molecules

Article A Complete Assessment of Dopamine Receptor- Ligand Interactions through Computational Methods

Beatriz Bueschbell 1 , Carlos A. V. Barreto 2 , António J. Preto 2, Anke C. Schiedel 1 and Irina S. Moreira 2,3,*

1 PharmaCenter Bonn, Pharmaceutical Institute, Pharmaceutical Chemistry I, University of Bonn, D-53121 Bonn, Germany; [email protected] (B.B.); [email protected] (A.C.S.) 2 Center for Neuroscience and Cell Biology, UC- Biotech Parque Tecnológico de Cantanhede, Núcleo 04, Lote B, 3060-197 Cantanhede, Portugal; [email protected] (C.A.V.B.); [email protected] (A.J.P.) 3 Institute for Interdisciplinary Research, University of Coimbra, 3004-531 Coimbra, Portugal * Correspondence: [email protected]; Tel.: +351-231-249-730



Received: 5 February 2019; Accepted: 23 March 2019; Published: 27 March 2019


Abstract: Background: Selectively targeting dopamine receptors (DRs) has been a persistent challenge in the last years for the development of new treatments to combat the large variety of diseases involving these receptors. Although, several drugs have been successfully brought to market, the subtype-specific binding mode on a molecular basis has not been fully elucidated. Methods: Homology modeling and molecular dynamics were applied to construct robust conformational models of all dopamine receptor subtypes (D1-like and D2-like). Fifteen structurally diverse ligands were docked. Contacts at the binding pocket were fully described in order to reveal new structural findings responsible for selective binding to DR subtypes. Results: Residues of the aromatic microdomain were shown to be responsible for the majority of ligand interactions established to all DRs. Hydrophobic contacts involved a huge network of conserved and non-conserved residues between three transmembrane domains (TMs), TM2-TM3-TM7. Hydrogen bonds were mostly mediated by the serine microdomain. TM1 and TM2 residues were main contributors for the coupling of large ligands. Some amino acid groups form electrostatic interactions of particular importance for D1R-like selective ligands binding. Conclusions: This in silico approach was successful in showing known receptor-ligand interactions as well as in determining unique combinations of interactions, which will support mutagenesis studies to improve the design of subtype-specific ligands.

Keywords: dopamine receptors; molecular docking; molecular dynamics; receptor-ligand interactions

1. Introduction

1.1. Dopamine Receptors The dopaminergic system has been intensively studied over the past 75 years due to the (patho)physiological role in modulating cognitive and motor behaviour [1,2]. The importance of dopamine has dramatically emerged from being just an intermediate in the formation of noradrenaline to having a celebrity status as the most important mammalian neurotransmitter [3]. Dopamine binds to five distinct dopamine receptors (DRs; D1–5 Receptor), grouped into two classes —D1-like and D2-like receptors— that differ in their physiological effects and signal transduction. The D1-like receptors, D1R and D5R, are principally coupled to Gs proteins and enhance the activity of adenylyl-cyclase, whereas D2-like receptors, D2–4R, are primarily coupled to inhibitory Gi proteins and suppress the activity of adenylyl cyclases [1,4]. The DRs belong to the G Protein-Coupled Receptor family (GPCRs), the largest and most diverse protein family in humans with approximately 800 members [5,6].

Molecules 2019, 24, 1196; doi:10.3390/molecules24071196 www.mdpi.com/journal/molecules Molecules 2019, 24, x FOR PEER REVIEW 2 of 26

adenylyl-cyclase, whereas D2-like receptors, D2–4R, are primarily coupled to inhibitory Gi proteins andMolecules suppress2019, 24 ,the 1196 activity of adenylyl cyclases [1,4]. The DRs belong to the G Protein-Coupled2 of 26 Receptor family (GPCRs), the largest and most diverse protein family in humans with approximately 800 members [5,6]. GPCRs share a conserved overall fold of seven transmembrane helices (TMs) GPCRs share a conserved overall fold of seven transmembrane helices (TMs) linked by three linked by three intracellular loops (ICLs) and three extracellular loops (ECLs). Around 30–40% of all intracellular loops (ICLs) and three extracellular loops (ECLs). Around 30–40% of all available available pharmacotherapeutics target this protein family [7]. pharmacotherapeutics target this protein family [7]. Many severe neuropsychiatric and neurodegenerative disorders such as Tourette’s syndrome, Many severe neuropsychiatric and neurodegenerative disorders such as Tourette’s syndrome, schizophrenia, Parkinson’s disease and Huntington’s disease are believed to occur as a result of schizophrenia, Parkinson’s disease and Huntington’s disease are believed to occur as a result imbalances and alterations in dopamine signaling [8–10]. Moreover, also a wide array of psychiatric of imbalances and alterations in dopamine signaling [8–10]. Moreover, also a wide array of disorders such as hallucinations, paranoia, bipolar disorder, gambling, alcoholism, mania, psychiatric disorders such as hallucinations, paranoia, bipolar disorder, gambling, alcoholism, mania, depression, eating disorders, movement and hyperactivity disorders are linked to malfunctioning depression, eating disorders, movement and hyperactivity disorders are linked to malfunctioning dopaminergic transmission [3,11–13]. The discovery of chlorpromazine, the first antipsychotic drug, dopaminergic transmission [3,11–13]. The discovery of chlorpromazine, the first antipsychotic in the 1950s, was the hallmark of the development of a collection of antipsychotics [14] which were drug, in the 1950s, was the hallmark of the development of a collection of antipsychotics [14] later reported to commonly bind to the D2R subtype (with different affinity) [4,11]. The “first- which were later reported to commonly bind to the D2R subtype (with different affinity) [4,11]. generation/classical” antipsychotics came along with significant motor side effects such as tardive The “first-generation/classical” antipsychotics came along with significant motor side effects such dyskinesia, extrapyramidal symptoms and related conditions. These problems were not eliminated as tardive dyskinesia, extrapyramidal symptoms and related conditions. These problems were not in “second-generation/atypical” antipsychotics, and others such as weight gain and the “metabolic eliminated in “second-generation/atypical” antipsychotics, and others such as weight gain and the syndrome” also appeared [11,15–17]. It was then later discovered that these multiple clinical and “metabolic syndrome” also appeared [11,15–17]. It was then later discovered that these multiple clinical adverse effects of several antipsychotics depended on the combination of occupied receptors from and adverse effects of several antipsychotics depended on the combination of occupied receptors from other systems such as cholinergic, histaminergic and serotoninergic receptors (but always including other systems such as cholinergic, histaminergic and serotoninergic receptors (but always including the the D2R) resulting in non-selective profiles and therefore in an insufficient explanation of the D R) resulting in non-selective profiles and therefore in an insufficient explanation of the mechanism mechanism2 of action [11,15]. Until today, no drug has yet been identified with antipsychotic action of action [11,15]. Until today, no drug has yet been identified with antipsychotic action without without a significant affinity for the D2R [15]. However, antipsychotics remain a necessary first-line a significant affinity for the D R[15]. However, antipsychotics remain a necessary first-line treatment treatment for the management2 of a variety of the already mentioned psychiatric disorders (Figure 1). for the management of a variety of the already mentioned psychiatric disorders (Figure1). In fact, In fact, it is difficult to directly target one of these diseases with one specific antipsychotic, since there it is difficult to directly target one of these diseases with one specific antipsychotic, since there are are also numerous cases of non-responding patients to first-line or any antipsychotic treatment or also numerous cases of non-responding patients to first-line or any antipsychotic treatment or which which become resistant to treatment over time [11,18]. become resistant to treatment over time [11,18].

Figure 1. Examples for first-line treatments of neurological diseases and selective dopamine receptor Figure 1. Examples for first-line treatments of neurological diseases and selective dopamine receptor ligands. Drugs are classified in typical and atypical antipsychotics [19]. The targets of the selective ligands.ligands haloperidole,Drugs are classified 7-OH-DPAT, in typical SCH23390 and atypical and SKF38393 antipsychotics were colored [19]. The in blue. targets of the selective ligands haloperidole, 7-OH-DPAT, SCH23390 and SKF38393 were colored in blue.

The search for DR subtype selective (foremost D2R-selective) therapeutics is an ongoing field of research [15] as current drugs have D2R/D3R-affinity or affinity for all DRs [19,20]. It has been proposed Molecules 2019, 24, 1196 3 of 26

that substituted 4-phenylpiperazine compounds distinguish between D3R and D2R selectivity [21,22]. In addition, the aminotetraline derivative 7-OH-DPAT was identified as a selective D3R agonist [23,24], whereas it was shown that most D4R available therapeutics are not selective [22], with only one exception, haloperidole [25] (Figure1). Also, finding D 1-like DR targeting ligands, a more challenging aim [26,27], may improve antipsychotic treatment, as D1R was also shown to be relevant for modulating behaviour in health and disease (reviewed in O’Sullivan et al. [28]). So far, SKF38393 was the only selective agonist attained for the D1R, while D5R completely lacks a selective ligand [29,30]. SCH23390 was proposed as the only D1-like DR selective antagonist [31] (Figure1).

1.2. Computer-Aided Drug Design The strive for finding new and effective therapeutics led to a growing interest in the use of Computer-Aided Drug Design (CADD). Originally developed for high-throughput screening (HTS) of compound libraries, the use of CADD nowadays plays an important role in drug discovery [32]. The CADD pipeline can be classified into two general categories: structure-based and ligand-based, dependent on the available information about the topic of investigation [33]. A structure-based CADD is used when the target, e.g., a receptor, is known and compound libraries can be screened to find suitable structures for the target. In contrast, ligand-based CADD relies on known active and inactive compounds with their affinities in order to construct quantitative pharmacophore models and to perform virtual screening that is carried out target-independently [32]. Both CADD approaches are only fruitful if sufficient information is present. Structure-based approaches rely on the availability of the target crystal structure or homologs proteins whereas pharmacophore and other ligand-based methodologies rely on the existence of a sufficient number of ligands. For example, for GPCRs potentially involved in Parkinson’s disease, a variety of molecular docking studies were carried out using resolved crystal structures to which self-synthesized ligands were docked (reviewed in Lemos et al., [34]). Vice versa, inspection of known ligands was used to build pharmacophores or Quantitative Structure-Activity-Relationships (QSAR) to screen for new bioactive molecules (reviewed in Lemos et al., [34]). All in all, CADD is capable of addressing many challenges in hit-to-lead-development and is currently widely used in the pharmaceutical industry [34,35].

1.3. Aim Modeling GPCRs remains problematic due to the complex structure of these membrane proteins and the lack of structural information about the desired receptor to target. However, the recent boom of resolved X-ray crystallography structures leads to a more promising application of CADD approaches to this receptor. Herein, we used tools of structure-based CADD to investigate the receptor-ligand properties of all DR-subtypes with marketed DR therapeutics. In particular, we: (i) applied homology modeling by using the resolved X-ray crystallography structures of the dopamine receptors D2R, D3R and D4R (D2R bound to the atypical antipsychotic risperidone, PDBid: 6CM4 [36]; D3R bound to D2R/D3R-selective antagonist eticlopride, PDBid: 3PBL [37]; and D4R in complex with D2R/D3R-selective antagonist nemonapride, PDBid: 5WIU [38]) in order to provide models with structural ligand-free properties; (ii) performed Molecular Dynamics (MD) of the five model structures, and (iii) performed molecular docking studies of 15 ligands targeting different conformational rearrangements’ of DR subtypes. The binding energies, number of conformations as well as the distances between ligands and receptor interacting residues of the binding pocket were calculated for all complexes. The interaction between ligands and receptors were further analyzed using an in-house pipeline that takes advantage of BINding ANAlyzer (BINANA), a Python- implemented algorithm for analyzing ligand binding [39]. BINANA was shown to successfully atomically characterize key interactions between protein amino acids and ligand atoms, and as such it is a promising approach to map such interactions in GPCRs [39]. The main goal was to reveal new structural findings to help explain the mode of binding of the selected ligands and their selectivity for certain DR-subtypes. Molecules 2019, 24, 1196 4 of 26

2. Results

2.1. Homology Models of Dopamine Receptors D1R-D5R Are Stable

The ligand-free D2-like homology models were generated using the resolved ligand-bound crystal structures of the D2R (PDBid: 6CM4) [36], D3R (PDBid: 3PBL) [37] and D4R (PDBid: 5WIU) [38] (over 90% identity). The 3D crystal structures of DRs are typically incomplete, lacking key regions for intracellular partner coupling such as intracellular loop 3 (ICL3). In contrast, D1-like DRs lack their own templates and therefore, the most suitable template to each DR was selected according to the percentage of similarity obtained upon sequence alignment by BLAST [40] in combination with ClustalOmega [41]. In fact, the D3R crystal structure was chosen as template for D1R (35.0% identity with BLAST and 39.5% with ClustalOmega), and the D4R crystal structure was chosen for D5R models (total similarity of 35.0% BLAST/39.1% ClustalOmega, check Materials and Methods section). We also calculated the similarity of the TMs in relation to the respective template and the results are summarized in Table S1. All TMs of the D2-like subtypes showed almost 100% identity with their crystal structure templates, which is also in line with the total similarity. For D1R an average TM similarity with its template was 41.0%, compared to a total similarity of 39.5%, while for D5R 36.0% TM identity was calculated compared to the total similarity of 39.1%. For the D1-like subtypes no differences between the TM similarity and the total similarity with their template were obtained. Furthermore, for D1R the highest similarity between the model and its template was observed for TM1-3, whereas for D5R it was achieved for TM2, TM3 and TM7. Consequently, TM2 and TM3 seem to be very conserved among all DR subtypes. The combination of different metrics and scores were used to choose the most accurate models in order to perform MD and molecular docking: (i) the Discrete Optimized Protein Energy (DOPE) [42] score, MODELLER’s standard metrics, (ii) Protein Structure Analysis (ProSA-web) [43,44] and (iii) Protein Quality (ProQ)-LGscore and MaxSub score [45,46]. All final DR models (check Materials and Methods section) achieved LGscores > 4 and MaxSub scores > 0.5. The highest z-score was obtained for D4R model, whereas the lowest were counted for D1-like DR models. MD simulations of 100 ns were briefly run for each ligand-free modelled receptor and analyzed to confirm their stability. Root-Mean-Square-Deviations (RMSD) of Cα atom mean values ranged from 0.3 nm and 0.5 nm (Figure S1). Each DR model showed good overall stability. However, D1-like models showed slightly higher RMSD values than D2-like models: D1R (0.48 ± 0.07 nm) and D5R (0.49 ± 0.06 nm) vs D2R (0.35 ± 0.09 nm), D3R (0.34 ± 0.04 nm) and D4R (0.36 ± 0.09 nm). This behavior is justified by the higher homology scores attained for the D2-like subfamily. Additionally, RMSD of Cα carbons for individual TM was computed and their average values were listed in Table S2. The low values obtained for TM’s RMSD further supports the good overall stability of the model structures.

2.2. Dopamine Receptor Binding Pocket Definition In this work, we used the comprehensive review of Floresca and Schnetz [47], highly cited [48–50], as a base for the definition of the binding pocket of all DRs (Figure2). Furthermore, by applying Ballesteros and Weinstein numbering (B&W) [51], the position of considered critical residues was more easily comparable between all receptors. This nomenclature is based on the presence of a highly conserved residue in each TMs [51], the so called X.50, in which X varies between 1 and 7 depending on the TM helix. The remaining residues were numbered according to their position relative to the most conserved one. Molecules 2019, 24, 1196 5 of 26 Molecules 2019, 24, x FOR PEER REVIEW 5 of 26

FigureFigure 2.2. KeyKey residuesresidues forfor molecularmolecular dockingdocking ofof DopamineDopamine toto DRDR models.models. ResiduesResidues werewere designateddesignated inin boldbold andand coloredcolored inin cyan,cyan, TMsTMs inin boldbold andand italic.italic. ThisThis set-upset-up waswas keptkept forfor FiguresFigures S3–S7S3–S7 andand highlyhighly correspondscorresponds toto thethe definitiondefinition ofof thethe bindingbinding pocketpocket ofof FlorescaFloresca andand SchnetzSchnetz [47[47].]. TheThe areaarea ofof thethe orthostericorthosteric andand secondarysecondary bindingbinding pocketspockets werewere coloredcolored and and violet, violet, respectively. respectively.

MutagenesisMutagenesis studies studies induced induced the the believe believe that that for for dopamine dopamine binding, binding, the the endogenous endogenous agonist agonist of the of DR,the DR, a negatively a negatively charged charged aspartate aspartate (3.32Asp) (3.32Asp) forms form an ionics an bondionic interactionbond interaction with the with protonable the protonable amine ofamine dopamine of dopamine [2,50,52 ].[2,50,52]. Moreover, Moreover, it was shown it was that shown this that effect this was effect crucial was for crucial ligand for binding ligand and binding that thisand amino-acidthat this amino-acid was not onlywas not conserved only conserved among the among DR, butthe alsoDR, but in all also biogenic in all biogenic amine GPCRs amine [GPCRs53,54]. Also,[53,54]. a serine Also, a microdomain serine microdomain in TM5 (5.42Ser,in TM5 (5.42Ser, 5.43Ser, 5.43Ser, 5.46Ser) 5.46Ser) was considered was considered as an important as an important feature forfeature dopaminergic for dopaminergic binding binding in all DRs in asall it DRs is believed as it is believed that the serinesthat the form serines hydrogen form hydrogen bonds with bonds the catecholwith the hydroxyls catechol hydroxyls of dopamine, of dopamine, increasing increasing the binding the affinity binding and affinity orienting and ligands orienting in the ligands orthosteric in the bindingorthosteric pocket binding [47,52 ,pocket55–57]. [47,52,55–57]. While 5.42Ser seemsWhile to5.42Ser be critical, seems 5.43Ser to be plays critical, a less 5.43Ser important plays role a [less47]. Aimportant further microdomain,role [47]. A further the aromatic microdomain, microdomain, the aromatic consisting microdomain, of 6.48Trp, consisting 6.51Phe, 6.52Phe of 6.48Trp, and 6.55His/Asn6.51Phe, 6.52Phe was and reported 6.55His/Asn to trigger was the reported activation to of trigger the dopamine the activation receptor. of the All dopamine amino-acids receptor. in this microdomainAll amino-acids share in the this same microdomain hydrophobic share face inthe the same water-assessable hydrophobic binding-site face in the crevice, water-assessable indicating thatbinding-site any reorientation crevice, indicating of these residues that any by reorientation binding to aof ligand these wouldresidues cause by binding steric clashes to a ligand and wouldwould thereforecause steric force clashes the residues and would to reorienttherefore themselves force the re insidues a domino-like to reorient fashion, themselves which in lastlya domino-like leads to thefashion, so-called which “rotamer lastly leads toggle to the switch” so-called [47,50 “rotamer,53,58]. Intoggle addition, switch” 6.48Trp [47,50,53,58]. was reported In addition, together 6.48Trp with 6.55Hiswas reported to stabilize together the positionwith 6.55His of the to ligand stabilize in thethe bindingposition pocket of the vialigandπ-π in-stacking the binding [47,58 pocket]. Therefore, via π- 6.48Trpπ-stacking and [47,58]. 6.55His Therefore, as well as 6.48Trp one phenylalanine and 6.55His as (6.51Phe) well as one were phenylalanine chosen for the (6.51Phe) docking were protocol chosen to mimicfor thethe docking ligand-binding protocol to on mimic TM6. the Dependent ligand-bindi on theng ligandon TM6. properties Dependent other on residues the ligand of TM3properties were alsoother considered, residues of such TM3 as were 3.33Val also andconsidered, 3.36Cys. such 3.36Cys as 3.33Val is believed and 3.36Cys. to be part 3.36Cys of a deeper is believed subpocket to be belowpart of thea deeper orthosteric subpocket binding below pocket the orthosteric (OBP) [36 binding]. Additionally, pocket (OBP) Ericksen [36]. etAdditionally, al. reported Ericksen that this et cysteineal. reported was that a relevant this cysteine residue was for a benzamiderelevant resi bindingdue for benzamide [49]. Regarding binding 3.33Val, [49]. Regarding it was reported 3.33Val, to showit was interaction reported to with showN-methylspiperdone interaction with N by-methylspiperdone Moreira et al. [53 by] as Moreira well as withet al. the[53] methoxy as well as ring with of nemonapride,the methoxy ring determined of nemonapride, in the crystal determined structure ofin the the D crystal4R[38]. structure Different authorsof the D hypothesized4R [38]. Different that DRsauthors have hypothesized a secondary that binding DRs pockethave a (SBP)secondary next tobinding the OBP, pocket which (SBP) was next confirmed to the OBP, by the which resolved was crystalconfirmed structures by the togetherresolved withcrystal computational structures together analyses with [37 computational,38,59]. Crystal analyses structures [37,38,59]. of D2R (PDBid: Crystal 6CM4)structures [36] of and D2 DR3 (PDBid:R (PDBid: 6CM4) 3PBL) [36] [37 ]and and D computational3R (PDBid: 3PBL) data [37] suggest and computational that 7.43Tyr is alsodata a suggest crucial amino-acidthat 7.43Tyr for is interactionalso a crucial in theamino-acid SBP [17,36 for,37 interaction]. 2.57Val was in the shown SBP to[17,36,37]. form a hydrophobic 2.57Val was pocket shown for to antagonistsform a hydrophobic like clozapine pocket and for haloperidole antagonists [like57]. clozapine However, and since haloperidole the OBP is widely [57]. However, explored since through the OBP is widely explored through experimental, computational and crystal structure data, there could be other residues important in the SBP. Detailed information about the literature, mostly regarding Molecules 2019, 24, 1196 6 of 26 experimental, computational and crystal structure data, there could be other residues important in the SBP. Detailed information about the literature, mostly regarding D2-like DR can be found on Table S3. InMolecules order to 2019 compare, 24, x FOR all PEER DRs REVIEW ligand-binding properties and specificity, we focused on the mentioned6 of 26 residues in the OBP. The residues considered flexible in the different dockings were listed in Methods D2-like DR can be found on Table S3. In order to compare all DRs ligand-binding properties and and Materials section. specificity, we focused on the mentioned residues in the OBP. The residues considered flexible in the 2.3.different Proof-of-Concept dockings ofwere Molecular listed in Docking Methods Success and Materials section.

2.3.Ten Proof-of-Concept conformational of Molecular rearrangements Docking Success. were chosen every 5 ns upon a 50 ns stabilization MD run. These 10 plus the initial model (time 0 ns) were then subjected to molecular docking of 15 different Ten conformational rearrangements were chosen every 5 ns upon a 50 ns stabilization MD run. ligands.These 10 The plus results the initial of the model molecular (time 0 dockingns) were then were subjected evaluated to bymolecular AutoDock4.2, docking whichof 15 different ranks the possibleligands. binding The results positions of the by molecular energy level docking and clusterswere evaluated these positions by AutoDock4.2, by RMSD ofwhich 2 Å. ranks In addition, the thepossible total number binding of positions conformations by energy (NoC) level inand these clusters clusters these were positions counted. by RMSD Binding of 2 posesÅ. In addition, with more thanthe fivetotal conformations number of conformations per cluster (NoC) were considered in these clusters as a validwere ligandcounted. position, Binding despite poses with the bindingmore energythan five (BE) conformations of this pose. All per results cluster of were the dockingconsidered can as be a checkedvalid ligand in the position, Supplementary despite the Information binding (Tablesenergy S4–S8). (BE) of this pose. All results of the docking can be checked in the Supplementary Information (TablesAs proof S4–S8). of concept, redocking of the co-crystalized ligands to the crystal structure templates of the D2R,As D proof3R and of Dconcept,4R[36– redocking38] was conducted of the co-crystaliz (Figure S2,ed ligands Table S9). to the Receptors crystal structure and ligands templates coordinates of werethe retrievedD2R, D3R and from D4R PDB [36–38] files. was Top conducted clusters (Figure achieved S2, Table a ligand S9). poseReceptors equivalent and ligands to the coordinates pose in the correspondentwere retrieved crystal, from PDB presenting files. Top very clusters small achiev RMSDed values. a ligand Lastly, pose theseequivalent results to werethe pose compared in the to thecorrespondent docking poses crystal, of the presenting corresponding very small DR-models RMSD values. and ligands Lastly, atthese time results point were 0 ns. compared The binding to energiesthe docking of the poses two sets of werethe corresponding found to fall withinDR-model a similars and range.ligands This at time is a further point 0 evidence ns. The ofbinding docking protocolenergies reliability. of the two sets were found to fall within a similar range. This is a further evidence of docking protocolFor a reliability. general overview, dopamine docking was analyzed in detail (Figure3A) as it is the endogenousFor a ligandgeneral of overview, the DRs and dopamine its binding docking mode was is well-known analyzed in compared detail (Figure to the 3A) other as ligandsit is the [47 ]. endogenous ligand of the DRs and its binding mode is well-known compared to the other ligands However, we have to stress out the lack of a crystal structure with the dopamine-bound DR as the [47]. However, we have to stress out the lack of a crystal structure with the dopamine-bound DR as ligand’s structural properties are not suitable for crystallization (too small, not suitable for stabilizing the ligand’s structural properties are not suitable for crystallization (too small, not suitable for a GPCR). We observed that the binding energy of D2R was the most stable at different analyzed MD stabilizing a GPCR). We observed that the binding energy of D2R was the most stable at different conformations, while for the other subtypes it oscillated more frequently. Over time the average binding analyzed MD conformations, while for the other subtypes it oscillated more frequently. Over time − energythe average for all binding DRs was energy found for to all be DRs at 9was kcal/mol. found to The be at highest −9 kcal/mol. NoC duringThe highest all MD NoC conformations during all were obtained for D R and D R (up to > 80 for D R at 95 ns), whereas for D R around 30 conformations MD conformations4 were obtained1 for D4R and4 D1R (up to > 80 for D4R at2 95 ns), whereas for D2R werearound counted 30 conformations for all conformational were counted arrangements for all conformational (Figure3B). arrangements Lastly, for all (Figure DRs complexed3B). Lastly, for with dopamine,all DRs complexed the first or with the seconddopamine, cluster the withfirst or the the lowest second binding cluster energy with the also lowest contained binding the energy highest also NoC, indicatingcontained that the thehighest docking NoC, of indicating dopamine that was the indeed docking stable of dopamine and reliable was (Tablesindeed S4–S8).stable and In reliable summary, the(Table binding S4–S8). energy In andsummary, 3D positions the binding of dopamine energy dockingand 3D positions may demonstrate of dopamine the bindingdocking mode may of dopaminedemonstrate to DRs. the Accordingbinding mode to Floresca of dopamine and Schnetz, to DRs. these According features areto Floresca crucial for and dopamine’s Schnetz, these binding affinityfeatures and are DR crucial activation for dopamine’s but may notbinding necessarily affinity beand true DR foractivation all dopaminergic but may not ligands necessarily (selective be true and non-selective)for all dopaminergic [47]. ligands (selective and non-selective) [47].

A B -12 40 D1R D1R D2R D2R 30 -10 D3R D3R D4R 20 D4R

-8 D5R D5R 10 Binding energy [kcal/mol] -6 Numberconformations of 0 0 556065707580859095100 0 556065707580859095100 Time points [ns] Time points [ns]

Figure 3. Results of the molecular docking of dopamine to all DR subtypes at all MD time steps. (AFigure) The average 3. Results binding of the energymolecular of the docking three lowestof dopamine energies to ofall dopamineDR subtypes was at calculated. all MD time (B steps.) The meanA. ofThe the numberaverage binding of conformations energy of the of thethree three lowe clustersst energies with of thedopamine lowest was binding calculated. energies B. The are mean shown offor eachthetime number point of andconformations receptor. of the three clusters with the lowest binding energies are shown for each time point and receptor. Molecules 2019, 24, 1196 7 of 26

The binding position of dopamine to all DR complexes was stable over time namely, the protonable amine was always directed towards the aspartic acid on TM3 (3.32Asp) and the hydroxy groups were facing the serine microdomain (5.42Ser, 4.32Ser and 4.46Ser), in agreement with Floresca and Schetz [47] Molecules 2019, 24, x FOR PEER REVIEW 7 of 26 Molecules 2019, 24, x FOR PEER REVIEW 7 of 26 andMolecules Durdagi 2019, 24 et, x al. FOR [60 PEER] (Figures REVIEW S3–S8). As known from the literature dopamine’s interaction7 of 26 with MoleculesThe 2019 binding, 24, x FOR position PEER REVIEW of dopamine to all DR complexes was stable over time namely,7 of the26 the serineThe microdomainbinding position only of typicallydopamine requires to all DR two complexes of the serines was stable binding over to time the hydroxynamely, the groups [47]. protonableThe binding amine wasposition always of directeddopamine towards to all theDR aspartic complexes acid wason TM3 stable (3.32Asp) over time and namely,the hydroxy the Atprotonable 0 nsThedopamine binding amine waswasposition always located of directeddopamine planar towards in to the all OBP theDR aspartic incomplexes the acid position wason TM3 stable described (3.32Asp) over above.time and thenamely, Notably, hydroxy the D2R and protonablegroups were amine facing was the alwaysserine microdomain directed towards (5.42Ser, the aspartic 4.32Ser andacid 4.46Ser),on TM3 in(3.32Asp) agreement and with the hydroxyFloresca D groupsRprotonable hydroxyl were amine facing groups was the were alwaysserine more microdomain directed directed towards (5.42Ser, towards the aspartic 4.32Ser serine andacid microdomain 4.46Ser), on TM3 in(3.32Asp) agreement (Figure and with S3). the hydroxy AtFloresca 55 ns torsions 4groupsand Schetz were [47] facing and the Durdagi serine microdomainet al. [60] (Figures (5.42Ser, S3–S8). 4.32Ser As andknown 4.46Ser), from in the agreement literature with dopamine’s Floresca andgroups Schetz were [47] facing and the Durdagi serine microdomainet al. [60] (Figures (5.42Ser, S3–S8). 4.32Ser As andknown 4.46Ser), from in the agreement literature with dopamine’s Floresca wereandinteraction observed Schetz with[47] for andthe dopamine serineDurdagi microdomain et bounded al. [60] (Figures only to all typi DR,S3–S8).cally which requiresAs known included two from of a thethe switch serinesliterature of binding interactions dopamine’s to the with the interactionand Schetz with[47] andthe serineDurdagi microdomain et al. [60] (Figures only typi S3–S8).cally requiresAs known two from of thethe serinesliterature binding dopamine’s to the serinesinteractionhydroxy at TM5 groups with for the[47]. D3 R,serine At since 0 microdomainns itdopamine is known wasonly that locatedtypi dopaminecally planar requires is in only the two capableOBP of thein theserines of interactingposition binding described withto the two of the hydroxyinteraction groups with the[47]. serine At 0 nsmicrodomain dopamine wasonly locatedtypically planar requires in the two OBP of thein theserines position binding described to the threehydroxyabove. serines Notably, groups [47]. D[47]. At2R 60 andAt ns 0D dopaminens4R dopaminehydroxyl is groupswas shifted located were more planarmore to the directedin serinethe OBP towards and in aromaticthe serine position microdomain microdomain described (TM6) above.hydroxy Notably, groups D[47].2R andAt 0D ns4R dopaminehydroxyl groupswas located were planarmore directed in the OBP towards in the serine position microdomain described above.(Figure Notably,S3). At 55 D 2nsR andtorsions D4R werehydroxyl observed groups for were dopamine more directedbounded towards to all DR, serine which microdomain included a for(Figureabove. all DRs Notably,S3). in At a different 55 D 2nsR andtorsions manner. D4R werehydroxyl However, observed groups for only were dopamine at Dmore4R aboundeddirected strong directiontowardsto all DR, serine ofwhich dopamine’s microdomain included protonablea (Figureswitch of S3). interactions At 55 ns withtorsions the serineswere observed at TM5 for for D 3dopamineR, since it isbounded known that to all dopamine DR, which is only included capable a amineswitch(Figure towards of S3). interactions At 3.32Asp 55 ns withtorsions was the observed. serines were observed at TM5 At 65 for nsfor Ddopamine 3dopamineR, since it is bounded known that to to alldopamine all DR, DRs which was is onlylocated included capable again a planar switchof interacting of interactions with two with of the the threeserines serines at TM5 [47]. for AtD3 R,60 since ns dopamine it is known is shiftedthat dopamine more to isthe only serine capable and in theofswitch interacting OBP of (Figureinteractions with S4). two with Smallof the the individualthreeserines serines at TM5 torsions [47]. for At D3 R,60 were sincens dopamine observed it is known is during shifted that dopaminethe more period to isthe only of serine 70–90 capable and ns (Figures ofaromatic interacting microdomain with two of(TM6) the three for allserines DRs [47].in a Atdifferent 60 ns dopamine manner. However,is shifted more only toat theD4 Rserine a strong and S4aromaticof and interacting S5). microdomain Interestingly, with two of(TM6) at the 95 three nsfor dopamineall serines DRs [47].in wasa Atdifferent strongly60 ns dopamine manner. involved However,is shifted in the more aromaticonly toat theD4 microdomainR serine a strong and (TM6) aromaticdirection microdomainof dopamine’s (TM6) protonable for all amineDRs in towards a different 3.32Asp manner. was However, observed. only At 65at nsD4 Rdopamine a strong directionaromatic microdomainof dopamine’s (TM6) protonable for all amineDRs in towards a different 3.32Asp manner. was However,observed. onlyAt 65at nsD4 Rdopamine a strong at alldirectionbounded DR, which toof alldopamine’s DRs was was then located protonable vanished again amine especiallyplanar towardsin the for OBP D3.32Asp3 (FigureR at 100was S4). ns. observed.Small The in largedividual At decrease65 nstorsions dopamine in were D4R binding boundeddirection toof all dopamine’s DRs was located protonable again amineplanar towardsin the OBP 3.32Asp (Figure was S4). observed.Small individual At 65 torsionsns dopamine were energyboundedobserved at 90 toduring nsall canDRs the bewas period explained, located of 70–90again by nsplanar the (Figure approximation in the S4 OBP and (FigureS5). ofInterestingly, S4). dopamine Small inatdividual to 95 3.32Asp ns dopamine torsions distance were was from the observedbounded toduring all DRs the was period located of 70–90 again nsplanar (Figure in the S4 OBPand (FigureS5). Interestingly, S4). Small inatdividual 95 ns dopamine torsions werewas serineobservedstrongly microdomain involved during the in (Figurethe period aromatic S5).of 70–90 microdomain ns (Figure (TM6 S4 and) at allS5). DR, Interestingly, which was thenat 95 vanished ns dopamine especially was stronglyobserved involved during thein the period aromatic of 70–90 microdomain ns (Figure (TM6 S4 and) at allS5). DR, Interestingly, which was thenat 95 vanished ns dopamine especially was stronglyfor D3R atinvolved 100 ns. in The the aromaticlarge decrease microdomain in D4R (TM6binding) at allenergy DR, whichat 90 nswas can then be vanished explained, especially by the forstrongly D3R atinvolved 100 ns. in The the largearomatic decrease microdomain in D4R (TM6binding) at energyall DR, whichat 90 nswas can then be vanished explained, especially by the 2.4.forapproximation Docking D3R at of100 Various ofns. dopamine The Ligands large to decrease to3.32Asp DR Models distancein D4R bindingfrom the energy serine microdomainat 90 ns can (Figurebe explained, S5). by the approximationfor D3R at 100 ofns. dopamine The large to decrease3.32Asp distancein D4R bindingfrom the energy serine microdomainat 90 ns can (Figurebe explained, S5). by the approximation of dopamine to 3.32Asp distance from the serine microdomain (Figure S5). approximationSince non-selective of dopamine agonistic to 3.32Asp activity distance was from already the coveredserine microdomain by dopamine (Figure docking, S5). chlorpromazine 2.4. Docking of Various Ligands to DR Models. 2.4. Docking of Various Ligands to DR Models. was2.4. chosen Docking as of Various a non-selective Ligands to DR antagonist Models. [61,62]. Herein, we also selected the following ligands: 2.4. DockingSince non-selectiveof Various Ligands agonistic to DR Models. activity was already covered by dopamine docking, SKF38393Since as selectivenon-selective D1R agonistagonistic [27 ,activity30] and SCH23390was already as Dcovered1-like DR by antagonist dopamine [31 ,63docking,], apomorphine chlorpromazineSince non-selective was chosen agonistic as a non-selective activity wa antagonists already [61,62]. covered Herein, by wedopamine also selected docking, the aschlorpromazine selective D2R agonist was chosen [60], 7-OH-DPATas a non-selective as selective antagonist D3R [61,62]. agonist Herein, [23], nemonapride we also selected as Dthe2R and D3R chlorpromazinefollowing ligands: was SKF38393 chosen as as selective a non-selective D1R agonist antagonist [27,30] and [61,62]. SCH23390 Herein, as Dwe1-like also DR selected antagonist the selectivefollowing antagonist ligands: SKF38393 [64] and as lastly selective haloperidole, D1R agonist due [27,30] to its and affinity SCH23390 for D asR[ D125-like]. ThisDR antagonist set of ligands was following[31,63], apomorphine ligands: SKF38393 as selective as selective D2R Dagonist1R agonist [60], [27,30] 7-OH-DPAT and SCH23390 as selective as4 D1-like D3R DR agonist antagonist [23], [31,63],following apomorphine ligands: SKF38393 as selective as selective D2R Dagonist1R agonist [60], [27,30] 7-OH-DPAT and SCH23390 as selective as D1 -likeD3R DR agonist antagonist [23], chosen[31,63],nemonapride as apomorphine example as D2 ofR and ligandsas Dselective3R selective with D different2R antagonist agonist DR [60], [64] selectivity and7-OH-DPAT lastly (Table haloperidole, as 1selective). The due obtained D to3R itsagonist affinity binding [23], for energies nemonapride[31,63], apomorphine as D2R and as Dselective3R selective D2R antagonist agonist [60], [64] and7-OH-DPAT lastly haloperidole, as selective due D to3R itsagonist affinity [23], for andnemonaprideD4 NoCR [25]. in This these asset D of clusters2R ligands and D are 3wasR selective summarized chosen antagonistas example in Figure [64] of ligands and4 (graphicallastly with haloperidole, different output DR due ofselectivity the to its other affinity (Table ligands for1). can be Dnemonapride4R [25]. This setas D of2R ligands and D 3wasR selective chosen antagonistas example [64] of ligands and lastly with haloperidole, different DR due selectivity to its affinity (Table for1). DThe4R [25].obtained This setbinding of ligands energies was andchosen NoC as examplein these clustersof ligands are with summarized different DR in selectivityFigure 4 (graphical (Table 1). foundTheD4R in[25].obtained the This appendix: bindingset of ligands energies Figure was S7). andchosen NoC as inexample these clustersof ligands are with summarized different DR in selectivityFigure 4 (graphical (Table 1). Theoutput obtained of the otherbinding ligands energies can beand found NoC in in the these appendix: clusters Figure are summarized S7). in Figure 4 (graphical outputThe obtained of the otherbinding ligands energies can be and found NoC in in the these appendix: clusters Figure are summarized S7). in Figure 4 (graphical output of the Tableother ligands 1. Ligands can usedbe found for molecular in the appendix: docking Figure and information S7). on their function. Table 1. Ligands used for molecular docking and information on their function. Table 1. Ligands used for molecular docking and information on their function. Table 1. LIGANDLigands used for molecular docking and FUNCTION information on their function. BP REFERENCES Table 1. LigandsLIGAND used for molecular docking andFUNCTION information on theirBP function.REFERENCES LIGAND FUNCTION BP REFERENCES LIGAND FUNCTION BP REFERENCES LIGAND EndogenousEndogenousFUNCTION agonist agonist of BP REFERENCES DOPAMINEDOPAMINE Endogenous agonist OBP OBP[47,52,65] [47,52,65] DOPAMINE Endogenousof allall DR DRagonist OBP [47,52,65] DOPAMINE Endogenousof all DR agonist OBP [47,52,65] DOPAMINE of all DR OBP [47,52,65] of all DR

Synthetic D3R 7-OH-DPAT SyntheticSynthetic D3 DR3R selective OBP [47,65,66] 7-OH-DPAT7-OH-DPAT selectiveSynthetic agonist D3R OBP OBP[47,65,66] [47,65,66] 7-OH-DPAT selectiveSyntheticagonist agonist D3R OBP [47,65,66] 7-OH-DPAT selective agonist OBP [47,65,66] selective agonist

APOMORPHINE D2R selective agonist OBP [47,52,65] APOMORPHINE D2R selective agonist OBP [47,52,65] APOMORPHINEAPOMORPHINE DD2RR selective selective agonist agonist OBP OBP [47,52,65] [47,52,65] APOMORPHINE D2R2 selective agonist OBP [47,52,65]

BROMOCRIPTINE D2R selective agonist OBP [47,65] BROMOCRIPTINE D2R selective agonist OBP [47,65] BROMOCRIPTINE D2R selective agonist OBP [47,65] BROMOCRIPTINEBROMOCRIPTINE DD2R2R selective selective agonist agonist OBP OBP [47,65] [47,65]

Molecules 2019, 24, 1196 8 of 26

Molecules 2019, 24, x FOR PEER REVIEW 8 of 26 Molecules 2019, 24, x FOR PEER REVIEW Table 1. Cont. 8 of 26 Molecules 2019, 24, x FOR PEER REVIEW 8 of 26 Molecules 2019, 24, x FOR PEER REVIEW 8 of 26 Molecules 2019, 24, x FOR PEER REVIEW Table 1. Cont. 8 of 26 Molecules 2019, 24, x FOR PEERLIGAND REVIEW Table 1. Cont. FUNCTION BP REFERENCES8 of 26 Molecules 2019, 24, x FOR PEER REVIEW Table 1. Cont. 8 of 26 Molecules 2019, 24, x FOR PEER REVIEW Table 1. Cont. 8 of 26 Molecules 2019, 24, x FOR PEER REVIEW Table 1. Cont. 8 of 26 Molecules 2019, 24, x FOR PEER REVIEW Table 1. Cont.“DIRTY DRUG”, 8 of 26 Molecules 2019, 24, x FOR PEER REVIEW Table 1. Cont.“DIRTY DRUG”, 8 of 26 Table 1. Cont.“Dirty“DIRTY MULTIPLE drug”, DRUG”, multiple CLOZAPINECLOZAPINE Table 1. Cont.“DIRTY MULTIPLE DRUG”, OBP OBP[47,65,67,68] [47, 65,67,68] CLOZAPINE Table 1. Cont.receptor RECEPTORMULTIPLE binding OBP [47,65,67,68] CLOZAPINE “DIRTYRECEPTORMULTIPLE DRUG”, OBP [47,65,67,68] Table 1. Cont.“DIRTY RECEPTORBINDING DRUG”, CLOZAPINE “DIRTYMULTIPLEBINDING DRUG”, OBP [47,65,67,68] CLOZAPINE RECEPTORMULTIPLE OBP [47,65,67,68] “DIRTYRECEPTORBINDING DRUG”, CLOZAPINE MULTIPLEBINDING OBP [47,65,67,68] CLOZAPINE “DIRTYRECEPTORMULTIPLE DRUG”, OBP [47,65,67,68] “DIRTYRECEPTORBINDING DRUG”, CLOZAPINE “DIRTYMULTIPLEBINDING DRUG”, OBP [47,65,67,68] CLOZAPINE RECEPTORMULTIPLE OBP [47,65,67,68] D2R/DRECEPTORBINDING3R selective NEMONAPRIDECLOZAPINE D2R/DMULTIPLE3R selective OBPOBP + SBP [38,47,55,65][47,65,67,68] CLOZAPINE D2RECEPTORR/DantagonistBINDING3R selective OBP [47,65,67,68] NEMONAPRIDE D2R/DRECEPTORBINDING3R selective OBP +OBP SBP + SBP [38,47,55,65] [38, 47,55,65] NEMONAPRIDE D2R/Dantagonistantagonist3R selective OBP + SBP [38,47,55,65] BINDING NEMONAPRIDE D2R/DantagonistBINDING3R selective OBP + SBP [38,47,55,65] NEMONAPRIDE D2R/Dantagonist3R selective OBP + SBP [38,47,55,65] NEMONAPRIDE D2R/Dantagonist3R selective OBP + SBP [38,47,55,65] NEMONAPRIDE D2R/Dantagonist3R selective OBP + SBP [38,47,55,65] NEMONAPRIDE D“Dirty2R/Dantagonist3R drug”,selective OBP + SBP [38,47,55,65] “Dirtyantagonist drug”, NEMONAPRIDESULPIRIDE Dmultiple2R/D3R selectivereceptor OBPOBP ++ SBPSBP [38,47,55,65] [47,65,66] NEMONAPRIDE D2“DirtyR/Dantagonist3R drug”,selective OBP + SBP [38,47,55,65] NEMONAPRIDESULPIRIDE “Dirtymultiple“Dirtyantagonist drug”, drug”,receptor multiple OBP + SBP [38,47,55,65] [47,65,66] SULPIRIDE multiplebinding receptor OBP + SBP [47,65,66] SULPIRIDE “Dirtyantagonistbinding drug”, OBP + SBP [47,65,66] SULPIRIDE multiplereceptor“Dirty drug”,receptor binding OBP + SBP [47,65,66] SULPIRIDE multiplebinding receptor OBP + SBP [47,65,66] “Dirtybinding drug”, SULPIRIDE multiple“Dirty drug”,receptor OBP + SBP [47,65,66] SULPIRIDE multiplebinding receptor OBP + SBP [47,65,66] SULPIRIDE multiple“Dirtybinding receptordrug”, OBP + SBP [47,65,66] “Dirtybinding drug”, SULPIRIDE multiple“Dirtybinding drug”,receptor OBP + SBP [47,65,66] SULPIRIDESCH23390 multipleD1R antagonist receptor OBP OBP + SBP [31,47,65,69] [47,65,66] SULPIRIDE multiplebinding receptor OBP + SBP [47,65,66] SCH23390 D1Rbinding antagonist OBP [31,47,65,69] SCH23390 D1Rbinding antagonist OBP [31,47,65,69] SCH23390SCH23390 DD1R1 Rantagonist antagonist OBP OBP [31,47,65,69] [31, 47,65,69] SCH23390 D1R antagonist OBP [31,47,65,69] SCH23390 D1R antagonist OBP [31,47,65,69] SCH23390 D1R antagonist OBP [31,47,65,69] SCH23390 D1R antagonist OBP [31,47,65,69] SKF38393SCH23390 D1RD selective1R antagonist agonis t OBP OBP [31, [31,47,65,69]47,65,70] SCH23390SKF38393 D1RD selective1R antagonist agonis t OBP [31,[31,47,65,69]47,65,70] SCH23390SKF38393 D1RD selective1R antagonist agonis t OBP [31,47,65,69][31,47,65,70] SKF38393 D1R selective agonist OBP [31,47,65,70]

SKF38393SKF38393 DD1R1R selective selective agonis agonistt OBP OBP [31,47,65,70] [31, 47,65,70] SKF38393 D1R selective agonist OBP [31,47,65,70] SKF38393 D1R selective agonist OBP [31,47,65,70] SKF38393 DD1R2 R/Dselective3R selective agonis t OBP [31,47,65,70] ETICLOPRIDE D2R/D3R selective OBP + SBP [66,37] SKF38393 D1R selectiveantagonist agonis t OBP [31,47,65,70] ETICLOPRIDESKF38393 DD1R2 R/Dselective3R selective agonis t OBPOBP + SBP [31, [66,37]47,65,70] ETICLOPRIDE 2 antagonist3 OBP + SBP [66,37] SKF38393 DD1RR/D selectiveR selective agonis t OBP [31,47,65,70] ETICLOPRIDE D2R/Dantagonist3R selective OBP + SBP [66,37] ETICLOPRIDE antagonist OBP + SBP [66,37] DD22R/DR/D3R 3selectiveR selective ETICLOPRIDE D2R/Dantagonist3R selective OBP +OBP SBP + SBP [66,37] [37,66] ETICLOPRIDE D2R/Dantagonistantagonist3R selective OBP + SBP [66,37] antagonist ETICLOPRIDE D“Dirty2R/D3R drug”,selective OBP + SBP [66,37] ETICLOPRIDE D2R/Dantagonist3R selective OBP + SBP [66,37] “Dirtyantagonist drug”, RISPERIDONEETICLOPRIDE Dmultiple2“DirtyR/D3R drug”,receptorselective OBPOBP+SBP + SBP [36,47] [66,37] RISPERIDONEETICLOPRIDE multipleantagonist receptor OBPOBP+SBP + SBP [36,47] [66,37] “Dirtyantagonistbinding drug”, RISPERIDONE multiple“Dirty drug”,receptor OBP+SBP [36,47] RISPERIDONE multiplebinding receptor OBP+SBP [36,47] “Dirtybinding drug”, RISPERIDONE multiple“Dirty drug”,receptor OBP+SBP [36,47] RISPERIDONE multiplebinding receptor OBP+SBP [36,47] “Dirty“Dirtybinding drug”, drug”, multiple RISPERIDONE multiple“Dirty receptordrug”, OBP+SBPOBP+SBP [36,47] [36,47] RISPERIDONE Partialmultiplereceptorbinding D2 Rreceptor agonist, binding OBP+SBP [36,47] Partial“Dirtybinding D2 Rdrug”, agonist, RISPERIDONE multiple“Dirty2 drug”,receptor3 OBP+SBP [36,47] PartialDbinding DR/D2R agonist,R ARIPIPRAZOLERISPERIDONE multipleD2R/D receptor3R OBPOBP+SBP + SBP [66,71][36,47] ARIPIPRAZOLERISPERIDONE Partialmultipleheterodimerbinding D2R receptor agonist, OBPOBP+SBP + SBP [66,71][36,47] PartialDbinding D2R/D2R agonist,3R ARIPIPRAZOLE heterodimerD2R/D3R OBP + SBP [66,71] ARIPIPRAZOLE Partialheterodimerantagonistbinding D2R agonist, OBP + SBP [66,71] PartialantagonistD 2DR/D2R agonist,3R ARIPIPRAZOLE heterodimerD2R/D3R OBP + SBP [66,71] ARIPIPRAZOLE Partialheterodimerantagonist D2R agonist, OBP + SBP [66,71] PartialPartialantagonistD 2DR/D D2R2 3agonist,RR agonist, ARIPIPRAZOLE heterodimerD2R/D3R OBP + SBP [66,71] PartialDantagonist2R D selective2R agonist, ARIPIPRAZOLE D R/DheterodimerD2R/DR heterodimer3R OBP +OBP SBP + SBP [66,71] [66,71] Partial2 Dheterodimerantagonist2R D 3selective2R agonist, HALOPERIDOLEARIPIPRAZOLE antagonist,D2R/D3R D 4R OBPOBP+SBP + SBP [47,65,67,72,73] [66,71] ARIPIPRAZOLE Dheterodimerantagonist2Rantagonist selective OBP + SBP [66,71] HALOPERIDOLE antagonist,Dantagonist2DR 2selectiveR/D3R D 4R OBP+SBP [47,65,67,72,73] HALOPERIDOLEARIPIPRAZOLE antagonist,heterodimerantagonist D 4 R OBPOBP+SBP + SBP [47,65,67,72,73] [66,71] Dheterodimerantagonistantagonist2R selective HALOPERIDOLE antagonist,Dantagonist2R selective D 4R OBP+SBP [47,65,67,72,73] HALOPERIDOLE antagonist,antagonist D 4R OBP+SBP [47,65,67,72,73] Dantagonist2R selective HALOPERIDOLE antagonist,2 D4R OBP+SBP [47,65,67,72,73] DantagonistR selective HALOPERIDOLE antagonist,DD2RR selective selective D4R OBP+SBP [47,65,67,72,73] HALOPERIDOLE antagonist,antagonist2 D 4R OBP+SBP [47,65,67,72,73] Dantagonist2R selective HALOPERIDOLE antagonist,antagonist,D2R selective D4 DR 4R OBP+SBPOBP+SBP [47,65,67,72,73] [47,65, 67,72,73] HALOPERIDOLESPIPERONE Affinityantagonist,antagonist for all D 4DRR OBPOBP+SBP + SBP [47,65,67,72,73] [47,65,66] HALOPERIDOLESPIPERONE Affinityantagonist,antagonistantagonist for all D 4 DRR OBPOBP+SBP + SBP [47,65,67,72,73] [47,65,66] SPIPERONE Affinityantagonist for all DR OBP + SBP [47,65,66] SPIPERONE Affinityantagonist for all DR OBP + SBP [47,65,66] SPIPERONE Affinity for all DR OBP + SBP [47,65,66] SPIPERONE Affinity for all DR OBP + SBP [47,65,66] SPIPERONE Affinity for all DR OBP + SBP [47,65,66] SPIPERONE Affinity for all DR OBP + SBP [47,65,66] SPIPERONE Affinity for all DR OBP + SBP [47,65,66] SPIPERONESPIPERONE AffinityAntagonistAffinity for for allon allDRall DR OBP + OBPSBP + SBP [47,65,66] [ 47,65,66] CHLORPROMAZINE Antagonist on all OBP [47,65,74] SPIPERONE AffinityDR for all DR OBP + SBP [47,65,66] CHLORPROMAZINE Antagonist on all OBP [47,65,74] CHLORPROMAZINE AntagonistDR on all OBP [47,65,74] CHLORPROMAZINE AntagonistDR on all OBP [47,65,74] DR CHLORPROMAZINE Antagonist on all OBP [47,65,74] CHLORPROMAZINE AntagonistDR on all OBP [47,65,74] CHLORPROMAZINEAbbreviations: DR-dopamine receptors, BP-binding pocket,Antagonist DROBP- onorthosteric all OBPbinding pocket,[47,65,74] SBP- CHLORPROMAZINE DR OBP [47,65,74] Abbreviations: DR-dopamine receptors, BP-binding pocket,Antagonist DROBP- onorthosteric all binding pocket, SBP- CHLORPROMAZINEsecondaryAbbreviations: binding DR-dopamine pocket. receptors, BP-binding pocket,Antagonist OBP- onorthosteric all bindingOBP pocket,[47,65,74] SBP- CHLORPROMAZINEAbbreviations:secondary binding DR-dopamine pocket. receptors, BP-binding pocket,Antagonist OBP-DR onorthosteric all OBPbinding pocket,[47,65,74] SBP- CHLORPROMAZINECHLORPROMAZINEAbbreviations:secondary binding DR-dopamine pocket. receptors, BP-binding pocket,Antagonist DROBP- onorthosteric all DR OBPbinding OBP pocket,[47,65,74] SBP- [47 ,65,74] secondaryAbbreviations: binding DR-dopamine pocket. receptors, BP-binding pocket, DROBP- orthosteric binding pocket, SBP- secondaryAbbreviations: binding DR-dopamine pocket. receptors, BP-binding pocket, OBP-orthosteric binding pocket, SBP- Abbreviations:secondary binding DR-dopamine pocket. receptors, BP-binding pocket, OBP-orthosteric binding pocket, SBP- secondaryAbbreviations: binding DR-dopamine pocket. receptors, BP-binding pocket, OBP-orthosteric binding pocket, SBP- secondaryAbbreviations: binding DR-dopamine pocket. receptors, BP-binding pocket, OBP-orthosteric binding pocket, SBP- Abbreviations:secondary binding DR-dopamine pocket. receptors, BP-binding pocket, OBP-orthosteric binding pocket, SBP- Abbreviations:secondary binding DR-dopamine pocket. receptors, BP-binding pocket, OBP-orthosteric binding pocket, SBP-secondary bindingsecondary pocket. binding pocket. Molecules 2019, 24, 1196 9 of 26

Molecules 2019, 24, x FOR PEER REVIEW 9 of 26 For 7-OH-DPAT we observed a low and stable binding energy upon binding to all DRs. For 7-OH-DPAT we observed a low and stable binding energy upon binding to all DRs. For For apomorphine, a decrease in the binding energy was determined for D2R at 65 ns (−11 kcal/mol), apomorphine, a decrease in the binding energy was determined for D2R at 65 ns (−11 kcal/mol), whereas an increase at 85 ns was shown for D4R(−9 kcal/mol). Stable binding energies around whereas an increase at 85 ns was shown for D4R (−9 kcal/mol). Stable binding energies around −10 −10 kcal/mol were observed for DR:nemonapride complexes, however a massive increase was kcal/mol were observed for DR:nemonapride complexes, however a massive increase was observed observed for the D5R at 100 ns. For SCH23390, but not for SKF38393 the binding energy was stable for the D5R at 100 ns. For SCH23390, but not for SKF38393 the binding energy was stable over time at over time at −9 kcal/mol for all DRs. The binding energy of SKF38393 at D2R and D4R increased −9 kcal/mol for all DRs. The binding energy of SKF38393 at D2R and D4R increased at 85 ns. at 85 ns. Haloperidole displayed the most interesting docking-profile: while the binding energies of Haloperidole displayed the most interesting docking-profile: while the binding energies of DRs were DRs were stable at −10 kcal/mol, only for D4R a massive increase was observed at 55 ns and 80–90 ns stable at −10 kcal/mol, only for D4R a massive increase was observed at 55 ns and 80–90 ns into the into the positive range, meaning these binding positions were extremely unfavorable for haloperidole. positive range, meaning these binding positions were extremely unfavorable for haloperidole. Lastly, Lastly, the chlorpromazine binding energy was increased only for D1R at 70 ns up to −3 kcal/mol. the chlorpromazine binding energy was increased only for D1R at 70 ns up to −3 kcal/mol. Similar to dopamine binding, the NoC of 7-OH-DPAT decreased at all DRs from 0 to 65 ns. Similar to dopamine binding, the NoC of 7-OH-DPAT decreased at all DRs from 0 to 65 ns. For For apomorphine, the lowest binding energies were obtained for D1R and D2R. Lesser NoC were apomorphine, the lowest binding energies were obtained for D1R and D2R. Lesser NoC were counted counted for nemonapride in total at all DRs (max. 30 at 85 ns for D2R). The NoC for SKF38393 were for nemonapride in total at all DRs (max. 30 at 85 ns for D2R). The NoC for SKF38393 were the lowest the lowest over 70–85 ns period for D1R, D2R and D3R. In contrast to the BE of haloperidole, the NoC over 70–85 ns period for D1R, D2R and D3R. In contrast to the BE of haloperidole, the NoC was found was found to be stable over time except for D1R with up to 40 conformations at 60 ns. In addition, to be stable over time except for D1R with up to 40 conformations at 60 ns. In addition, most most conformations were counted for the D4R especially at 0–70 ns for haloperidole. conformations were counted for the D4R especially at 0–70 ns for haloperidole.

7-OH-DPAT 7-OH-DPAT -12 50 D1R D1R D2R 40 D2R -10 D3R D3R 30 D4R D4R 20 -8 D5R D5R 10 Binding energyBinding [kcal/mol] -6 Numberofconformations 0 0 556065707580859095100 0 556065707580859095100 Time points [ns] Time points [ns]

Apomorphine Apomorphine -12 50 D1R D1R D2R 40 D2R -10 D3R D3R 30 D4R D4R 20 -8 D5R D5R 10 Binding energy[kcal/mol] -6 conformations of Number 0 0 556065707580859095100 0 556065707580859095100 Time points [ns] Time points [ns]

Nemonapride Nemonapride -12 50 D1R D1R 40 -10 D2R D2R D3R D3R 30 -8 D4R D4R D5R 20 D5R -6 10 Bindingenergy [kcal/mol] -4 Numberofconformations 0 0 556065707580859095100 0 556065707580859095100 Time points [ns] Time points [ns]

SCH23390 SCH23390

-12 50 D1R D1R -10 D2R 40 D2R D3R D3R -8 30 D4R D4R -6 D5R 20 D5R

-4 10 Binding energyBinding[kcal/mol] -2 Numberof conformations 0 0 556065707580859095100 0 556065707580859095100 Time points [ns] Time points [ns]

Figure 4. Cont.

Molecules 2019, 24, x FOR PEER REVIEW 10 of 26

Molecules 2019, 24, 1196 10 of 26 Figure 4. Cont.

SKF38393 SKF38393

-12 50 D1R D1R D2R 40 D2R -10 D3R D3R 30 D4R D4R 20 -8 D5R D5R 10 Bindingenergy [kcal/mol] -6 Number of conformations 0 0 556065707580859095100 0 556065707580859095100 Time points [ns] Time points [ns]

Haloperidol Haloperidol 50 -12 D1R D1R -10 40 -8 D2R D2R -6 D3R D3R -4 30 -2 D4R D4R 0 20 D5R 2 0 556065707580859095100 D5R 4 10 6 8 Number ofconformations Bindingenergy [kcal/mol] 10 Time points [ns] 0 0 556065707580859095100 Time points [ns] Chlorpromazine Chlorpromazine

-12 50 D1R D1R -10 D2R 40 D2R D3R D3R -8 30 D4R D4R -6 D5R 20 D5R

-4 10 Binding energy [kcal/mol] -2 Number of conformations 0 0 556065707580859095100 0 556065707580859095100 Time points [ns] Time points [ns]

Figure 4. Results of the molecular docking of 7-OH-DPAT, apomorphine, nemonapride, SCH23390, FigureSKF38393, 4. Results haloperidole of the andmolecular chlorpromazine docking of for 7-OH-D all DRPAT, subtypes apomorphine, at time points nemonapride, [ns]. The average SCH23390, of the SKF38393,three lowest haloperidole binding energies and chlorpromazine of dopamine were for calculated all DR subtypes in the left at plots.time points The mean [ns]. of The the average number of of theconformations three lowest of binding the three energies clusters of with dopamine the lowest were binding calculated energies in werethe left plotted plots. for The each mean time of point the numberand receptor of conformations (right plot). of the three clusters with the lowest binding energies were plotted for each time point and receptor (right plot). We also calculated the distance between the center of mass of the ligand and the alpha carbon of the bindingWe also pocketcalculated residues. the distance Overall between results the of all center ligand-residue of mass of the measurements ligand and the (Figure alpha5 )carbon showed of thethat binding 3.32Asp pocket was the residues. closest Overall residue forresults the majorityof all ligand-residue of ligands. Themeasurements ligand center (Figure of mass-residue 5) showed thatalpha 3.32Asp carbon was distance the closest was lower residue than for 7–8 the Å, majority particularly of ligands. for D 1TheR (<6 ligand Å). We center noted of anmass-residue increase in alphathe distance carbon betweendistance 3.32Aspwas lower and than several 7–8 Å, ligands particularly for D4 R.for The D1R distances (< 6 Å). We between noted 3.32Aspan increase and in both the distanceSKF38393 between and SCH23390 3.32Asp ligands and several were larger ligands at for for D D3R,4R. D 4TheR and distances D5R, but between also D2 R.3.32Asp This effect and mightboth SKF38393occur due and to the SCH23390 fact, that ligands SCH23390 were and larger SKF38393 at for D are3R, reportedD4R and toD5 beR, but D1R-selective also D2R. This [30, 63effect]. Subtype might occurspecific due tendencies to the fact, were that observed SCH23390 for and the SKF38393 serine microdomain. are reported 5.42Ser to be D was1R-selective shown to [30,63]. be most Subtype distant specificat D1-like tendencies receptors were and 5.43Serobserved for for D 2theR and serine D3R microdomain. (D2-like). These 5.42Ser differences was shown are less to be accentuated most distant for atdopamine, D1-like receptors 7-OH-DPAT, and 5.43Ser apomorphine for D2R and and bromocriptine. D3R (D2-like). These differences are less accentuated for dopamine,For 7-OH-DPAT, 7-OH-DPAT, a known apomorphine D3R selective and bromocriptine. agonist, distances between ligand and the defined pocket are higherFor 7-OH-DPAT, for D1-like receptorsa known andD3R distinctive selective agonist, residue betweendistances D between2-like seems ligand to be and 6.52Phe, the defined that is pocketcloser toare the higher ligand for on D D1-like3R. The receptors same pattern and distinctive was visible residue with apomorphine,between D2-like a selectiveseems to Dbe2R 6.52Phe, agonist, thatwhere is closer distances to the in Dligand1-like on are D higher,3R. The although same pattern distinction was visible within with D2-like apomorphine, family is less a selective pronounced. D2R agonist,Clozapine, where sulpiride distances and in risperidone D1-like are are higher, known although as “dirty distinction drugs” because within ofD2-like their family non-selective is less pronounced.profile, and for Clozapine, that reason sulpiride none of and these risperidone ligands showed are known distinctive as “dirty differences drugs” betweenbecause of DR their subtypes. non- selectiveLikewise, profile, residues and 3.32Asp for that and reason 3.33Val/Ile none wereof these the closestligands to showed clozapine distinctive in all five differences subtypes, suggesting between DRthat subtypes. these residues Likewise, are crucial residues for 3.32Asp this ligand’s and binding.3.33Val/Ile Haloperidole, were the closest categorized to clozapine as D2 Rin selective all five subtypes,antagonist suggesting with some activitythat these on D 4residuesR, has distinctive are crucial differences for this between ligand’s D 1binding.-like and DHaloperidole,2-like family, categorizedbeing closer as to D the2R selective second (although antagonist within with some D2-like activity family on there D4R, ishas no distinctive great differences differences on distancesbetween Dpattern).1-like and Spiperone D2-like family, and chlorpromazine being closer tohave the second affinity (although for all DR within subtypes, D2-like which family agrees there with is no the great lack differencesof significant on differences distances pattern). in the measured Spiperone distances. and chlorpromazine Finally, nemonapride have affinity and eticlopride, for all DR subtypes, described Molecules 2019, 24, x FOR PEER REVIEW 11 of 26 Molecules 2019, 24, 1196 11 of 26 which agrees with the lack of significant differences in the measured distances. Finally, nemonapride and eticlopride, described as D2R/D3R selective antagonists, were located closest to D2-like DR as D2R/D3R selective antagonists, were located closest to D2-like DR residues compared to the D1-like residues compared to the D1-like DR, however it seemed as these two ligands demonstrated DR, however it seemed as these two ligands demonstrated preference for D4R. preference for D4R.

D1R Å D2R Å 2.57Val 4 2.57Val 4 3.32Asp 5 3.32Asp 5 3.33Val/Ile 3.33Val/Ile 6 6 3.36Cys 3.36Cys 7 5.42Ser 7 5.42Ser 5.43Ser 8 5.43Ser 8 5.46Ser 9 5.46Ser 9 6.48Trp 10 6.48Trp 10 6.51Phe 6.51Phe 11 11 6.52Phe 6.52Phe 12 6.55His/Asn 12 6.55His/Asn 7.43Tyr/Trp blank 7.43Tyr/Trp blank

l e T e e le 0 e n ne ide 90 d o ne ine AT ine ne de ide ol dol ine ride 3 393 z ido o pi 39 393ride i onez pti pinepr i pri a r r azine m DP pt pri 3 8 r r -DPArphin a a p o - i 2 3 op ridone H o ul F38 l eridone ipe orphine l praz pe opami K ic ipr lopep om opa ocr onaSulpir c pe pi oma O Cloz S ip -OH Cloza SKF ti s pi D SCH23S Et isp a S pr D 7 m SCH E ri S 7- emon R Ar H pomom Ri A Halope romocriN or Ne ApomB hl A Br hlorpr C C

D3R Å D4R Å 2.57Val 4 2.57Val 4 3.32Asp 5 3.32Asp 5 3.33Val/Ile 3.33Val/Ile 6 6 3.36Cys 3.36Cys 7 5.42Ser 5.42Ser 7 5.43Ser 8 5.43Ser 8 5.46Ser 9 5.46Ser 9 6.48Trp 10 6.48Trp 10 6.51Phe 6.51Phe 11 11 6.52Phe 6.52Phe 12 6.55His/Asn 6.55His/Asn 12 7.43Tyr/Trp blank 7.43Tyr/Trp blank

e T e 0 le e T e 0 3 ol in ne ine n ide de 9 ide o in ine A n ne ide 9 9 ide hi pi ri 3 idolonez P zoleid one m DPA pr i 3 r r a m apinepr 83 donea r r azine p ript za a lp opr ripti 233 opr H- or c n u H2 F38393l praz pe orphi oz ipr pe m opa lo C K ic lopepi om opa onaSulpirideKF3icl peri pi -O C S ipi -OH-D Cl S t s ip D om S S Et speridonea S pr D 7 m SCH E r S 7 emo Ri Ar H r Ri A Halope romo o Ne lorpro Ap B N ApomBromoc h Chl C

D5R Å 2.57Val 4 3.32Asp 5 3.33Val/Ile 6 3.36Cys 7 5.42Ser 5.43Ser 8 5.46Ser 9 6.48Trp 10 6.51Phe 11 6.52Phe 12 6.55His/Asn 7.43Tyr/Trp blank

T e 3 e ine A ne ine in de de id ole t ri ri 390 39 onezine p 3 r a am phirip ap a lpi 2 opr ridoneraz e H-DPor l e p p O oc on Su KF38 p pi loperidolpi Dop- Clozm SCHS Eticis S 7 om R Ari Ha r Ne ApomB hlorprom C Figure 5. Summary of the distances between ligands and residues used in molecular docking for allFigure DR subtypes. 5. Summary For of each the distances ligand-residue-distance between ligands [Å],and weresidues calculated used in the molecular mean of docking all time for points all DR subtypes. For each ligand-residue-distance [Å], we calculated the mean of all time points of the of the conformational models (11) of the three best docked clusters ranked by binding energy conformational models (11) of the three best docked clusters ranked by binding energy [kcal/mol] [kcal/mol] Noteworthy is that not all ligands were set to interact with all residues shown in the Noteworthy is that not all ligands were set to interact with all residues shown in the x-axis in the x-axis in the molecular docking. (e.g., only clozapine and aripiprazole were set to interact with molecular docking. (e.g. only clozapine and aripiprazole were set to interact with 3.33Val). The 3.33Val). The distances are color coded: while dark colors indicate short distances, light colors indicate distances are color coded: while dark colors indicate short distances, light colors indicate wider wider distances. distances. 2.5. The Type of Pairwise Interactions Between Receptor Amino-Acids and Ligand is Relevant for Binding 2.5. The Type of Pairwise Interactions Between Receptor Amino-Acids and Ligand is Relevant for Binding. In-house scripts using the BINANA algorithm (software used in other non-GPCR In-house scripts using the BINANA algorithm (software used in other non-GPCR studies [39,75– studies [39,75–78]) were constructed to identify the type of interactions established between the ligands 78]) were constructed to identify the type of interactions established between the ligands and binding and binding pocket amino-acids [39]. We measured close contacts between receptor and ligands below or equal 2.5 Å and below or equal 4.0 Å, hydrogen bonds (HB), hydrophobic contacts (hydrocontacts) Molecules 2019, 24, 1196 12 of 26 and salt-bridges (SB) as well as π-interactions, further subdivided into cation-π-interactions (cat-π), aromatic superpositions (π-π-stack) and perpendicular interactions of aromatic rings also referred to as edge-face-interactions (T-stack) [39]. For a first overview, all interactions despite their type and ligand were summarized and compared between the DR-subtypes (Results section at SI and Figure S8). Moreover, detailed mapping of pairwise interactions for each receptor is displayed in Figure6. Figures S9–S23 show the change of interaction pattern over time for each ligand. Furthermore, the pairwise analysis highlighted the role of key receptor residues. By assorting those for each ligand at all DRs (time points summarized), patterns but also unique receptor-ligand interactions were highlighted (Tables S10–S14).

2.6. 2.5 Å-Interactions 2.5 Å-interactions, very short (closer) contacts are especially relevant for ligand binding and are described in more detail herein. For dopamine the number of these interactions increased for D1-like DRs, while for 7-OH-DPAT the highest number of interactions observed in total only occurred for D3R. For bromocriptine, 2.5 Å-interactions were significantly higher for D4R. Also, haloperidole seemed to have a higher number of established interactions with D4R as well as eticlopride. Only risperidone had a higher number of interactions with D2R. Chlorpromazine had the lowest number of compared to all ligands with no preference for any DR-subtype. All in all, 2.5 Å-interactions seemed to be particularly relevant for the ligand binding to D4R.

2.7. Hydrogen Bonds and Hydrophobic Contacts Charge-reinforced hydrogen bonds are reported to be much stronger than the neutral hydrophobic contacts [79]. Moreover, it was reported that HBs determine the specificity of receptor-ligand binding [79]. Nevertheless, hydrophobic contacts also contribute to ligand binding, and a balance between HB and hydro contacts is required for drug-like molecules [79]. It was not surprising that a large number of hydro contacts was observed for all ligands, while HB were less common. Hydro contacts were preferably formed for D1R and achieved their lowest value for D3R. These contacts were particularly relevant for one ligand, bromocriptine (Figure6). Moreover, a large hydrophobic network involving conserved and non-conserved residues of all TMs were found for all DRs (less pronounced for D5R). The “dirty drugs” were the second in line with the highest number of hydro contacts. Most interesting were the HB interactions. For dopamine a different set-up was presented at each DR. While the D2-like DRs and D5R HB were formed by the serine microdomain (5.42Ser, 5.43Ser and 5.46Ser), for D1R the serine microdomain was not involved at all. 3.32Asp appeared as interaction partner for all DRs. For D5R, an HB between 5.38Tyr and dopamine was stressed out as unique for all ligands. However, 5.38Tyr was found at the D4R to form HB with 7-OH-DPAT. Not more than 2 HB were found at any DR bounded to 7-OH-DPAT. Lastly, chlorpromazine does not seem to form any HB in any DR complex.

2.8. Salt-Bridges Most stable SB interactions were unsurprisingly achieved by dopamine for all DR sub-types. 7-OH-DPAT, SB were found for D1R (three in total), while for the other subtypes, contacts ranged between one and three over time. The same trend was observed for nemonapride and SKF38393. SCH23390 formed the highest number of SB with D5R and with D2R between 70 and 85 ns. Haloperidole seemed to establish a higher number of SB with D1-like DR and D2R, while none were formed with D3R and D4R. Spiperone seemed to preferably form SB with D1-like DRs. The following ligands did not form any salt-bridges at any time point: apomorphine, bromocriptine, clozapine, risperidone, aripiprazole and chlorpromazine. Undoubtedly, 3.32Asp was always involved in the establishment of SB in all DRs. However, at D1R, 74Pro located on ECL1 appeared also to establish relevant SB interactions. In addition, Molecules 2019, 24, 1196 13 of 26

D3R SB-bonding for spiperone was found to occur involving 1.44Leu and 75Ser (ECL1) rather than 3.32Asp. All in all, salt-bridges were found to be highly conserved regarding the residues involved.

2.9. Cat-π- and π-π-Stacking Interactions Cationic-π and π-π-stacking are considered as natural key non-covalent interactions [80]. They are important as solitary effects, but also their interplay omnipresent in many biological systems [81]. In the DR-ligand system frequent oscillations between different receptor conformations were noted for some ligands, depicted in Figures S9–S13. Dopamine, for example, showed the highest cat-π-interactions for D2R, oscillating from 2–4 interactions/time point. Cation-π-interactions seemed to be more relevant for D4R and were less common and mainly formed by conserved residues on TM6 (6.42Gly, 6.31Thr, 6.30Glu, 6.39Val) for D1R. Bromocriptine (3.28Trp, 6.51Phe), nemonapride (6.48Trp, 6.51Phe, 6.52Phe), sulpiride (2.61Lys, 6.48Trp, 6.51Phe) and SCH23390 (6.48Trp) showed one cat-π-contacts to D5R each. For risperidone, cat-π-interactions were mainly formed with D4R, while π-π-stacking was mostly related to D3R complexes. Aripiprazole seemed to preferably form cat-π-interactions with D4R, while increasing π-π-stacking-interactions were observed with D1R between 65 and 80 ns. Haloperidole seemed to prefer π-π-stacking-interactions with D2R, maybe important for its selectivity towards this receptor. For chlorpromazine, no cat-π-interactions were observed at D1-like DRs (D1R and D5R), while many interactions were counted with D2R between 65 and 75 ns, with D3R at 95 ns and with D4R at 60 ns. The π-π-interactions were rather rare compared to the other interaction types. Some ligands did not form π-π-stacking interactions with DR subtypes (e.g., D1R binding to dopamine, 7-OH-PAT and sulpiride; D2R binding to sulpiride either, D4R binding to eticlopride and haloperidole; D5R binding to nemonapride). It was also obvious that the residues of the aromatic microdomain (6.48Trp, 6.51Phe, 6.52Phe, 6.55His) were responsible for the majority of ligands interactions to all DRs. However, different residue partners were determined for π-π- compared to T-stacking such as residues from TM5 (5.38Tyr, 5.47Phe). For aripiprazole, residues 7.43Tyr (D2R-D4R) and 7.34Thr (D1R) seemed also to be important for this type of interaction. Most interesting was the interaction pattern for sulpiride: while for D1R and D2R no π-π-stacking was detected, for D3R and D5R only a few residues seemed to be relevant (2.43Val, 2.44Val, 2.48Val, 38Thr, 5.38Phe, 6.51Phe, 6.52Phe for D3R; 3.28Trp and 6.48Trp for D5R) while for D4R, 27 residues from all TMs were involved in contact network formation. This may be explained by the different possible binding poses of sulpiride on the different D4R conformations.

2.10. T-Stacking Interactions T-stacking-interactions were similar to cat-π- and π-π-interactions, yet more frequent fluctuations in the number of interacts between ligands and receptor were observed in total. Especially for risperidone, which showed the highest number of T-stacking-contacts, preferably with D2R. Haloperidole and spiperone also seemed to have a D2R-preference, while chlorpromazine formed a large number of interactions with D5R. Despite the ligand, T-stack-contacts involved mainly conserved residues (6.39Val, 6.42Gly, 6.43Val) or residues from the aromatic microdomain (6.48Trp, 6.51Phe, 6.52Phe, 6.55His). An exception was bromocriptine and sulpiride for D2R, haloperidole for D4R and spiperone for D5R. Unique interactions were found for risperidone binding to D4R with 6.44Phe and for chlorpromazine binding to D1R with 6.30Glu. However, other residues from other TMs were also involved in forming T-stack-contacts: for example, 7-OH-DPAT unique interaction with 2.47Ala and SKF38393 with 35Ala (ICL1) were found at D3R. For risperidone, a unique interaction with 231Phe (ICL3) was determined for D1R. Whereas for spiperone 1.35Tyr and 159Ile (ECL2) seemed to be relevant for D4R, 2.14Tyr was relevant for chlorpromazine coupling. However, TM7 residues were also involved in T-stack-formation: 7.34Thr (D1R) and 7.35Tyr (D2-like)/7.35Phe(D5R), 7.43Tyr(D2-like). Residues on TM2 were also relevant for T-stack-formation (2.41Tyr, 2.43Val, 2.45Ser, 2.46Leu, 2.47Ala, 2.50Asp) but only for D3R. For D4R and D5R, only residues from TM6 and TM7 were involved in T-stack-contacts, except for SKF38393 where 5.47Phe was Molecules 2019, 24, 1196 14 of 26 Molecules 2019, 24, x FOR PEER REVIEW 14 of 26 established meaningful interactions with nemonapride, sulpiride, SCH23390, aripiprazole and relevant for binding to D R. Lastly, for D R and D R TM3 (3.28Trp(D R)/3.28Phe(D R)) residues spiperone. Although these4 residues (especially1 on TM22 and TM7) are more1 related to the2 SBP than to also established meaningful interactions with nemonapride, sulpiride, SCH23390, aripiprazole and the OBP (herein TM6 is the most relevant TM), contact formation was also observed for smaller spiperone. Although these residues (especially on TM2 and TM7) are more related to the SBP than ligands (7OH-DPAT, SCH23390, SKF38393). It was not expected that these ligands would access the to the OBP (herein TM6 is the most relevant TM), contact formation was also observed for smaller SBP. Noteworthy is also the fact, that dopamine exclusively formed T-stack-contacts with the ligands (7OH-DPAT, SCH23390, SKF38393). It was not expected that these ligands would access the conserved aromatic microdomain for all DR. Finally, it was also obvious that the variety of T-stack- SBP. Noteworthy is also the fact, that dopamine exclusively formed T-stack-contacts with the conserved contacts was also limited by the number of aromatic rings of the ligand (e.g. dopamine only contacted aromatic microdomain for all DR. Finally, it was also obvious that the variety of T-stack-contacts was 3 different sequential residues). In brief, our results also pinpoint for the fact that T-stacking- also limited by the number of aromatic rings of the ligand (e.g., dopamine only contacted 3 different interactions seem to be relevant for large ligands, primary in antagonists binding than in agonists sequential residues). In brief, our results also pinpoint for the fact that T-stacking-interactions seem to case. be relevant for large ligands, primary in antagonists binding than in agonists case.

D1R D2R 2.5 Å high 2.5 Å high > 100 > 100 4 Å 75 - 100 4 Å 75 - 100 Cat-π 50 - 75 Cat-π 50 - 75 25 - 50 25 - 50 HB 10 - 25 HB 10 - 25 Hydrocontacts 8 - 10 Hydrocontacts 8 - 10 6 - 8 6 - 8 π-π-stack 4 - 6 π-π-stack 4 - 6 SB 2 - 4 SB 2 - 4 1 - 2 1 - 2 T-stack 0 - 1 T-stack 0 - 1

e 0 3 e e le le e e 0 e e le le e in AT ine rde 9 9 id n o ne ne AT in in ide 93 id n o o ne in hinet i r z o P t pine 39 r o m DP p ip p p ido a ido mi p pr 3 p do az id az - r 383 r r r a rph za 2 o i r r er pa H or c ul lo ip iper mazine p cri na H l er p ip o onaprideS c pe o o SulpirdeKF383c p i pe p Do -O m Clozapine SKF spe ip lo r mo Clo s lo rom 7 m SCH233Eti i r a Sp p D o mo mo SC S Eti i S p o R A r 7-OH-Do e R Arip a r r Nem H lo H lo ApoB Ap Br N Ch Ch

D3R D4R 2.5 Å high 2.5 Å high > 100 > 100 4 Å 75 - 100 4 Å 75 - 100 Cat-π 50 - 75 Cat-π 50 - 75 25 - 50 25 - 50 HB 10 - 25 HB 10 - 25 Hydrocontacts 8 - 10 Hydrocontacts 8 - 10 6 - 8 6 - 8 π-π-stack 4 - 6 π-π-stack 4 - 6 SB 2 - 4 SB 2 - 4 1 - 2 1 - 2 T-stack 0 - 1 T-stack 0 - 1

e e e e 0 3 e le e e T e e e e e 3 e e le le e e in AT in in in 9 9 n o ole in n A in id d 90 id o o n in h ride 3 3 o z z P hin t pin ir 3 39 r on z z DP pt p 3 8 pride id mi D pr 3 8 d id ro a - rp i ap a lpirde 3 o rid ra rp ip za 2 3 op ri ra e pam cr z n H2 F l p er iperonema pa H- na H F l er ip m o o lo Su K p o o Sulp K c ip p OH C tic ipi p O mo ti ip p ro D - om mo C mo S S E isper lo S D - Cl mo SC S E isper S 7 p e R A a rpro 7 e R A rp H lo N Halo lo A Bro N h ApoBromocr C Ch

D5R 2.5 Å high > 100 4 Å 75 - 100 Cat-π 50 - 75 25 - 50 HB 10 - 25 Hydrocontacts 8 - 10 6 - 8 π-π-stack 4 - 6 SB 2 - 4 1 - 2 T-stack 0 - 1

T e e e e 0 e e le e ine A ine in id d 9 id P t r ir 3 r on zole zine m hin p 3 d a a a D ip ap ap 2 i r ridoeron p r z ul F38393lop r e H- orp c lo S c ip ip O m o K i pe p p rom Do - m C mon SCHS Et s ri lop S p 7 e Ri A a r N H ApoBro hlo C Figure 6. Interaction types counted for each ligand at DR-subtypes. Data are summarized for each Figureligand at6. allInteraction time points. types Total counted numbers for ofeach the ligand contacts at forDR-subtypes. each interaction Data typeare summarized are color-coded: for each few ligandinteractions at all weretime points. colored Total white, numbers while many of the interactions contacts for were each coloredinteraction dark. type Grey are cells color-coded: indicate thatfew interactionsthese values were are outside colored the white, scale, while which many was onlyinteractions the case were for bromocriptine colored dark. at Grey the Dcells4R withindicate 360 fourthat Å-interactions.these values are outside the scale, which was only the case for bromocriptine at the D4R with 360 four Å-interactions.

3. Discussion

One of the major research efforts in the research of dopamine receptors is the design of DR- subtype selective ligands [82]. However, most predictive studies have been performed on D2R ligand Molecules 2019, 24, 1196 15 of 26

3. Discussion One of the major research efforts in the research of dopamine receptors is the design of DR-subtype selective ligands [82]. However, most predictive studies have been performed on D2R ligand specificity, as this receptor is the most crucial in neurotransmission [17,57,83]. Herein, we present a comprehensive in silico approach, which reveals important interactions between DRs key residues and ligands in a more detailed way when compared with available literature [55,57,59,60,84,85].

3.1. Validation of the In Silico Pipeline Homology modeling and TM definition of all DR subtypes showed that there were smaller structural differences among the “classical” TMs (TM3, TM5, TM6), which are known to be key for ligand binding. Yet, as expected, structural differences between the subtypes were observed in the intracellular and extracellular loops, where some are important for ligand binding (ECL2) or for intracellular signaling (ICL2 and ICL3) [86]. This was particularly true for D1-like receptors, due to their much larger ICL3. Although no crystal structure was available for the D1-like DRs, the high sequence similarity among all DR helped to find suitable models for molecular docking. Validating the docking performance by low binding energies and high NoC by cluster also showed that the homology modeling-docking approach was suitable and reproducible. In fact, the combination of the different software and in-house scripting resulted in a straightforward in silico approach which can certainly be applied for studying other GPCRs. Data is also in line with experimental information, which corroborates the conceptual framework of this analysis protocol [47].

3.2. Pairwise Interactions Analysis Was Able to Determine Key Amino-Acids and Types of Interaction

A clear D2-like selectivity or binding preference was only found for apomorphine, while for others either D2R and D5R seemed to form a lower number of 4 Å-interactions such as nemonapride (D2R/D3R-antagonist [87]), SCH23390 (D1-like antagonist [88]), SKF38393 (D1R-antagonist [30]) or D1R and D4R were highly preferred (higher number of meaningful interactions). In other cases, such as for eticlopride (D2R/D3R antagonist [37]) and spiperone (D2R-antagonist [64]), the D3R was the least attractive DR for interaction. It was shown that the “classical” conserved residues e.g., 3.32Asp, the serine microdomain 5.42Ser, 5.45Ser, 5.46Ser and the aromatic microdomain 6.48Trp, 6.51Phe, 6.52Phe, 6.55His were relevant for all ligands and formed specific interactions, electrostatic (cat-π, π-π, T-stack), salt-bridges and hydrogen bonds. These residues were omnipresent in all our analyses. Yet, the distances for the most conserved OBP residues (3.32Asp, serine residues and 6.48Trp), distinct differences were observed between agonists and antagonists. For example, dopamine was constantly close to OBP, indicating its receptor activating properties as described by Floresca and Schnetz [47], while risperidone was found distant from these residues according to its antagonistic properties. This was also the case for the other antagonists such as haloperidole, nemonapride and the biased ligand aripiprazole. In addition, we described other TM residues involved in binding of these ligands, as previously described by Kalani et al. for D2R[57]. It was not surprising that the “classical” TMs, e.g., TM3, TM5, TM6 and TM7 were involved in many different interaction types. TM3 residues such as 3.35Cys, 3.36Ser, 3.33Val or 3.33Ile and 3.39Ser were often found forming different interactions with different ligands. This was also in concordance with previous studies regarding the involvement of other conserved residues on TM2 and TM7 (and TM3) [57,82,83,85], which was also described as part of a SBP only assessable for ligands with piperazine-moieties [59]. Residues on TM4 were not contributing to receptor-ligand interaction, except for D4R complexes. By comparing large ligands such as spiperone or haloperidole with rather compact ligands such as dopamine, SCH23390 or clozapine, it was possible to point out a larger number of TM1 and TM2 residues involved in establishing meaningful interactions. Author’s had already hypothesized that these residues could belong to a SBP, only accessed by large ligands [57,85]. Furthermore, there was a clear higher network contact formation with D4R. Except for that fact that Molecules 2019, 24, 1196 16 of 26

the D4R is physiologically distant compared to D2R and D3R[82], no further explanation could be found for this trend. A systematic study by De Freitas and Schapira [89] showed that the most frequent type of non-covalent interactions for protein-ligand complexes were hydrophobic contacts, followed by hydrogen bonding, π-stacking, salt-bridges, amide-stacking (corresponds to T-stack) and lastly cation-π-stacking. The same ranking of frequency of interaction type was found in our study. As also described by Davis and Teague [79] hydrophobic contacts are the most common type of receptor-ligand-interactions as they not only enhance binding affinity but also are sometimes favored over tight, charged hydrogen bonds [79]. In addition, they can be formed with different ligand-atoms such as carbons, halogens or sulphurs [89]. As reviewed in Davis and Teague [79] most docking studies fail to count in the hydrophobicity for their ligands. However, the balance between polarity (causing hydrogen bonds) and lipophicity (causing hydrophobic contacts) is the main drive to make a ligand “drug-like” [79]. Our study was successful to determine not only the hydrogen bonds but also the large hydrophobic network of each “drug-like” ligand (as well as of the marketed drugs). Hydrophobic contacts appeared to form a huge network of conserved and non-conserved residues that stabilized ligand positions during binding. This network was spread across TM2-TM3-TM7. Residues from TM1 and TM2 were shown to be relevant for binding large ligands such as nemonapride. Lastly, T-stacking interactions revealed as especially relevant for some large ligands such as apomorphine, risperidone or aripiprazole. Conserved residues in the OBP were found to be clustered in microdomains, stabilizing ligand-binding through the formation of a HB network. Indeed, HBs where mostly mediated by the serine microdomain (5.42Ser, 5.34Ser and 5.46Ser especially at D2R and D5R). Interestingly, these residues were not relevant for D1R, although a study by Hugo et al. mentioned 5.46Ser as key residue for activating D1R[90]. In this study, 3.37Trp was also proposed to be mediator of the D1R-activation [90]. We were not able to confirm these findings in our study, only bromocriptine and spiperone were interacting 3.37Trp at D1R, while at D5R we did not observe any interaction with this residue. 3.37Thr D2R was found to interact with 7-OH-DPAT, indicating that these residues may not be D1R-specific. Salt-bridges were exclusively formed by 3.32Asp but appeared to involve also residues from ECL1 for spiperdone for D1R and D3R. For “bulky” ligands such as clozapine or bromocriptine no salt-bridges were formed. Frontera et al. reported that the strength of cation-π-interactions is also influenced by the presence of weaker interactions such as hydrogen or hydrophobic bonds [81]. For instance, it is well known that H-bonding is highly contributing to the bond strength of π-stacking [81]. But not only weaker interactions benefit π-interactions, cat-π and π-π-stacking were also found to be cooperative for each other [81]. Such combinations where cat-π and π-π-stacking were simultaneously present, were indisputably found for D2-like rather than for D1-like DRs. In addition, these residues and those of the TM6 aromatic microdomain (6.48Trp, 6.51Phe, 6.52Phe, 6.55His/Asn) were mostly involved in forming π-interactions (cat-π, π-π or T-stack). Phenylalanine, tyrosine and tryptophan interacting ligands could indeed be further extended in order to design a new selective SAR for D5R, as they were found to be exclusively involved in π-interactions and π-stacking formation at this DR subtype. Since for the D1R-like DR SCH23390 and SKF38393 are the only known selective ligands, a closer look at the interacting residues of these ligands revealed that cat-π-interactions (6.30Glu, 6.39Val, 6.42Gly) were only present at the D1R for SCH23390, the antagonist at the D1-like DR [88]. Moreover, these residues were not the “classical” TM6 residues usually involved in binding, while this was true for the other ligands. This encourages the search for D1R- or D5R-selective ligands, which should ideally form cat-π-interactions with certain amino-acids, as they were found in this ligand set. From a structural basis SCH23390 and SKF38393 are more related to the benzodiazepines, compared to the other ligands that are either small molecules or longer ligands with piperidine moieties [91]. Lastly, another difference found between SCH23390 and SKF38393 binding to D5R were that SKF38393 established more interactions with residues from different TMs and a variety of neighboring residues of the Molecules 2019, 24, 1196 17 of 26

“classical” interacting residues; whereas SCH23390-receptor-interactions were more limited to a smaller number of residues. These observations were not found for both ligands at the D1R. Reported by Bourne, who discovered SCH23390, this compound is the 3-methyl, 7-chloro analogue of the D1R agonist SKF38393, which is furthermore enantioselective [88]. In addition, it was stated that the phenyl ring in the benzodiazepine-derivatives and the receptors was involved in electrostatic forces, important for binding [88,92]. Mapping the full electrostatic potential of the D5R using ligands with benzodiazepine properties may be useful to find D5R-selective SAR. In order to find future SARs for DRs and improve subtype selectivity, we should not only considerer the known “classical” residues and binding motifs such as the “DRY” motif, but also conserved neighboring amino acids as shown herein. For sure, this would improve the treatment with antipsychotics of many patients.

4. Materials and Methods

4.1. Homology Modeling

4.1.1. General Approach The apo-DR models were generated with MODELLER 9.19 (version MODELLER 9.19, released Jul 25th, 2017) (University of California San Francisco, San Francisco, CA, USA) [93]. For D2-like receptors we used their corresponding crystal structures as templates: the D2R complexed with risperidone (PDBid: 6CM4) [36], the D3R complexed with D2R-antagonist eticlopride, (PDBid: 3PBL) [37] or D4R complexed with D2R/D3R-antagonist nemonapride (PDBid: 5WIU) [38]. Depending on the sequence similarity obtained with Basic Local Alignment Search Tool (BLAST, NCBI, Rockville, MD, USA) [40] and ClustalOmega (EMBL-EBI, Cambridgeshire, UK) [41] and listed in Table2, either D 3R (for the D1R) or D4R (for the D5R) were chosen as template to model the D1-like DRs. Due to the length of the ICL3, this was cut and substituted with two or four alanine residues, for D2- and D1-like receptors. Water and co-crystalized compounds were removed from the template structures. In the modeling protocol the lengths of the TMs and the perimembrane intracellular helix (HX8) were specified. In addition, disulphide bonds were constricted in the known pairs of cysteines, in particular between 3.25Cys and a non-conserved cysteine in ECL2 and between two non-conserved cysteines in the ECL3. Furthermore, loop refinement was performed for extracellular and intracellular loops for all DR using the module “loop refinement” of MODELLER 9.19. The number of models calculated with MODELLER [93] was set to 100.

Table 2. Identity between DRs in study and their corresponding templates calculated with BLAST [40] and ClustalOmega [41].

DOPAMINE RECEPTOR TEMPLATE BLAST [%] CLUSTALOMEGA [%]

D1R 3PBL 35.0 39.5 D2R 6CM4 97.0 100.0 D3R 3PBL 93.0 99.3 D4R 5WIU 93.0 100.0 D5R 5WIU 35.0 39.1

4.1.2. Model Evaluation/Methods of Quality There are several approaches to validate homology models such as built-in metrics of open-source [52] and licensed softwares [94]. In a preliminary study we experienced [50] that the combination of different independent metrics provided adequate models suitable for molecular docking. For instance, the combination of MODELLER’s metrics [95], ProSA-web [43,44] and ProQ [45,46] revealed to be a promising and reliable protocol to create valid models for molecular docking. Molecules 2019, 24, 1196 18 of 26

Discrete Optimized Protein Energy (DOPE) [42] scores are MODELLER’s standard metrics and were utilized in combination with visual inspection to initially remove incorrect models. DOPE is specific for a given target sequence, e.g., it accounts for the finite and spherical shape of native protein states with the lowest free energy [42]. It should be noted, that although DOPE is not an absolute measure, it helps to rank the proposed models. Then, out of a small set of potential candidates (selection of 5–10), ProSA web service [44] and the online ProQ prediction server [46] were used to determine the final models with the best combination of scores. For the z-score provided by ProSA-web analysis values around −4 are suggested as acceptable. It was only used for error recognition, as it indicates overall model quality with respect to an energy distribution derived from random conformations for globular proteins [44] The ProQ analysis (LGscore [95] and MaxSub [96]) provides absolute measures based on a neural network, which were set as base for the more detailed evaluation of the models. Regarding the LGscore, values > 3, for MaxSub values > 0.5 are typically considered as “good”. Additionally, ProQ allows to include secondary structure information calculated with PSIPRED [97], improving the prediction accuracy and increasing the model quality up to 15%. The ProQ analysis was only carried out, if z-scores around 2–4 were achieved using the ProQ protocol. We could not compare our models with other authors as metrics scores are mostly not shown [43,98].D1-like models, without a known crystal structure and D2-like models for which there are 3D crystal structure, showed similar quality (Table3).

Table 3. Metrics and scores of the final DR homology models used herein.

DR DOPE LGscore LGscore + PSIPRED MaxSub MaxSub + PSIPRED z-Score

D1R −39070.82 2.53 4.26 0.18 0.53 −2.14 D2R −39284.66 2.52 4.22 0.21 0.52 −2.22 D3R −39458.37 3.14 4.19 0.27 0.55 −3.12 D4R −36738.05 3.33 4.25 0.25 0.59 −3.90 D5R −38356.05 2.60 4.14 0.15 0.57 −1.49

4.2. Molecular Dynamics

4.2.1. System Setup It is well known that GPCRs take an infinite number of conformations over time. As such we performed MD simulations of modelled apo-forms to verify the effect of punctual fluctuations into the overall binding arrangements of ligands. Before setting up the system, the selected DR models were subjected to the Orientations of Proteins in Membranes (OPM) web-server [99–102] to calculate spatial orientations respecting to the Membrane Normal defined by the z-axis. In addition, the state of titratable residues was calculated by Propka 3.1 [103,104] within the PDB2PQR web-server [105] at a pH of 7.0. The prepared receptor structures were inserted into a previously constructed lipid bilayer of POPC: Cholesterol (9:1). Insertion of the receptors in the membrane was performed with g_membed package of GROMACS [106]. System was then solvated with explicitly represented water. Sodium and chloride ions were added to neutralize the system until it reached a total concentration of 0.15 M. The final systems dimensions were 114 × 114 × 107 Å and included approximately 370 POPC, 40 cholesterols, 125 sodium ions, 139 chloride ions and 28500 water molecules, with small variations from receptor to receptor.

4.2.2. Molecular Dynamics Simulation Protocol CHARMM36 force field, with an implemented CMAP correction, was used for ions, water (TIP3P model), lipids and protein parameters [107]. Prior to MD simulation, the systems were relaxed to remove any possible steric clashes by a set of 50,000 steps of Steepest Descent energy minimization. Equilibration was performed afterwards as following: the system was heated using Nosé-Hoover thermostat from 0 to 310.15 K in the NVT ensemble over 100 ps with harmonic restraints Molecules 2019, 24, 1196 19 of 26 of 10.0 kcal/mol. Then systems were subjected through a first step of NPT ensemble of 1 ns with semi isotropic pressure coupling and a pressure of one bar. Further equilibration was performed with sequential release of membrane lipids and protein’s atoms with a final step of NPT ensemble with harmonic restraints on the protein of 1.0 kcal/mol, for a total of 5 ns of restrained equilibration. MD simulations of all DR models were performed with the periodic boundary condition to produce isothermical-isobaric ensembles using GROMACS 5.1.1 [106]. The Particle Mesh Ewald (PME) method [108] was used to calculate the full electrostatic energy of a unit cell in a macroscopic lattice of repeating images. Temperature was regulated using the Nosé-Hoover thermostat at 310.15 K. Pressure was regulated using the Parrinello-Rahman algorithm. The equations of motion were integrated using leapfrog algorithm with a time step of 2 fs. All bonds, involving hydrogen atoms within protein and lipid molecules were constrained using the LINear Constraint Solver (LINCS) algorithm [109]. Additionally, a cut-off distance of 12 Å was attributed for Coulombic and van der Waals interactions. Then a single independent simulation of 100 ns was initialized from the final snapshot of the restrained equilibration from each DR, for a total of 5 simulations. Trajectory analysis was performed by in-house scripting using GROMACS [106] and Visual Molecular Dynamics (VMD) [110]. Trajectory snapshots were saved every 5 ns. The snapshots after the first 50 ns MD stabilization were used for molecular docking studies.

4.3. Molecular Docking

4.3.1. Ligand Dataset The following ligands were docked into the receptor decoys: dopamine, 7-hydroxy-N,N-dipropyl-2-aminotetralin (7-OH-DPAT), apomorphine, bromocriptine, clozapine, nemonapride, sulpiride, SCH23390, SKF38393, eticlopride, risperidone, aripiprazole, haloperidole, spiperone and chlorpromazine (Table1). All structures were obtained from the DrugBank database (https://www.drugbank.ca) or from ChemSpider (http://www.chemspider.com)[111].

4.3.2. Docking Procedure DR binding pocket was defined in several experimental and computational studies [2,47,52,55,57,59,85]. Herein, we used the comprehensive review by Floresca and Schetz [47] as a base for exploration of the DR binding pocket, since it contains detailed experimental data. A summary of the procedure can be better reviewed in Bueschbell et al. [50]. AutoDock4.2 (version AutoDock 4.2.6, released in 2009) was used to perform ligand docking [112]. DR hydrogens were added and Kollman united atom charges were assigned. Hydrogens were also added to the ligand and Gasteiger-Marsili was used to calculate charges. Before docking an energy, grid was created using AutoGrid (version AutoGrid 4.2.6, released 2009) with a box-size varying with the times step and ligand. For each docking simulation 100 independent Lamarckian genetic algorithm (LGA) runs were performed with the number of energy evaluations set to 10.000.000, the population size set to 200 and the maximum number of generations set to 27.000. Default settings were maintained for the rest of the parameters. Docked conformations within a RMSD of 2 Å were clustered. The most populated and lowest energy cluster (Gibbs free energy of binding) was used for conformational analysis. To find the local energy minimum of the binding site with a limited search space to that region, a low-frequency local search method was used. The 100 conformations obtained from docking were clustered by low-energy and RMSD. The top-ranked conformations within the best 3 clusters were visually inspected. The docking parameters were not changed for any ligand, only the residues treated as flexible in the docking protocol differed between the ligands. The flexible residues for each DR model are summarized in Table4. Molecules 2019, 24, 1196 20 of 26

Table 4. Flexible residues used in the molecular docking different ligands.

LIGAND FLEXIBLE RESIDUES IN B&W NUMBERING DOPAMINE 3.32Asp, 5.42Ser, 5.43Ser, 5.46Ser, 6.48Trp, 6.51Phe, 6.52Phe, 6.55His/Asn 7-OH-DPAT APOMORPHINE 3.32Asp, 3.36/3.35Cys, 5.42Ser, 5.43Ser, 5.46Ser, 6.48Trp, 6.51Phe, 6.52Phe, 6.55His/Asn BROMOCRIPTINE CLOZAPINE 3.32Asp, 3.33Val, 3.36Cys, 5.42Ser, 5.43Ser, 5.46Ser, 6.48Trp, 6.55His/Asn NEMONAPRIDE 2.57Val, 3.32Asp, 5.42Ser, 5.43Ser, 5.46Ser, 6.48Trp, 6.51Phe, 6.52Phe, 7.43Tyr SULPIRIDE 3.32Asp, 6.48Trp, 5.42Ser, 5.43Ser, 5.46Ser, 6.55His/Asn, 7.43Tyr, 6.51Phe SCH23390 3.32Asp, 6.48Trp, 5.42Ser, 5.43Ser, 5.46Ser, 6.55His/Asn, 6.51Phe, 6.52Phe SKF38393 ETICLOPRIDE 3.32Asp, 6.48Trp, 5.42Ser, 5.43Ser, 5.46Ser, 6.55His/Asn, 7.43Tyr, 6.51Phe, 6.52Phe RISPERIDONE 3.32Asp, 6.48Trp, 3.36Cys, 6.55His/Asn, 2.57Val, 5.42Ser, 5.43Ser, 5.46Ser ARIPIPRAZOLE 3.32Asp, 6.48Trp, 3.33Val, 5.42Ser, 5.43Ser, 5.46Ser, 7.43Tyr, 6.55His/Asn HALOPERIDOLE 3.32Asp, 6.48Trp, 6.51Phe, 6.52Phe, 3.36Cys, 2.57Val, 5.42Ser, 5.43Ser, 5.46Ser SPIPERONE 3.32Asp, 6.48Trp, 5.42Ser, 5.43Ser, 5.46Ser, 3.36Cys, 6.55His/Asn, 2.57Val CHLORPROMAZINE 3.32Asp, 6.48Trp, 5.42Ser, 5.43Ser, 5.46Ser, 6.55His/Asn, 3.36Cys, 6.51Phe

4.3.3. Analysis of Molecular Docking In this study, 15 DR ligands were docked to the homology model and to different conformational arrangements retrieved at every 5 ns for the 55–100 ns range for each DR simulation (825 dockings in total). All distances between the center of mass of the ligand and the alpha-C-atom (Cα) of the residues, treated as flexible in the docking protocol, were calculated using in-house PyMOL scripts [2,17,47,52,57,59] as well as previously published work [50]. We also develop in-house BINANA scripts to predict the main receptor-ligand interactions [39]. BINANA is an open-source python-implemented algorithm which uses output files from AutoDock [112] for the analysis of interactions and visualizes them in the free molecular-visualization program VMD [110]. Key binding characteristics such as hydrogen bonds, hydrophobic contacts, salt-bridges and π-interactions were calculated with BINANA.

Supplementary Materials: The following are available online. Figure S1—RMSD throughout the 100 ns of simulation for all DR models; Figure S2—Redocking of ligands with their respective DR and bound ligand; Figure S3—Molecular docking of Dopamine at the D1R–D5R during 0–60 ns; Figure S4—Molecular docking of Dopamine at the D1R–D5R during 65–75 ns; Figure S5—Molecular docking of Dopamine at the D1R–D5R during 80–90 ns; Figure S6—Molecular docking of Dopamine at the D1R–D5R during 95 and 100 ns; Figure S7—Results of the molecular docking of bromocriptine, clozapine, sulpiride, eticlopride, risperidone, aripiprazole and risperidone for all DR subtypes at time points [ns]; Figure S8—Total interactions counted for each DR over time points [ns]; Figure S9—Pairwise interaction results for dopamine; Figure S10—Pairwise interaction results for 7-OH-DPAT; Figure S11—Pairwise interaction results for apomorphine; Figure S12—Pairwise interaction results for bromocriptine; Figure S13—Pairwise interaction results for clozapine; Figure S14—Pairwise interaction results for nemonapride; Figure S15—Pairwise interaction results for sulpiride; Figure S16—Pairwise interaction results for SCH23390; Figure S17—Pairwise interaction results for SKF38393; Figure S18—Pairwise interaction results for eticlopride; Figure S19—Pairwise interaction results for risperidone; Figure S20—Pairwise interaction results for aripiprazole; Figure S21—Pairwise interaction results for haloperidole; Figure S22—Pairwise interaction results for spiperone; Figure S23—Pairwise interaction results for chlorpromazine. Table S1—Comparison between the total and transmembrane specific identity [%] of the DR model with their crystal structure templates calculated with Clustal Omega; Table S2—Averages RMSD values for TM throughout the simulations; Table S3—Summary of the structures used in literature for defining the binding pocket for the D2R and source (experimental and computational); Table S4—Docking results for the D1R; Table S5—Docking results for the D2R; Table S6—Docking results for the D3R; Table S7—Docking results for the D4R; Table S8—Docking results for the D5R; Table S9—Docking results for the crystal structure templates of D2R (PDBid: 6CM4), D3R (PDBid: 3PBL) and D4R (PDBid: 5WIU) docked with their co-crystalized ligands; Table S10—D1R residues with Ballesteros & Weinstein-numbering participating in different interaction types sorted by ligands; Table S11—D2R residues with Ballesteros & Weinstein-numbering participating in different interaction types sorted by ligands; Table S12—D3R Molecules 2019, 24, 1196 21 of 26 residues with Ballesteros & Weinstein-numbering participating in different interaction types sorted by ligands; Table S13—D4R residues with Ballesteros & Weinstein-numbering participating in different interaction types sorted by ligands; Table S14—D5R residues with Ballesteros & Weinstein-numbering participating in different interaction types sorted by ligands. Author Contributions: B.B., A.J.P. and C.A.V.B. performed the experiments; B.B, A.J.P. and C.A.V.B. analyzed the data; B.B., A.C.S and I.S.M conceived and designed the experiments; all authors wrote the paper. Funding: B.B. and A.C.S. were supported by the German Federal Ministry of Education and Research (BMBF project) of the Bonn International Graduate School in Drug Sciences (BIGS DrugS). Irina S. Moreira acknowledges support by the Fundação para a Ciência e a Tecnologia (FCT) Investigator programme - IF/00578/2014 (co-financed by European Social Fund and Programa Operacional Potencial Humano). This work was also financed by the European Regional Development Fund (ERDF), through the Centro 2020 Regional Operational Programme under project CENTRO-01-0145-FEDER-000008: BrainHealth 2020. We also acknowledge the grant PTDC/QUI-OUT/32243/2017 financed by national funds through the FCT / MCTES and/or State Budget. Conflicts of Interest: The authors declare no conflict of interest.

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Sample Availability: Samples of the compounds are not available from the authors.

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