http://www.doe.mbi.ucla.edu/verify3d.html) (Luthy et al., 1992). Molecular dynamics simulations The minimum energy conformation of A1AR obtained from protein homology modeling process was employed as starting structure for 20-ns MD simulations. All MD simulations were carried out by GROMACS 4.5.3 package (Van der Spoel et al., 2005) with ffgmx force field at constant temperature and pressure. To mimic the membrane environment, the best model from the homology modeling step was inserted at the center of 340 molecules of the POPE (palmitoyl-oleyl-phosphatidylethanolamine) bilayer with its long axis normal to the membrane–water interface. The a-helices were modulated manually perpendicular to xy plane of the lipid bilayer by VMD 1.8.5 software (Humphrey et al., 1996). The lipid bilayer was described using a previously developed topology file (Tieleman, see http://moose.bio.ucalgary.ca) (Tieleman et al., 2006). Overlapping protein–lipids atoms were removed using ‘‘InflateGro’’ script (Kandt et al., 2007). The embedded A1AR together with the POPE molecules was positioned in the center of a box with the dimensions of 9.6 9 9.5 9 10 nm and solvated using a simple point charge (SPC) water model (Rivail et al., 2007). Overlapping lipid and water molecules were removed. Chloride ions were added into the box to neutralize the system. Also, periodic boundary conditions were used in molecular dynamics. Then, energy minimization was carried out for the system using the steepest descent algorithm. After energy minimization step, equilibration step was done. In this step, heavy atoms were restrained with a force constant of 1000 kJ mol-1 nm-1. Also all bonds were constrained via the LINCS algorithm. Equilibration step has two phases. In the first phase, an NVT ensemble of 1 ns was adopted at constant temperature of 323 K and with coupling scheme of V-rescale as modified Berendsen thermostat and coupling constant of 0.1 ps. In the second phase, an NPT ensemble of 2 ns was performed. In this phase, a constant pressure of 1 bar was employed with a coupling constant of 5 ps (Berendsen et al., 1984). Final step or production phase of MD simulation was performed under NPT ensemble with Nose´–Hoover thermostat, and position restraints were removed in this step. The particle mesh Ewald (PME) method for electrostatic interaction by using a cutoff of 1.2 nm and the LINCS algorithm for covalent bond constraints were used. Time step in this step was set to 2 fs (Darden et al., 1993; Hess et al., 1997). Duration time for production step was 20 ns. The system contained 13,068 water molecules, 234 POPE molecules, A1AR and 12 Cl- ions.

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The calculation of the eigenvectors and eigenvalues was carried out using essential dynamics method according to protocol of Amadei et al. (1993) within the GROMACS software package. Principle component analysis (PCA) was performed using analysis tools of GROMACS package, considering only the Ca atoms for generating the covariance matrix. The covariance matrices, eigenvectors and eigenvalues were constructed using the ‘‘g_covar’’ command. The essential dynamics analysis, projections of structures onto eigenvectors, analysis of eigenvectors for visualizing the main motions in the PCs and the calculation of the inner products between eigenvectors were carried out with the ‘‘g_anaeig’’ command. Docking simulation Molecular docking studies were performed for these ligands to understand the ligand–protein interactions in details. Docking was performed by AutoDock 4.0 software, using the routine procedure and default parameters of Autodock and implemented empirical free energy function and the Lamarckian genetic algorithm (LGA) (Morris et al., 1998). ArgusLab 4.0.1 (http://www.arguslab.com) molecular modeling program was used to create the 3D structure of ligands. Using the MM? molecular mechanic force field, 3D geometry optimization calculations for each ligand were performed until the root mean square gradient ˚ mol was obtained. less than 0.01 kcal/A The final structure obtained from MD simulation used for docking simulation and 200 docking runs was performed. Since the location of the binding site of ligands in the complex has been known by site-directed mutagenesis (Olah et al., 1992; Kim et al., 1995; Gao et al., 2000; Fredholm et al., 2001; Dawson and Wells, 2001; Giordanetto et al., 2003; Gutierrez-de-Teran et al., 2004; Ivanov et al., 2007; Piirainen et al., 2011; Kolb et al., 2012), the grid box was centered on amino nitrogen of His278. ˚ Grid box dimension was 60 9 60 9 60 with a 0.375 A grid point spacing. Grid maps were calculated by Autogrid4. At the end of docking experiment with 200 runs, a cluster analysis was performed. Conformations were clus˚ . LigPlot tered according to the RMSD tolerance of 1.0 A software was used to investigate the hydrophobic and hydrogen bonding interactions between the ligand and protein (Wallace et al., 1995). The binding site of RG14718 was identified as the best A1AR agonist and bamifylline as an antagonist. AutoDock4 computes the free energy of binding to assign the best binding conformation. Among the various conformations of these ligands obtained from the docking procedure, the conformation with the lowest binding free energy to A1AR receptor was selected as the final conformation.


Results and discussion Homology modeling It is commonly accepted that a homology model is not the same as an experimentally determined structure of a given protein. However, it is still very helpful for us to comprehend the binding modes of A1AR and its ligands. This keeps researchers away from clear drawbacks in further design procedure by using the combination of homology modeling and other computational methods. Although homology modeling has been extensively performed in the case of GPCRs, this technique shows several limitations for these receptors, including poor sequence identity between target and template sequences, and the limited number of structural templates (Zare et al., 2011) (presently only six). In spite of the large number of known aqueous protein structures (solid state X-ray structures), structural details of so few membrane proteins are available. One of the most important approaches to overcome this deficiency is the use of computational methods. Experimental 3D structures of some GPCRs can help in modeling studies of these proteins (Jaakola et al., 2008; Cherezov et al., 2007; Okada et al., 2004; Warne et al., 2008; Wu et al., 2010). This emphasizes the need to incorporate available experimental information such as site-directed mutagenesis and structure–activity relationships data along with homology modeling for developing acceptable 3D models of GPCRs. The sequence identity between A2AAR and A1AR is 51 %. It can be concluded that the obtained model possesses a high reliability based on the high sequence identity to A2A crystal structure template (Sali and Blundell, 1993). One of the most important points in modeling the individual backbone of hydrophilic loops is the presence of a disulfide bond between TM3 and EL2, which is highly conserved among all rhodopsin-like receptors (Bockaert and Pin, 1999). The conserved disulfide bond, found in all adenosine receptors, is formed in the extracellular domain of the A1AR between Cys80 (at the top of TM3) and Cys169 (located in EL2). This disulfide bond was made and kept as a constraint in the homology model building. (Jaakola et al., 2008). To study the orientation of A1AR helices, we superimposed the TM regions of the model on the A2A crystal ˚ , constructure. The RMSD of the Ca atoms was 0.36 A firming a high likelihood of having a model near native structure. The quality of model obtained was checked by Procheck. Ramachandran plot showed that more than 93.5 % of the u/w angles of the residues are located in the favored regions (Fig. S2). The ‘‘goodness factor’’ or G-factor indicates the quality of dihedral, covalent and

overall bond angles of the developed model. The overall value of this factor should be above -0.5 for a reliable model, and the best models display values close to zero. The observed G-factors for the present model were 0.16 for dihedrals, -0.18 for covalent and overall 0.03. The overall main chain and side chain parameters, as evaluated by Procheck, are all very favorable. In addition, the average G-factor, the measure of the normality degree of the protein properties, was close to zero (within the allowed values for homology model. The final A1AR model indicates that more than 99 % of residue u/w angles are in the favored or additional allowed regions of Ramachandran plot. With respect to the Ramachandran plot, it is observed that almost all residues are in the allowed region except Ser219 that is located in generously allowed region of the Ramachandran plot. Therefore, according to Procheck software, the optimized model showed relatively good protein geometry, and most of the quality parameters were better than average or in the range of tolerance default (thresholds). The final structure was further evaluated for overall quality by Verify 3D. Verify 3D employs energetic and empirical techniques to generate averaged data points for each residue to evaluate the quality of extended model of protein. The compatibility score above zero in the Verify 3D graph corresponded to acceptable side chain environments. This score measures the compatibility of the generated model with its sequence by means of a scoring function. Using a predictor, prediction of torsion angle restraints for the side chains of the developed A1 model showed confidence and similarity score of above zero for almost all residues (Fig. S3). The model then could have an overall self-consistency in terms of sequence-structure compatibility. According to these evaluation methods, the generated human A1AR model appeared to be in good quality based on common structural criteria. Overall analysis of molecular dynamics simulation MD simulation was performed to study the conformational variations and to determine the stability of the obtained 3D A1AR structure within an explicit lipid environment. Clarification of ligand binding mechanisms is the essential step to introduce more selective and potent lead compounds for a given target protein. To do this, we needed to construct a 3D model of protein and then let it feel its natural environment. MD simulation is one of the best methods for such refinement. An accepted way for performing MD simulations of membrane proteins is the use of a phospholipid bilayer solvated by water molecules (Ivanov et al., 2005). The lipid environment in the membrane can have a serious impact on the protein

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conformation (Ivetac and Sansom, 2008), and it is not easy to obtain this final conformation via crystallography. Hydrophobic interactions between the nonpolar amino acids and the fatty acyl groups of the membrane lipids firmly anchor the protein in the membrane. Our study focused on the biomembrane model of the protein in a preequilibrated native-like phospholipids bilayer environment, comprising A1AR and lipid bilayer at full hydration. With the aim of evaluating the system, stabilization throughout the molecular dynamics time, the potential energy was plotted versus time (Fig. S4-A). The physicochemical parameters such as energy of the system reached a plateau at the desired value after a few hundreds of picoseconds. The temperature of the system reached to plateau at 323 K after 1 ns (Fig. S4-B). The final extracted structure was obtained under stable temperature conditions. The average of potential energy and temperature of the system in the last 15 ns of MD were -923459 ± 10719 kJ mol-1 and 323 ± 1.4 K, respectively. The fluctuations of density during 20 ns of MD simulation are shown in Fig. S4-C. The average of density of lipid bilayer in the last 15 ns of simulation was 1015.171 ± 1.74 kg/m3, and the final density of lipid bilayer at the end of 20 ns was 1018.489 kg/m3. Structural fluctuations The system’s MD equilibration was examined by following the time evolution of structural quantities. The stability and convergences of the simulated system were evaluated by calculating the root mean square deviation (RMSD) of the protein atoms relative to the initial structure, radius of gyration (Rg) of protein, root mean square fluctuation (RMSF) of protein backbone, the number of hydrogen bonds between protein atoms and protein–membrane and distance between protein atoms and POPE atoms, as well as average RMSF of Ca atoms of A1AR projected on the first and second principal components. These measures are discussed in the following sections.

Fig. 2 Time evolution of a backbone RMSD, b radius of gyration of the A1AR along the trajectory of MD simulation. c The RMSF of the protein backbone (in solid black) and the RMSF of the protein backbone plus side chains (in red dashed) for coordinates are shown as a function of residue number (Color figure online)

RMSD RG A comparison of the RMSD of backbone atoms with the initial structure as a function of time is shown in Fig. 2a. After 5-ns simulation, the system reached equilibrium. Maximum fluctuation for the studied structure was on the order of *0.36 nm (for the backbone atoms), which means that to some extent larger conformational changes took place at about last F15 ns. The RMSD value implies that this protein structure has been affected by its environment dramatically. The average RMSD of the backbone atoms during the last 15 ns of MD was 0.33 ± 0.01 nm. In the last 15 ns of simulation, the system was fairly stable and did not show a meaningful change from 0.33 ± 0.01 nm.

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The general alterations in the structure of simulated protein can best be visualized by studying the radius of gyration. The radius of gyration (Rg) is defined as the mass–weight root mean square distance of a collection of atoms from their common center of mass (Kundu and Roy, 2008). Hence, this analysis gives us insight into the overall dimensions of the proteins. Said in other words, the radius of gyration of proteins in the system is a criterion of the compactness of protein arrangement and can be used to measure the protein aggregates during MD simulation (Shahlaei et al., 2011a, b). The plot of radius of gyration of


the protein versus time is shown in Fig. 2b. The starting Rg was 2.24 nm, and after 1 ns, Rg maintained the lowest value around 2.18 nm and remained the same for rest of the simulation, indicating a largely preserved compact conformation at A1AR simulation. This compaction appeared to be a slow process and was attributed generally to the change of the interhelical angle between helices of the developed model. RMSF

conditions for protein simulation. All hydrogen bonds along with the hydrophobic interactions contribute to the stability of the helical conformation. The average number of protein–protein hydrogen bonds was 228 ± 7 at the last 15 ns in A1AR simulation. The number of protein–membrane hydrogen bonds was also 74 ± 6 in the same period of time. In MD simulations, the total number of hydrogen bonds between protein and membrane tended to increase (Fig. 3a). As the MD simulation progressed, more residues came into contact with head groups, leading to the

The total root mean square fluctuation (RMSF) of the protein backbone plus side chains (red dashed) and the RMSF of the protein backbone (N, Ca and C) (solid black) are depicted in Fig. 2c, for the developed geometry of A1AR in lipid bilayer over the last 5 ns of the simulation. The averaging time period, 15–20 ns, was selected based on the observation of the RMSD drift of A1AR during the MD simulation. An analysis of the Fig. 2c reveals that the residues at N-terminal and C-terminal regions have values which are more or less equal to RMSF values. It was found that throughout the dynamic simulations, very few fluctuations exceeded 0.1 nm and even fewer fluctuations over passed 0.15 nm for the total protein. The b-sheets and ahelices of A1AR located in the central region of protein experience rather small fluctuations. This stable behavior of the b-sheets and a-helices of A1AR is attributed to the network of hydrogen bonds stabilizing these secondary structures. Fluctuation analysis on a residue by residue basis showed that the residues Ser216–Gly220 with fluctuations close to 0.16 nm observed in the dynamics plots were located in IL3, which indicates that these regions of protein are more unstable than other regions of protein during MD simulations. Special attention was given to the Ser219 residue in the generously allowed region at PROCHECK plot. RMSFs of the total protein and backbone adopt the maximum value 0.21 and 0.13 nm for Ser219 that is away from active site amino acids. IL3 has large fluctuations relative to other parts of the protein. This feature appears to be inherent for this region, and the fluctuations of this region could be a functional part of the receptor. When a ligand binds to the extracellular part of receptor (extracellular loops), this massage translates to this region via changing in the structure of intracellular loops (IL3). Then, a biochemical reaction will start by this intracellular loop. Intramolecular and intermolecular contacts Hydrogen bonding pattern The average number of intramolecular hydrogen bonds (shorter than 0.35 nm) was calculated at different

Fig. 3 Number of H-bonds formed a between A1AR and lipid biomembrane (intermolecular), b protein–protein (intramolecular) and c time evolution of distances between A1AR and lipid biomembrane along the trajectory of MD simulation

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formation of more hydrogen bonds. Hydrogen bonds between A1AR and head groups of phospholipids are persistent and establish the geometry of the protein in the bilayer. It is also evident from the plot (Fig. 3a, b) that the number of hydrogen bonds was steadily maintained throughout the simulation. The portion of A1AR in the water sub-phase (such as loops) can sample a much larger conformational space than the membrane interfacial region can do. Therefore, once the residues with hydrogen bond donors get close enough to the interface, they form hydrogen bonds with the membrane. Thus, it is possible that the hydrogen bonds limit the distribution of the protein and the conformational space sampling and eventually control the equilibrium distribution of the conformations of the protein–membrane complex. However, it can be concluded that the proteins prefer to be located in the interface since this leads to the most energetically favorable configuration through the formation of hydrogen bonds. Although the above conclusion has been made in relation to the side chains, the protein backbone itself can also form hydrogen bonds with the POPE acceptor head groups. However, it is the side chain–membrane interactions that mainly control the overall protein–membrane interactions. This is due to the higher accessibility and hence higher probability of hydrogen bond formation. Distances between center of mass of A1AR atoms and that of lipid biomembrane for the developed A1AR model in the 20-ns MD simulation were calculated and are shown in Fig. 3c. An analysis of the time-dependent fluctuation of the distances provided in Fig. 3c shows that the distances are fairly stable after 10 ns during the simulation time. It also reveals that distance fluctuations are within the range of *0.1 nm. The average value of compaction of the lipids around the protein at the last 15 ns of A1AR simulation was 0.43 ± 0.05 nm. Principal component analysis (PCA) Essential dynamics (ED) is a method that utilizes principal component analysis (PCA) on the actual coordinates of the system and thus gives essential motions of the protein in the phase space (Amadei et al., 1993). Said another way, the protein moves within the (6 N-dimensional) phase space, essential dynamics focuses on the (3 N-dimensional) conformational space. The ED method is based on the construction of the covariance matrix of the coordinate fluctuations. In this study, the covariance matrix was diagonalized to obtain the eigenvectors and eigenvalues that provide information about the correlated motions throughout the protein. The eigenvectors represent the directions of motion, and the eigenvalues represent the amount of motion along each eigenvector. The

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Fig. 4 RMSFs of Ca atoms projected along PC1 and PC2 of A1AR, respectively

eigenvectors were then sorted according to their eigenvalues in a descending order with 0.59, 0.33 and 0.17 nm2. The fluctuations of Ca atoms of A1AR projected on the first and second principal component are shown in Fig. 4. The average RMSF values of the Ca atoms of A1AR projected on the first and second principal component (RMSF1 and RMSF2) during the last 15-ns MD simulation were 0.04 nm and 0.03 nm, respectively. It means the proteins reached to the stable structure after 5-ns simulation. However, maximum RMSF values of Ca atoms on the first and second principal component were 0.19 nm and 0.11 nm, respectively. The largest conformational changes took place for Met1 at the N-terminal with 0.19 nm, Ser267 at EL3 with 0.16 nm and then for Phe307 at C-terminal with 0.15 nm at RMSF1. Since Met1 and Phe307 residues were in terminal regions, they were more affected by the free terminal residues and exhibited higher RMSF. A special attention was given to the EL3 region that was affected more than the N-terminal region. These kinds of analyses were performed to monitor the differences in the local regions. Binding modes between RG-14718 and bamifylline in A1AR model: Phase I To explore the characteristics and binding modes of A1AR ligands and also to reveal the most possible residues involved in ligand recognition, molecular docking was performed on A1AR binding pocket. Figure 5 depicts a 2D schematic representation of the best possible binding conformation of RG-14718 and bamifylline in the A1AR active site, which was derived from our docking studies after the MD simulation. Among the various conformations of these ligands obtained from the docking procedure, the conformation with the lowest docked energy of binding to A1AR was selected. Also, the results of the best scored pose for


Fig. 5 Schematic representation of the interaction between A1AR and selective A1 agonist RG-14718 (left) and selective A1 antagonist bamifylline (right) produced using the Ligplot software

docked ligands in the A1AR active site are reported in table S2. N6-substituents in N6-substituted adenine derivatives show large conformational freedom. This is reflected by the different interaction modes displayed during docking calculations. According to the results obtained, N6-moiety in N6-substituted adenine derivatives (Fig. 5 and Fig. S6) could be responsible for the correct fit with the following residues. There are hydrophobic interactions between Thr270 and Tyr271 (Kolb et al., 2012; Giordanetto et al., 2003; Dawson and Wells, 2001) (on the TM7) pocket and the ethyl moiety of RG-14718 (Figs. 1, 5). The chlorothienyl part is packed perfectly in Ile274 (on the TM7) (Kolb et al., 2012; Piirainen et al., 2011; Giordanetto et al., 2003; Jaakola et al., 2008; Dawson and Wells, 2001) pocket. The chlorine atom of RG-14718 is located between mentioned residue Ile274 and Phe171 (Kolb et al., 2012; Piirainen et al., 2011; Jaakola et al., 2008). This possibility is taken into account based on the 3D representation of the interactions of the studied ligands prepared by ViewerLite and VMD softwares. The aromatic moiety of RG-14718 (adenine ring) forms p–p interaction with the side chains of the Phe171 (on the EL2) (Kolb et al., 2012; Jaakola et al., 2008; Jiang et al., 1997) and hydrophobic interactions with Tyr271 (Kolb et al., 2012; Giordanetto et al., 2003) and Glu172 (on the EL2) (Kolb et al., 2012; Piirainen et al., 2011; Jaakola et al., 2008; Jiang et al., 1997; Kim et al., 1996). In this case, our results indicate that the ethyl fragment and the

chlorothiophene and adenine rings are responsible for establishing exhaustive van der Waals contacts in N6moiety. As shown in Fig. 5 (left), there is an option for hydrogen bonds formation between the hydroxyl hydrogen atoms in the ribose moiety of RG-14718 and the oxygen atoms of Ile69 (on the TM2) (Xie et al., 2006), Cys169 (Jason Scholl and Wells, 2000), Phe171 (Kolb et al., 2012; Jaakola et al., 2008; Jiang et al., 1997) and Glu172 (on the EL2) (Kolb et al., 2012; Piirainen et al., 2011; Jaakola et al., 2008; Ivanov et al., 2007; Giordanetto et al., 2003; Jiang et al., 1997; Kim et al., 1996) with the bond lengths of 2.78, 2.92, ˚ , respectively. Ile69, Cys169 and Phe171 2.90 and 3.27 A are involved with the substituent at the 40 position of the ribose ring. There is the additional possibility for the formation of another hydrogen bond between the oxygen atom in the ribose moiety of RG-14718 and the nitrogen atom of Phe171 (Kolb et al., 2012; Jaakola et al., 2008; Jiang et al., ˚ . These interactions 1997) with the bond length of 2.75 A ensure that pentose ring is stably anchored in the hydrophobic pocket constructed by residues Asn70 (on the TM2) (Xie et al., 2006) and Glu170 (Kolb et al., 2012; Piirainen et al., 2011; Jaakola et al., 2008; Ivanov et al., 2007) (on the EL2). Thus, RG-14718, as the best agonist fills both TMs 2,7 and EL2 pockets. Said another way, this cooperative mechanism could therefore be a target for new structure-based design projects. As shown in the 2D schematic interaction model of bamifylline and A1AR (Fig. 5, right), carbon atoms of the xanthine moiety are located in the hydrophobic pocket

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formed by Ile13 and Glu16 (on the TM1) (Gao et al., 2000; Fredholm et al., 2001; Ivanov et al., 2007; Piirainen et al., 2011; Barbhaiya et al., 1996), Ala66 (on the TM2), Ser267, Thr270 and Tyr271 (on the TM7) (Kolb et al., 2012; Giordanetto et al., 2003; Dawson and Wells, 2001). Phenyl moiety of bamifylline becomes almost perpendicular to the xanthine ring and is surrounded by hydrophobic cage Val62, Ala66, Ile69 (on the TM2) (Xie et al., 2006), Phe171 (Kolb et al., 2012; Jaakola et al., 2008; Jiang et al., 1997), Glu172 (on the EL2) and Ile274 (on the TM7) (Kolb et al., 2012; Piirainen et al., 2011; Jaakola et al., 2008; Giordanetto et al., 2003, Dawson and Wells, 2001; Kim et al., 1995, 1996) (Based on the 3D representation prepared by ViewerLite and VMD softwares). N-Methyl xanthine moiety shows van der Waals contacts with Ala66, Ser267 and Tyr271 (Kolb et al., 2012). Also, the phenyl ring of bamifylline participates in p–p stacking interactions with Phe171 (Kolb et al., 2012; Jaakola et al., 2008; Jiang et al., 1997). On the other hand, by the same interactions, xanthine ring is involved in ligand–receptor interactions through p–p stacking with the aromatic ring of Tyr271, suggesting that more hydrophobic interactions around this area can improve the antagonistic activity. OH group of the N-hydroxyethyl moiety establishes a hydrogen bond with Glu172 side chain with the bond ˚ (Kolb et al., 2012; Piirainen et al., 2011; length of 2.81 A Jaakola et al., 2008; Ivanov et al., 2007; Giordanetto et al., 2003; Jiang et al., 1997; Kim et al., 1996), whereas nitrogen atom forms another hydrogen bond with the oxygen atom of Thr270 (Kolb et al., 2012; Giordanetto et al., 2003; Dawson and Wells, 2001) with the bond length ˚ . This fragment of bamifylline is located in close of 3.22 A proximity to His278 (Kolb et al., 2012; Giordanetto et al., 2003, Olah et al., 1992; Gao et al., 2000), Ile274 (Kolb et al., 2012; Piirainen et al., 2011; Jaakola et al., 2008; Giordanetto et al., 2003; Dawson and Wells, 2001; Kim et al., 1995) and Phe275 (Kolb et al., 2012; Giordanetto et al., 2003) in the substrate cavity space. These residues are involved in the stabilization of xanthine ring. During the docking analysis, we identified that the effective binding site of A1AR for the studied antagonist is in the upper region of the helical bundle (EL2), surrounded by TMs 1, 2, and 7. Also, mutagenesis experiments was shown that some residues were located in the first (Townsend-Nicholson and Schofield, 1994), secondly (Townsend-Nicholson and Schofield; Xie et al., 2006) and seventh (Olah et al., 1992; Dawson and Wells, 2001; Fredholm et al., 2001; Rivkees et al., 1999). TM helices are important for ligand binding, although the nature of their specific interactions still remains unclear. Most of these residues, especially Glu16, Val87, Glu170, Phe171, Glu172, Thr270, Tyr271, Ile274 and His278, and in the putative binding site are conserved among the four

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adenosine receptor subtypes. A potential binding site of A1AR was verified according to the previous studies of site-directed mutagenesis (Kolb et al., 2012; Piirainen et al., 2011; Ivanov et al., 2007; Giordanetto et al., 2003; Dawson and Wells, 2001; Fredholm et al., 2001; Gao et al., 2000; Rivkees et al., 1999; Jiang et al., 1997; Barbhaiya et al., 1996; Kim et al., 1995, 1996; Townsend-Nicholson and Schofield, 1994; Olah et al., 1992). A1 antagonist XAC (xanthine amine congener) and A1 agonist (R)-PIA (N6-(R-phenylisopropyl) adenosine of the N6-adenine substituted compounds (Fig. S5) appeared to be selective for the human A1AR with respect to the other human ARs. As shown in Fig. S6, the docking results indicate that the above-mentioned ligands occupy an almost similar space in the binding site (almost their interactions resemble the corresponding residues). The binding mode of this series of ligands is conserved. The investigated selected compounds by molecular docking have some common structural features. The A1AR is supposed to detect these structures and stabilizes the ligand binding. This cooperative mechanism could therefore be considered in new structure-based design projects. Molecular dynamics simulation: Phase II To perform an MD simulation development on a protein– ligand complex in a biological membrane system, one usually employs available experimental data and computational running to construct a reasonable structure for the protein–ligand complex already bound to the biological membrane interface or inserted into the biological membrane interior (Woolf and Roux, 1996). Ensuing a suitable equilibration procedure on a given system consisted of a protein–ligand inserted in a biological membrane, a production simulation can be performed from which a researcher obtains information on the structure and dynamics of the studied system and its components. Due to existing limitations on simulation time lengths, the studied system is often unable to investigate the interactions significantly from the starting configuration, and thus, any bias in the starting geometries will affect the final results of MD simulation. To avoid this bias and make the comparisons to experimental conditions more meaningful, the MD simulation in phase I was carried out to obtain a stable and reliable starting point. Another limitation in carrying out ideal MD simulations on these types of systems is the absence of enough information delivered of the binding process itself. In an ideal world, a researcher would like to perform simulations in which the ligand is gradually approached into the binding pocket of the protein and to extract thermodynamic information on the binding process. Sorrowfully, the computational cost of such simulation procedures can become too expensive when full atomic


details are considered (Shahlaei et al., 2011a, b). Thus, as discussed above, we primarily applied simple docking of the ligand to A1AR to place the ligand into the active site. These results were not decisive because in vivo, binding of inhibitor to a receptor is a dynamic process. Docking results can only stand for instantaneous states. Stable binding modes of ligand to receptor are valuable for further study. MD calculation will help choose stable binding structures and recognize the best binding mode. Therefore, one additional phase MD simulation was applied to gain further insight into the mechanisms that could explain the experimentally observed activity. Worthy of attention that what we can expect from MD simulations of proteins in the presence of biological lipid bilayer is that such MD simulations give complete details of the motions of lipids, protein and ligands in the system. Concluded those information and statistical mechanics, thermodynamic properties could be estimated. Therefore, the A1AR–ligand complex structure corresponding to the lowest calculated free energy of binding was selected for structure refinement using MD simulation. Binding modes between RG-14718 and bamifylline in A1AR model after MD simulation: Phase II Figure 6a, b depicts the binding conformation of RG14718 and bamifylline in the binding pocket of A1AR, which were derived from the MD simulation followed by energy minimization. The results obtained from our

docking studies are supported by MD simulation in phase II for the A1AR_RG-14718 complex and A1AR_bamifylline complex. Importantly, as it can be seen in Fig. 6, the presence of the ligand in the vicinity of protein during MD simulation leads to introduction of some new residues and some new interactions into the binding pocket compared with docking. There are some differences in key residues interacting with RG-14718 and bamifylline, and these differences somewhat affect the position of RG-14718 and bamifylline in the binding pocket of A1AR. These interactions are also observed in docking and well conserved in MD simulation confirming a crucial role of these interactions in ligand binding. To further validate the developed model of interaction between RG-14718 and A1AR active site, the software LIGPLOT was used to investigate the hydrophobic and hydrogen bonding interactions. As shown in the 2D schematic interaction model of RG-14718 with A1AR (Fig. 6A, left), there are hydrophobic interactions between the carbon atoms of the ribose moiety of RG-14718 and Val62, Val87 (Kolb et al., 2012; Giordanetto et al., 2003), Glu172 (Kolb et al., 2012; Piirainen et al., 2011; Jaakola et al., 2008; Ivanov et al., 2007; Giordanetto et al., 2003; Jiang et al., 1997; Kim et al., 1996) and Leu250 (on the TM6) (Kolb et al., 2012; Giordanetto et al., 2003), suggesting that more hydrophobic interactions around this area should improve A1AR inhibitory activity. Also, it must be noted that the conformation of RG-14718 in the A1AR binding pocket becomes quite different from that in the docking binding

Fig. 6 Schematic representation of the interaction between A1AR and selective A1 agonist RG-14718 (left) and selective A1 antagonist bamifylline (right) produced after MD simulation phase II using the Ligplot software

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pocket. The ethyl moiety of RG-14718 (Figs. 1, 5) is packed with Phe171. The chlorothienyl part is located in close proximity to Thr270 (Kolb et al., 2012; Giordanetto et al., 2003; Dawson and Wells, 2001) and Ile274 (Kolb et al., 2012; Piirainen et al., 2011; Jaakola et al., 2008; Giordanetto et al., 2003, Dawson and Wells, 2001; Kim et al., 1995). The most important adjustment was formation a hydrogen bond between Thr270 and the hydroxyl hydrogen atom at the 40 position of the ribose ring in the ˚ . In the RG-14718 with the bond length of 2.95 A A1AR_RG-14718 complex after MD simulation, the relative position of two chlorothienyl and ribose rings in RG-14718 exchanges. Docking results of the bamifylline to A1AR model showed that at the end of MD simulation two new hydrogen bonds was found to exist between docked molecule and His278 (Olah et al. 1992; Kim et al., 1995; Gao et al., 2000; Fredholm et al., 2001; Dawson and Wells, 2001; Ivanov et al., 2007; Piirainen et al., 2011; Kolb et al., 2012) and Asn70 (Xie et al., 2006) of A1AR model, and previous hydrogen bond between ligand and Glu172 was vanished (Fig. 6a, right). The new hydrogen bond was formed between the nitrogen atom of xanthine ring of ligand and nitrogen atom of His278 with the bond length of ˚ . The new another hydrogen bond was formed 2.97 A between the oxygen atom of xanthine ring of ligand and ˚. nitrogen atom of Asn70 with the bond length of 2.88 A Also, OH group of the N-hydroxyethyl moiety establishes a hydrogen bond with Thr270 (Kolb et al., 2012; Giordanetto et al., 2003; Dawson and Wells, 2001) side chain with the ˚ . This moiety is packed perfectly in bond length of 2.91 A Tyr271 (Kolb et al., 2012; Giordanetto et al., 2003). The bamifylline is also stabilized by hydrophobic interactions on the two farthest ends of the molecule. On the one end, the phenyl ring is making stacking interaction with Phe275 (Kolb et al., 2012; Giordanetto et al., 2003) as shown in the 2D LIGPLOT analyses (Fig. 6a, right). Thus, Phe275 may play a key role in orienting the ligand at the active site by hydrophobic interactions. This residue does not exist in the binding site of A1AR recognized by docking process. Similarly, on the other end, the phenyl group is surrounded by three hydrophobic residues, Ala66, Ile69 (Xie et al., 2006) and Ile274 (Kolb et al., 2012; Piirainen et al., 2011; Jaakola et al., 2008; Giordanetto et al., 2003, Dawson and Wells, 2001; Kim et al., 1995). At the end of MD simulation, the hydrophobic pocket of A1AR, which appears at the end of docking including Phe171, was vanished and hydrophobic interactions between phenyl ring of ligand and these residues could not be recognized. The orientation of the phenyl and xanthine rings of the bamifylline was not similar in binding modes after molecular docking and MD simulation. In addition, N-methyl xanthine moiety shows van der Waals contacts with Glu16 (Gao et al., 2000;

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Fredholm et al., 2001; Ivanov et al., 2007; Piirainen et al., 2011; Barbhaiya et al., 1996), Ile63 and Asn70 (Xie et al., 2006). These results reveal that MD simulation applies the Newton laws to the nuclei of a molecular system. On docking of this ligand with A1AR, the lowest energy conformation did not show any interaction with the Glu172 and Phe171 side chains due to the absence of appropriate orientation and distance as was observed at the end of MD simulation with A1AR. After structural refinement of the A1AR model by MD in phase I and phase II, the geometric quality determination of the backbone conformation, namely all tests performed in homology modeling step, was carried out again and the quality of the model was confirmed (for instance, results of PROCHECK are reported in table S1). Importantly, simulation of A1AR in the presence and the absence of ligand led to the different final peptide conformation and orientation. There are some differences in the backbone dihedral angle values, and these differences considerably affect the position of some residues in the Ramachandran plot. As it can be seen in table S1, the presence of the ligand in the active site of protein during MD simulation leads to the displacement of some residues into the disallowed regions. Figure 7a shows the time history of RMSD for protein structure immersed in lipid bilayer relative to the starting structure (the output of the phase I process). As it is evident, backbone RMSD was about 0.17 nm after 20 ns of simulation and was not increased significantly after 15 ns of simulations. After 2-ns simulation, the system reached equilibrium. Maximum fluctuation for the studied structure

Fig. 7 System stability was monitored as the backbone RMSD of A1AR in a MD phase II with respect to starting structures. Also, in b RMSD of ligand’s atoms from the starting structure of docked ligand as a function of time during MD simulation phase II


was on the order of *0.19 nm (for the backbone atoms), which means that to some extent larger conformational changes took place at about last ^17 ns. The average RMSD of the backbone atoms during the last 15 ns of MD was 0.16 ± 0.01 nm. In the last 15 ns of simulation, the system was fairly stable and did not show a meaningful change from 0.16 ± 0.01 nm. As it is indicated in this figure, the structures of the developed model are stable during the MD simulation. Consequently, the MD simulation was essential to specify geometry of A1AR in the vicinity of lipid bilayer. The RMSD of the RG-14718 and bamifylline was calculated based on the MD simulation of the system to obtain information on position fluctuations and movements of ligand’s atoms. Figure 7b shows that the RMSD of RG14718 from the initial conformation increased to 0.14 nm after 3 ns and then leveled off to the end of simulation. This shows that, after 3-ns simulation and preliminary fluctuations in the magnitude of RMSD of ligand’s atoms, the ligand obtained an equilibrium state characterized by the RMSD profile. Also, Fig. 7b shows that the RMSD of the bamifylline from the initial conformation increased to 0.16 nm after a few hundreds of picoseconds and then leveled off to nearly 7.8 ns. At this point of simulation, RMSD of ligand roused up to F0.20 nm suddenly and after about 10.3 ns dropped up again to 0.10 nm and leveled off. This shows that, after F10-ns simulation and preliminary fluctuations in the magnitude of RMSD of ligand’s atoms, the ligand obtained an equilibrium state characterized by the RMSD profile. Also, it can be seen that ligands (RG-14718 and bamifylline) are within the active site after a few hundreds of picoseconds in both systems (phase II) with a mean RMSD values of 0.10 ± 0.02 and 0.11 ± 0.02 nm, respectively. During the MD simulations, these two modes remain in a stable binding position with low RMSD fluctuations, confirming the feasibility of the binding poses predicted by AutoDock. This is in agreement with biological activity of the ligand. It can be seen that the A1AR_RG-14718 and A1AR_bamifylline complexes immersed among biomembrane and water molecules stays in equilibrium throughout the entire MD. Hence, we deduce that the MD simulation has constructed an improved and more relaxed structure of protein and ligand, which can be analyzed for further studies.

Conclusions The lack of an experimentally determined 3D structure of target proteins often limits the application of structurebased drug approaches. The model developed in this study

involves the main structural features of A1AR, according to the GPCRs characteristics and therefore, could serve as a reliable target for drug design investigations. Homology modeling combined with molecular docking and MD simulation in an explicit membrane environment were performed effectively for recognizing various characteristics of A1AR, a protein with no experimentally determined 3D structure. In the lack of an experimentally established crystal structure of a given protein, homology modeling is one of the best alternative methods to construct a reasonable 3D model of the target. According to evaluation methods, the generated human A1AR model of the current study by means of homology modeling seems to provide a good quality based on common structural criteria. A common shortcoming shared by the previously mentioned studies is that the authors explored A1 structure/function without considering the presence of lipid bilayer; however, this study presented the first modeling of A1AR in lipid bilayer. Structural features of the simulated model were evaluated by different parameters. Passing all aspects of checking by the developed model ensures that the A1AR model will be able to describe various protein– ligand interactions and also the protein structure. Docking simulations were carried out in order to predict the potential binding site, the type of interactions of a potent agonist (RG-14718) and a selective antagonist (bamifylline) in the constructed A1AR model. The docking results of RG-14718 and bamifylline allowed us to propose a general binding mode for A1AR and to determine residues involved in ligand binding. Explorative runs of MD simulation on the receptor–ligand complex revealed that except for Phe171, Glu172, Thr270, Tyr271 and Ile274, the rest of the residues in the active site determined by docking are changed. Asn70, Ihr270 and His278 were crucial to form hydrogen bonds with the ligands. Glu16, Val62, Ile63, Ala66, Ile69, Val87, Leu250, Ile274 and Phe275 were also involved in hydrophobic interactions with the ligands. Key binding residues were located on the EL2 and TMs 1, 2 and 7. Most of these residues, especially Glu16, Val87, Glu170, Phe171, Glu172, Thr270, Ile274 and His278, and in the putative binding site are conserved among the four adenosine receptor subtypes. On the basis of our results, we could confirm for binding mode of the A1AR agonists and antagonists, the importance of interactions with amino acid residues Ile69, Val87, Cys169, Glu170, Phe171, Glu172, Leu250, Ser267, Thr270, Tyr271, Ile274 and His278 previously highlighted by site-directed mutagenesis experiments. At the end of MD simulation, position and orientation of ligand in the introduced binding site were changed, and this important observation indicates useful application of MD simulations after docking of ligands in the binding site.

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