Amino Acid Hot Spots of Halogen Bonding: A Combined Theoretical

Article Cite This: J. Med. Chem. 2018, 61, 8717−8733

pubs.acs.org/jmc

Amino Acid Hot Spots of Halogen Bonding: A Combined Theoretical and Experimental Case Study of the 5‑HT7 Receptor Rafał Kurczab,*,† Vittorio Canale,‡ Grzegorz Satała,† Paweł Zajdel,‡ and Andrzej J. Bojarski† †

Department of Medicinal Chemistry, Institute of Pharmacology, Polish Academy of Sciences, 12 Smętna Street, 31-343 Krakow, Poland ‡ Department of Medicinal Chemistry, Jagiellonian University Medical College, 9 Medyczna Street, 30-688 Krakow, Poland

J. Med. Chem. 2018.61:8717-8733. Downloaded from pubs.acs.org by UNIV OF SUNDERLAND on 10/14/18. For personal use only.

S Supporting Information *

ABSTRACT: A computational approach combining a structure−activity relationship library of halogenated and the corresponding unsubstituted ligands (called XSAR) with QM-based molecular docking and binding free energy calculations was used to search for amino acids frequently targeted by halogen bonding (hot spots) in a 5-HT7R as a case study. The procedure identified two sets of hot spots, extracellular (D2.65, T2.64, and E7.35) and transmembrane (C3.36, T5.39, and S5.42), which were further verified by a synthesized library of halogenated arylsulfonamide derivatives of (aryloxy)ethylpiperidines. It was found that a halogen bond formed between T5.39 and a bromine atom at 3-position of the aryloxy fragment caused the most remarkable, 35-fold increase in binding affinity for 5-HT7R when compared to the nonhalogenated analog. The proposed paradigm of halogen bonding hot spots was additionally verified on D4 dopamine receptor showing that it can be used in rational drug design/optimization for any protein target.

■

INTRODUCTION In the range of intermolecular interactions, halogen bonding (XB) is one of the most intensively investigated in recent years.1−8 Halogen bonds are ruled by the same mechanism as hydrogen bonds; i.e., they show mostly electrostatic character and are highly directional, and their length is shorter than the sum of the van der Waals radii of their constituent atoms.9 A halogen bond can be defined as a directional bond between a covalently bound halogen atom (acting as a donor) and a Lewis base, the bond acceptor. It originates from the anisotropy of the electron density distribution around the halogen atom,10−14 in particular from the fundamental properties of the covalent σ-bond between atoms in the C− X group. Halogen atoms have five electrons occupying the p atomic orbitals of their valence shell (according to molecular orbital theory), and the single valence electron of the pz orbital is involved in the creation of a covalent σ-bond with a carbon atom. As a result, the depopulation of this orbital opposite the C−X σ-bond leaves a hole that partially exposes the positive nuclear charge. This so-called σ-hole accounts for the electropositive crown and polar flattening associated with © 2018 American Chemical Society

polarization effects (anisotropy in charge distribution), whereas the four electrons remaining in the px and py orbitals account for the electronegative ring lying perpendicular to the σ-bond. This arrangement leads to attractive interactions between C−X moieties and classical hydrogen bond acceptors (Figure 1). Halogen bonds are strong enough to control the aggregation of organic molecules in solid,15 liquid,16 and gas phases17 and have been intensively investigated in recognition processes.18−21 They are also considered a new tool in materials science22 or a novel interaction for rational drug design.2,7,8,23−27 Among 14 5-hydroxytryptamine receptor (5-HTR) subtypes, 5-HT7R is the mostly recently identified.28 It is canonically coupled to Gαs or Gα12 proteins, promoting signal transduction through cAMP/PKA- and ERK-dependent pathways.29 Distribution studies revealed a correlation between the localization of 5-HT7R in the central nervous system (CNS), Received: May 24, 2018 Published: September 6, 2018 8717

DOI: 10.1021/acs.jmedchem.8b00828 J. Med. Chem. 2018, 61, 8717−8733

Journal of Medicinal Chemistry

Article

Figure 1. Illustration of the σ-hole concept: formation of a covalent carbon−halogen bond (a C−X σ-bond) by pairing of the electrons from the valence orbitals of the two atoms. As a result, the portion of the pz orbital of the halogen opposite the σ-bond becomes depopulated, resulting in an electropositive crown (in blue), whereas the px and py orbitals retain their complement of electrons to account for the overall negative charge of the halogen (in red).

Figure 2. Workflow of computational algorithm to identify amino acids (hot spots) that are common anchoring points for halogen bonding with halogenated ligands that show greater activity than their unsubstituted analog.

tic benefit in the treatment of cognitive impairment in depression38 and the negative symptoms of schizophrenia.39 This work presents a universal computational algorithm developed to identify the amino acids frequently targeted by halogen bonding (called hot spots) in the 5-HT7R binding site used as a case study. The theoretical predictions were evaluated experimentally by using newly synthesized library of halogenated arylsulfonamide derivatives of (aryloxy)ethylpiperidines. The results confirmed the existence of amino acids preferred for halogen bonding and provide evidence for the rational design of new bioactive molecules using halogen atoms.

especially in the thalamus, hypothalamus (suprachiasmatic nucleus), hippocampus, and cerebral cortex, and its functions, suggesting an involvement in physiological (specifically, regulation of sleep and circadian rhythm and learning and memory processes) as well as pathophysiological phenomena.30,31 Several studies have confirmed that 5-HT7R agonists may produce beneficial effects in the treatment of dysfunctional memory in neurodegenerative disease (e.g., Alzheimer’s disease),32 X fragile syndrome, ADHD, and pain.33,34 On the other hand, preclinical findings revealed that the genetic inactivation or pharmacological blockade of 5-HT7R produced an antidepressant-like effect.35−37 A large body of evidence underlines the involvement of 5-HT7R antagonists in improving memory processes, suggesting a potential therapeu8718



Article

Figure 3. Illustration of an XSAR matrix generated for 25 sets showing the positive changes in activity against 5-HT7R upon halogenation. Several selected examples were depicted to represent the different roles of halogen atoms in ligand−receptor binding. The complexes are shown in the homology model of the 5-HT7 receptor based on the D3 template. In each case, the geometries of unsubstituted (green) and halogenated (cyan, magenta) structures were presented together with the substituted analog activity and corresponding change in the binding free energy (ΔΔG). At the bottom, the newly synthesized library designed on the basis of set24 is shown.

■

RESULTS AND DISCUSSION Study Design. A computational workflow (Figure 2) was developed to identify amino acids that frequently form halogen bonds with ligands. In the first stage, a library containing unsubstituted and halogenated analog(s) (called XSAR) whose affinities for 5-HT7R are available in the ChEMBL database40 was built. Next, a positive subset of the XSAR library (i.e., sets showing improved activity after halogenation) was used to probe the binding site using a combination of QM-based molecular docking (i.e., quantum-polarized ligand docking, QPLD) to multiple 5-HT7R homology models and binding free energy (ΔG) calculations (i.e., generalized-Born/surface area, GBSA). In the final stage, all amino acids involved in the formation of halogen bonds that yielded higher ΔG values for halogenated analogs than unsubstituted structures (ΔΔG < 0)

and fulfilled the geometric criteria for halogen bonding (both distance and σ-hole angle) were determined. The 5-HT7R amino acid hot spots identified by using XSAR sets were further verified by the library containing newly synthesized halogenated compounds. This library was designed based on previously reported series of arylsulfonamide derivatives of (aryloxy)ethylpyrrolidines classified as 5-HT7R ligands. Among them, one XSAR set was identified (set24).41 This chemotype was modified at the central amine moiety by the replacement of pyrrolidine with piperidine. Interestingly, the initially synthesized unsubstituted arylsulfonamide derivative of (aryloxy)ethylpiperidine (58, Figure 3) showed low affinity for 5-HT7R but became a good starting point for a systematic investigation of fine-tuning the effects of halogen 8719



Article

1.0) was caused by unfavorable steric interactions (the median fold change is −5.0, Table S1) or a docking failure (the median fold change is −5.0, Table S1) of a given derivative to the receptor model(s). 5-HT7R Halogen Bonding Hot Spots. The QPLD/GBSA docking procedure and XSAR data were used to probe the 5HT7R binding site to determine the amino acids most frequently targeted by halogen bonding. Since the 5-HT7R has not been crystallized, a set of homology models built on eight templates close to the 5-HT7R was used. For each template, amino acids were classified into two categories, namely, the primary (i.e., those commonly forming halogen bonding) and the secondary hot spots (less frequently engaged). For each category, halogen bonding contacts with the carbonyl oxygens of the protein backbone or with Lewis bases in side chains were distinguished (Figure S4). All amino acids classified as halogen bond acceptors were averaged over all templates, and a final hypothesis was generated (Figure 4) independently for side chains and backbone carbonyl oxygens.

bonding on the affinity for 5-HT7R through the introduction of halogen atoms and hydrophobic substituents. Using XSAR Sets To Evaluate the Role of Halogen Atoms in Ligand−Protein Complexes. The search of the ChEMBL database using the developed algorithm yielded 43 XSAR sets (all sets are provided as a Supporting Information), and in 25 of these sets, halogenation improved activity relative to the unmodified structure (i.e., Xeffect > 1.00). The docking to 5-HT7R homology models was performed by the use of the QPLD/GBSA procedure, which showed very good performance in reproducing halogenated ligand−receptor (L−R) complexes stored in the protein database (PDB).42 An analysis of the best docking poses for each XSAR set showed a common interaction pattern with 5-HT7R, i.e., a salt bridge with D3.32 and hydrophobic/aromatic interactions with an aromatic cluster consisting of F6.51, F6.52, and W6.48. Additional contacts were specific and determined by the chemotype and substituents. The selected XSAR sets highlight different roles of halogen atoms that result in the halogenated analogs having higher activity than the nonhalogenated analogs (Figure 3). Set8 is represented by the 3- (magenta) and 4-Cl (cyan) derivatives of the N-biphenyl-2-ylmethyl-2methoxyphenylpiperazinylalkanamide 43 chemotype and showed an Xeffect value slightly greater than 1. The binding mode revealed that no halogen bond was formed; however, the position change in the chlorine atom decreased (3-Cl, approximately 11-fold and ΔΔG = +3.04 kcal/mol) and increased (4-Cl, approximately 1.5-fold and ΔΔG = −0.28 kcal/mol) the activity. This change was caused by a change on the chlorobenzene ring orientation triggered by a steric interaction of the chlorine atom. Compounds belonging to set1444 (with a chlorinated fragment exposed toward the extracellular part of the receptor) had only slightly higher activity for halogenated ligand (1.1-fold and ΔΔG = −1.81 kcal/mol) than the nonhalogenated analog. Set1145 displayed the highest Xeffect value (97.1-fold higher activity for the halogenated derivative and ΔΔG = −9.15 kcal/mol), which was related to the formation of two halogen bonds, one with the backbone carbonyl oxygen of T5.39 (distance 3.15 Å, σhole angle = 168.5°) and one with the backbone carbonyl oxygen of S5.42 (distance 2.95 Å, σ-hole angle = 155.4°). The halogenated derivatives of long-chain 4-substituted piperazines linked to a quinazoline system (set1546) confirmed that the T5.39 is a preferred amino acid for the creation of halogen bonds and illustrate the high geometric preferences for their formation. The 3-Cl derivative showed an approximately 7-fold increase in affinity (ΔΔG = −4.78 kcal/mol) as a result of the formation of halogen bonding (distance 3.22 Å and σ-hole angle = 157.2°); however, when the chlorine atom was switched to the 4-position, the activity decreased approximately 8-fold from that of the nonhalogenated form (ΔΔG = +13.05 kcal/mol). The reason for this lower activity by the 4Cl derivative is steric hindrance that leads to the destabilization of the complex. On the basis of the analysis of the interactions of all XSAR sets with the 5-HT7R (Figure S2, Figure S3, and Table S1), it can generally be concluded that the increases in the Xeffect value resulting from steric (the median fold is 2.1, Table S1) or hydrophobic (the median fold change is 1.75, Table S1) interactions of the ligand with the protein were smaller than those originating from halogen bonding interactions (the median fold change is 5.5, Table S1). On the other hand, the decrease in the activity of halogenated derivatives (Xeffect
1.00). Structural clustering of the XSAR library indicated 14 different clusters (molecular scaffolds), whose centroids were next used to tune the crystal structure of the D4 8726



Article

In general, the theoretical predictions made by the developed computational approach were in good agreement with the experimental validation. However, among all identified XB hot spots for 5-HT7R, only several were found to make an important contribution to halogen bonding (i.e., D2.65, T2.64, and T5.39). Although S5.42 was indicated by the XSAR library to be the most frequently targeted XB hot spot (an amino acid neighboring T5.39 and buried deeper than it in the binding site), T5.39 had the most significant contribution to the affinity increase for the synthesized library. A plausible explanation for this difference may result from the molecular scaffold influencing the spatial orientations and conformational restrictions of a ligand in the binding site; moreover, it should be emphasized that the majority of the XSAR subsets contained a halogenated arylpiperazine fragment that was more rigid than the aryloxyethyl fragment. The lower impact of extracellular XB hot spots compared with transmembrane regions on the affinity change can be explained by the higher flexibility of the extracellular fragment and its greater exposure to the solvent, which can lead to the creation of weaker and less frequent XB contacts. The detailed analysis of molecular interactions involved in the stabilization of the L−R complex of both XSAR and synthesized libraries revealed that halogen atoms can play different roles in the L−R interaction; i.e., they can halogenbond and provide hydrophobic interactions and steric hindrance. The interplay between halogen bonding and hydrophobic/steric interactions is difficult to quantify; thus, hydrophobic substituents with a diverse range of volumes and shapes were used to verify the differences in XB vs hydrophobic effects. Interestingly, this approach showed that in the case where XB was detected in L−R complexes, the affinity increase was significantly higher for derivatives containing halogen than those containing hydrophobic substituents, and in the case where only steric/hydrophobic effects dominated the L−R complexes, the gain in affinity was well correlated with the volume and shape of the substituent. Besides the evaluation of L−R interactions with halogenated derivatives, other important effects, i.e., the synergy of halogen bonding, the plateau of an affinity, and the selectivity for the similar subtype receptor 5-HT1AR, were considered in this study. The initial hypothesis of halogen bonding being additive was investigated using disubstituted derivatives targeting hot spots belonging to the different regions (i.e., extracellular and transmembrane). Interestingly, the theoretical method indicated that two halogen bonds were formed (86); however, probably due to induced conformational changes, the L−R complex with 86 had a lower binding free energy than the complex with monosubstituted compound 73, which was in line with experimental data. A purely theoretical assumption states that halogen bonding strength increases from chlorine to bromine to iodine; however, among derivatives with a halogen atom localized in a favorable position to form a halogen bond (2- and 3positions at the phenylsulfonyl and aryloxy fragments, respectively), a plateau of affinity was reached with bromine. Unfortunately, in both series (for aryloxy and phenylsulfonyl fragments), iodine provided no significant increase in affinity, which means that in this system, other factors counteracted. The results of MD simulations supported the experimental data for these findings and showed that steric restrictions have a key impact on the stabilization of the L−R complex by halogen bonding. Moreover, to the best of our knowledge, this

receptor conformations using an induced-fit docking procedure (described in the Experimental Section; all data are provided in the Supporting Information). On the basis of the analysis of the interactions of all XSAR sets with a set of D4 receptor conformations, a different role of halogen atoms in L−R complexes was identified (Figure S6, Figure 9). First, the most frequently targeted amino acids by halogen bonding were identified, i.e., the primary V5.40 (c) and the secondary S5.43 (c), S5.46x461 (c, s), and H6.55 (s). The prediction of the primary XB hot spot (V5.40) was confirmed by a majority of XSAR sets; however, the most significant evidence came from the experimental structure of the D4 receptor complexed with nemonapride, where aromatic chlorine forms a halogen bond to the backbone carbonyl of V5.40 (Figure 9A, distance 3.68 Å, σ-hole angle = 163.2°). Worthy of note were L−R complexes of XSAR sets having a particularly high Xeffect value (Figure S6; i.e., set44 and set51) because they all contained di-Cl derivatives that formed halogen bonds with the carbonyl oxygen of V5.40 (Figure 9B, distance 3.37 Å, σ-hole angle = 167.4°) and with the side chain of H6.55 (Figure 9B, distance 3.19 Å, σ-hole angle = 148.5°). Moreover, the analysis also indicated that H6.55 was generally targeted by 2-halogenated aryl-piperazine/piperidine derivatives. The last two secondary XB hot spots were less accessible for a halogenated fragment of ligands and were detected only for several chemotypes. For instance, in the case of set53 (Figure 9C), an approximately 39-fold increase of affinity for halogenated analog can result from the formation of two medium halogen bonds, i.e. with the backbone of S5.43 (distance 3.56 Å, σ-hole angle = 142.6°), and the side chain of S5.46x461 (distance 3.41 Å, σ-hole angle = 141.3°). In XSAR sets, which had only a slightly higher activity for halogenated analogs and no possibility to form halogen bonds, a visual inspection revealed that the halogenated fragment of a ligand was usually exposed toward the extracellular part of the receptor (Figure 9D).

■

CONCLUSIONS The hypothesis of certain amino acids being targeted for halogen bonding (i.e., hot spots) was examined first in silico and then verified experimentally. For this purpose, a new computational approach was developed to search for hot spots of halogen bonding by probing the binding site using a structure−activity relationship library containing pairs of halogenated and unsubstituted ligands (so-called XSAR) with known affinity for the 5-HT7R. The algorithm identified a set of primary and secondary XB hot spots located in two distinct parts of the receptor, namely, the extracellular (including D2.65, T2.64, and E7.35) and transmembrane parts (i.e., C3.36, T5.39, and S5.42). To verify these hypotheses, N-[1-(2phenoxyethyl)piperidin-4-yl]benzenesulfonamide was used as a molecular framework for the generation of a library of halogenated derivatives. The rationale behind the choice of this structure resulted from its low affinity for 5-HT7R, its known synthetic route enabling the facile generation of halogenated derivatives, and its binding mode exposing the two terminal aromatic rings toward the part of the receptor where sets of XB hot spots were found. Notably, a complementary strategy that uses halogen-enriched fragment libraries (HEFLibs) has recently been developed and used for lead discovery.58,59 HEFLibs contain small halogenated fragments, which as molecular probes can explore binding sites for favorable halogen bond interactions to identify unique binding modes (halogen bonding hot spots). 8727



Article

report was the first use of MD simulations to study the plateau effect in halogen bonding. Finally, among all synthesized monohalogenated derivatives, the most selective also showed the highest affinity gain from halogenation. Unfortunately, due to its very low affinity to the second receptor, no coherent binding mode was obtained, and thus it cannot be concluded that halogen bonding in one receptor is crucial for selectivity over the second one. However, it should be noted that halogen bonding was previously verified as a selectivity trigger.2 Analysis of the DrugBank database for the presence of halogen atoms in drug molecules shows that they are a frequently used modification by medicinal chemists (the percentages of all drug molecules containing −Cl, −Br, −I, −CH3, −OCH3, −i-Pr, and −tert-butyl groups are 11.1%, 2.6%, 1.3%, 4.7%, 6.3%, 1.2%, and 0.5%, respectively). The computational algorithm developed and tested in this study showed a high potential to indicate the most favorable halogen substitution position in the aromatic ring to efficiently maximize biological activity. Thus, this algorithm might be used to rationalize synthetic protocols and to improve virtual screening for any target.

■

halogenation position, the halogenation at each position was written in a separate row. Homology Modeling of 5-HT7R. Building of Homology Models. The sequence of the human 5-HT7 receptor (code P34969) was obtained from the UniProtKB/Swiss-Prot database.62 Homology models of the 5-HT7 receptor were built based on class A GPCR crystal structures retrieved from the Protein Data Bank (Table S2; similarity/identity scores before sequences are shown in Figure S7). Sequences of the modeled receptor and selected templates were aligned manually (Figure S8) using Accelrys Discovery Studio v3.0, making sure that the most conserved amino acids in each helix and motifs characteristic for class A GPCRs were in equivalent positions. Ranges of helices were determined on the basis of crystal structures; loop regions were modeled but not refined. For each aligned template, a series of 200 models were generated using Modeller 9v8 software63 employing an approach previously utilized in our laboratory.64,65 Validation. The modeled structures were validated using a flexible ligand docking method (performed using Glide software in XP precision mode) and enrichment calculations. Sets of ligands with known activities (Ki 5-HT7 < 100 nM) and inactivities (Ki 5-HT7 > 1000 nM) were retrieved from the ChEMBL database (version 22).40 All active compounds were hierarchically clustered using Moldprint2D, the Tanimoto metric, and the Complete Cluster Linking Method implemented in Canvas v2.0.66 The centroid was selected from each cluster containing more than two members. Finally, all centroids of active compounds (121 for 5-HT7R) and the whole set of inactive compounds (1634) constituted the input for docking. The three-dimensional structures of the synthesized compounds were prepared using LigPrep v3.0,67 and the appropriate ionization states at pH = 7.4 ± 1.0 were assigned using Epik v2.8.68 Protein Preparation Wizard was used to assign the bond orders, check for steric clashes, and assign appropriate amino acid ionization states for each receptor model. The receptor grids were generated (the OPLS3 force field69) by centering the grid box of the size of 12 Å on the D3.32. Automated docking was performed using Glide v6.370 at the XP level with the flexible docking option turned on. Selection of Final Models. The homology models were evaluated by calculating the ROC curve based on the Glide Score values of docked compounds (the undocked actives and inactives were assigned as false negatives and true negatives, respectively). The final model quality was determined by the area under the ROC curve (AUROC), based on which 10 models per template with the highest AUROC value were selected for further studies. QM-Polarized Ligand Docking. Quantum mechanic/molecular mechanic (QM/MM) docking was performed using the QPLD implemented in Schrödinger Suite.71 QPLD combines the Glide docking algorithm with QM/MM calculations performed by the QSite program,72,73 which uses the Jaguar74 and Impact programs for the QM (ligand) and MM (protein) regions, respectively. At the initial stage of the QPLD procedure, the ligands were docked into a rigid protein using Glide SP. Next, the resulting binding poses were used to calculate the partial atomic charges of the ligand by a singlepoint calculation in Q-Site. The B3PW91 functional,75−78 which was applied in several theoretical studies of halogen bonding systems,79−82 was used in conjunction with the cc-pVTZ basis set for Cl and Br and the cc-pVTZ-pp basis set for I-containing ligands.83 Thus, the effect of the polarization of the charges on the ligand by the receptor was considered in the final docking stage, where the partial charges derived from the QM calculations of the ligand were used. During the QPLD calculations, no protein flexibility was present; however, the ligands are treated as flexible in each of the two docking stages. The number of returned poses per ligand in each docking stage was set to 10. Binding Free Energy Calculations. GBSA was used to calculate the binding free energy based on the L−R complexes generated by the QPLD procedure. The ligand pose energies were minimized using the local optimization feature in Prime, the flexible residue distance from a ligand pose was set to 4.0 Å, and the ligand charges obtained in the QPLD stage were used. The energies of complexes were calculated with the OPLS369 force field and GBSA continuum solvent model. To

EXPERIMENTAL SECTION

Workflow for Searching for Halogen-Bonding Hot Spots. A calculation procedure (Figure 2) was developed to identify key amino acids that improve the activity of a ligand by participating in the formation of halogen bonding with it. The first step was to create a library (called XSAR) containing sets, each containing an unsubstituted molecule and all of its halogenated analogs, whose biological activity data for a given receptor are available. Next, the XSAR sets that showed greater activity for the halogenated derivative(s) were used to probe the binding site using QM-based molecular docking and the binding free energy (ΔG) calculations. In the final stage, all amino acids (XB hot spots) involved in the formation of halogen bonding contacts that resulted in greater ΔG values for halogenated analogs than for their corresponding unsubstituted structure and fulfilled the geometric criteria for halogen bonding (both in terms of distance and σ-hole angle) were identified. The JChem suite v17.23.0 60 and MayaChemTools v9 61 libraries for computational drug discovery were used. Generation of Structure−Activity Relationship Data Sets for Halogenated Analogs (XSAR). Compounds whose activity for 5-HT7R had been determined were fetched from the ChEMBL v22 database.40 However, only molecules whose activities were quantified by Ki, pKi, IC50, or pIC50 and had been tested in human protein assays were taken into account. Next, an algorithm was developed and used to find all pairs containing halogenated structures and their corresponding unsubstituted structures (a detailed description of the algorithm is in the Supporting Information, Figure S1). To describe the influence of halogenation on the biological activity of the unsubstituted (parent) molecule, the Xeffect parameter was calculated as a ratio of parent compound to its halogenated derivative activity. Xeffect values between 0 and 1 denote the negative influence of halogenation (a decrease in the activity upon halogenation), whereas values higher than 1 mean that the activity increased after halogen substitution. To visualize the collected XSAR data, an R script was prepared and used to generate a heat matrix. For individual XSAR subsets, the influence of halogenation (both the type of halogen and the place of substitution) was represented by a field whose spectrum of colors (purple to blue to red) were assigned to denote increases in the Xeffect level. The field was black when the Xeffect had been decreased by halogenation. The columns of the XSAR matrix indicate the Xeffect value of the halogenation type in the aromatic ring. A single row shows the Xeffect value for a given substitution position in the aromatic ring; however, if an XSAR set contains more than one 8728



Article

assess the influence of a given substituent on the binding, the ΔΔG was calculated as a difference between the binding free energy (ΔG) of a halogenated compound and its unsubstituted (parent) analog. Identification of Halogen Bonding Hot Spots. The docking results for the XSAR sets (which showed that the activity of the halogenated derivatives was greater than that of the parent) were used to determine the number of halogen bonding interactions with the side chains and carbonyl oxygen atoms of amino acids. Only amino acids forming a halogen bond with a distance lower than 4.00 Å, a σhole angle larger than 140°, and a ΔΔG lower than 0 were accepted. For individual receptor conformations, the halogen bonding interaction frequency (calculated as a number of halogen bonds formed by a given amino acid divided by all halogen bonds detected overall) was obtained to distinguish the most commonly interacting amino acids, which were depicted as hot spots (a frequency of >40% threshold was used), and those that were involved in the formation of the halogen bonding less frequently (20% < frequency 1.0 was hierarchically clustered using Moldprint2D, the Tanimoto metric, and the Complete Cluster Linking Method implemented in Canvas v2.0. The centroid was selected from each cluster and used as input for IFD. In each case, the grid box was anchored on D3.32 and allowed on residues refinement within 12 Å from ligand. Then, for each centroid the 10 top-scored L−R complexes were inspected visually to select those showing the closest compliance with the common binding mode for D4 receptor ligands.85 The final validation of the selected receptor conformations was performed by QPLD docking of the XSAR library, retaining at least one receptor conformation per cluster centroid from the XSAR library. Chemistry. Organic syntheses were carried out at ambient temperature, unless otherwise indicated. The organic solvents used in this study (Sigma-Aldrich and Chempur) were of reagent grade and were used without purification. All other commercially available reagents were of the highest purity (from Sigma-Aldrich, Fluorochem, and TCI). All workup and purification procedures were performed with reagent-grade solvents under ambient atmosphere. Column chromatography was performed using Merck 60 silica gel (70−230 mesh ASTM). Mass spectra were recorded on a UPLC−MS/MS system consisting of a Waters ACQUITY UPLC (Waters Corporation, Milford, MA, USA) coupled to a Waters TQD mass spectrometer (electrospray ionization (ESI) tandem quadrupole). Chromatographic separations were performed using an Acquity UPLC BEH (bridged ethyl hybrid) C18 column (2.1 mm × 100 mm, 1.7 μm particle size) equipped with an Acquity UPLC BEH C18 VanGuard precolumn

(2.1 mm × 5 mm, 1.7 μm particle size). The column was maintained at 40 °C, and elution was performed under gradient conditions from 95% to 0% of eluent A (water/formic acid (0.1%, v/v); eluent B was acetonitrile/formic acid (0.1%, v/v)) over 10 min at a flow rate of 0.3 mL min−1. Chromatograms were obtained using a Waters eλ PDA detector. The spectra were analyzed in the 200−700 nm range with a 1.2 nm resolution and a sampling rate of 20 points/s. The MS detection settings of the Waters TQD mass spectrometer were as follows: source temperature, 150 °C; desolvation temperature, 350 °C; desolvation gas flow rate, 600 L h−1; cone gas flow, 100 L h−1; capillary potential, 3.00 kV; and cone potential, 40 V. Nitrogen was used as both the nebulizing gas and the drying gas. The data were obtained in a scan mode ranging from 50 to 2000 m/z at 1.0 s intervals. MassLynx v 4.1 (Waters) was used as the data acquisition software. The UPLC/MS purity of all the final compounds was confirmed to be 95% or higher. 1 H NMR and 13C NMR spectra were obtained with a Varian BB 300 spectrometer using CDCl3 and were recorded at 300 and 75 MHz, respectively. The J values are reported in hertz (Hz), and the splitting patterns are designated as follows: s (singlet), br s (broad singlet), d (doublet), t (triplet), dd (doublet of doublets), dt (doublet of triplets), dq (double of quartets), td (triplet of doublets), ddd (doublet of doublet of doublets), dtd (doublet of triplets of doublets), and m (multiplet). The general procedures used for the synthesis of intermediate and final compounds were in accordance with previously reported methodology.86,87 Characterization Data for Representative Final Compounds. 2-Iodo-N-[1-(2-phenoxyethyl)piperidin-4-yl]benzenesulfonamide (61). Brown oil, 110 mg (yield 70%) following chromatographic purification over silica gel with CH2Cl2/MeOH (9/0.7 v/v); UPLC/MS purity 97%, tR = 4.69, C19H23IN2O3S, MW 486.37, monoisotopic mass 486.05, [M + H]+ 487.2. 1H NMR (300 MHz, CDCl3) δ 1.47−1.60 (m, 2H), 1.71−1.80 (m, 2H), 2.11−2.21 (m, 2H), 2.74 (t, J = 5.9 Hz, 2H), 2.78−2.85 (m, 2H), 3.13−3.23 (m, 1H), 4.03 (t, J = 5.6 Hz, 2H), 5.11 (d, J = 7.6 Hz, 1H), 6.83−6.89 (m, 2H), 6.90−6.97 (m, 1H), 7.22−7.30 (m, 2H), 7.38−7.51 (m, 2H), 7.71−7.75 (m, 1H), 8.16 (dd, J = 7.6, 1.8 Hz, 1H). 3-Chloro-N-[1-(2-phenoxyethyl)piperidin-4-yl]benzenesulfonamide (62). Yellow oil, 120 mg (yield 82%) following chromatographic purification over silica gel with CH2Cl2/MeOH (9/0.7 v/v); UPLC/MS purity 96%, tR = 4.61, C19H23ClN2O3S, MW 394.92, monoisotopic mass 394.11, [M + H]+ 395.1. 1H NMR (300 MHz, CDCl3) δ 1.43−1.58 (m, 2H), 1.73−1.85 (m, 2H), 2.14−2.27 (m, 2H), 2.76 (t, J = 5.8 Hz, 2H), 2.85 (dt, J = 12.2, 3.3 Hz, 2H), 3.15−3.30 (m, 1H), 4.05 (t, J = 5.8 Hz, 2H), 4.59 (d, J = 7.7 Hz, 1H), 6.85−6.89 (m, 2H), 6.94−6.96 (m, 1H), 7.24−7.29 (m, 2H), 7.29− 7.41 (m, 1H), 7.69 (ddd, J = 7.9, 1.9, 1.0 Hz, 1H), 7.78−7.83 (m, 1H), 8.03 (t, J = 1.8 Hz, 1H). N-[1-(3-Bromophenoxyethyl)piperidin-4-yl]benzenesulfonamide (73). Brown oil, 90 mg (yield 78%) following chromatographic purification over silica gel with CH2Cl2/MeOH (9/0.7 v/v); UPLC/ MS purity 97%, t R = 4.61, C 19 H 23 BrN 2 O 3 S, MW 439.37, monoisotopic mass 438.06, [M + H]+ 439.0. 1H NMR (300 MHz, CDCl3) δ 1.40−1.55 (m, 2H), 1.77 (dd, J = 12.9, 4.1 Hz, 2H), 2.12− 2.22 (m, 2H), 2.73 (t, J = 5.9 Hz, 2H), 2.76−2.85 (m, 2H), 3.13− 3.26 (m, 1H), 4.01 (t, J = 5.9 Hz, 2H), 4.45 (d, J = 7.6 Hz, 1H), 6.77−6.82 (m, 1H), 7.02−7.08 (m, 2H), 7.08−7.15 (m, 1H), 7.48− 7.61 (m, 3H), 7.86−7.91 (m, 2H). 13C NMR (75 MHz, CDCl3) δ 29.7, 32.9, 50.5, 51.7, 56.8, 66.1, 113.0, 117.8, 122.8, 123.9, 126.8, 129.1, 130.5, 132.6, 141.2, 159.4. N-{1-[2-(3-Tolyloxy)ethyl]piperidin-4-yl}benzenesulfonamide (75). Brown oil, 80 mg (yield 65%) following chromatographic purification over silica gel with CH2Cl2/MeOH (9/0.7 v/v); UPLC/ MS purity 97%, tR = 4.03, C21H26N2O3S, MW 374.50, monoisotopic mass 374.17, [M + H]+ 375.1. 1H NMR (300 MHz, CDCl3) δ 1.45− 1.56 (m, 2H), 1.72−1.84 (m, 2H), 2.13−2.23 (m, 2H), 2.31 (s, 3H), 2.74 (t, J = 5.8 Hz, 2H), 2.83 (dt, J = 12.1, 3.3 Hz, 2H), 3.14−3.25 (m, 1H), 4.02 (t, J = 5.8 Hz, 2H), 4.58 (br s, 1H), 6.64−6.70 (m, 8729



Article

2H), 6.73−6.79 (m, 1H), 7.14 (t, J = 7.9 Hz, 1H), 7.47−7.61 (m, 3H), 7.86−7.92 (m, 2H). N-{1-[2-(3-Isopropylphenoxy)ethyl]piperidin-4-yl}benzenesulfonamide (77). Brown oil, 80 mg (yield 65%) following chromatographic purification over silica gel with CH2Cl2/MeOH (9/0.7 v/v); UPLC/MS purity 96%, tR = 4.09, C22H30N2O3S, MW 402.55, monoisotopic mass 402.20, [M + H]+ 403.3. 1H NMR (300 MHz, CDCl3) δ 1.22 (d, J = 6.5 Hz, 6H), 1.41−1.55 (m, 2H), 1.69− 1.82 (m, 2H), 2.12−2.22 (m, 2H), 2.74 (t, J = 5.9 Hz, 2H), 2.80− 2.90 (m, 3H), 3.15−3.25 (m, 1H), 4.03 (t, J = 5.6 Hz, 2H), 4.47 (br s, 1H), 6.68 (dd, J = 8.1, 2.3 Hz, 1H), 6.74−6.76 (m, 1H), 6.81 (d, J = 7.6, 1H), 7.18 (t, J = 7.9 Hz, 1H), 7.47−7.61 (m, 3H), 7.85−7.91 (m, 2H). 13C NMR (75 MHz, CDCl3) δ 23.9, 32.9, 34.2, 50.6, 52.3, 57.1, 65.7, 111.3, 113.1, 119.1, 126.9, 129.1, 129.2, 132.6, 141.2, 150.6, 158.7. N-(1-{2-[3-(tert-Butyl)phenoxy]ethyl}piperidin-4-yl)benzenesulfonamide (78). Brown oil, 80 mg (yield 65%) following chromatographic purification over silica gel with CH2Cl2/MeOH (9/0.7 v/v); UPLC/MS purity 97%, tR = 5.35, C23H32N2O3S, MW 416.58, monoisotopic mass 416.21, [M + H]+ 417.3. 1H NMR (300 MHz, CDCl3) δ 1.29 (s, 9H), 1.44−1.56 (m, 2H), 1.73−1.83 (m, 2H), 2.12−2.22 (m, 2H), 2.75 (t, J = 5.9 Hz, 2H), 2.80−2.89 (m, 2H), 3.16−3.25 (m, 1H), 4.04 (t, J = 5.9 Hz, 2H), 4.44 (br s, 1H), 6.65−6.70 (m, 1H), 6.90−6.92 (m, 1H), 6.98 (dd, J = 7.0, 1.8 Hz, 1H), 7.17−7.23 (m, 1H), 7.48−7.58 (m, 3H), 7.86−7.91 (m, 2H). N-[1-(4-Chlorophenoxyethyl)piperidin-4-yl]benzenesulfonamide (80). Brown oil, 110 mg (yield 69%) following chromatographic purification over silica gel with CH2Cl2/MeOH (9/0.7 v/v); UPLC/ MS purity 97%, t R = 4.53, C 19 H 23 ClN 2 O 3 S, MW 394.92, monoisotopic mass 394.11, [M + H]+ 395.2. 1H NMR (300 MHz, CDCl3) δ 1.45−1.55 (m, 2H), 1.73−1.83 (m, 2H), 2.12−2.22 (m, 2H), 2.73 (t, J = 5.8 Hz, 2H), 2.78−2.86 (m, 2H), 3.14−3.27 (m, 1H), 4.00 (t, J = 5.8 Hz, 2H), 4.55 (br s, 1H), 6.71−6.80 (m, 2H), 7.32−7.39 (m, 2H), 7.48−7.61 (m, 3H), 7.86−7.91 (m, 2H). 13C NMR (75 MHz, CDCl3) δ 32.8, 50.5, 52.3, 56.9, 65.4, 115.8, 125.7, 126.8, 128.7, 129.3, 132.6, 141.2, 157.23. N-{1-[2-(3-Bromo-2-iodophenoxy)ethyl]piperidin-4-yl}benzenesulfonamide (82). Brown oil, 120 mg (yield 73%) following chromatographic purification over silica gel with CH2Cl2/MeOH (9/0.5 v/v); UPLC/MS purity 97%, tR = 5.08, C19H22BrIN2O3S, MW 565.26, monoisotopic mass 563.96, [M + H]+ 565.0. 1H NMR (300 MHz, CDCl3) δ 1.41−1.55 (m, 2H), 1.77 (dd, J = 12.9, 3.5 Hz, 2H), 2.19−2.30 (m, 2H), 2.79−2.85 (m, 2H), 2.85−2.92 (m, 2H), 3.10− 3.26 (m, 1H), 4.07 (t, J = 5.3 Hz, 2H), 4.75 (d, J = 7.0 Hz, 1H), 6.65 (dd, J = 8.2, 1.2 Hz, 1H), 7.08−7.15 (m, 1H), 7.22−7.29 (m, 1H), 7.46−7.62 (m, 3H), 7.83−7.94 (m, 2H). 13C NMR (75 MHz, CDCl3) δ 33.0, 50.5, 52.5, 56.6, 68.4, 94.9, 109.9, 110.0, 125.3, 126.9, 129.1, 130.1, 131.1, 132.6, 141.1, 159.3. 2-Bromo-N-{1-[2-(3-bromophenoxy)ethyl]piperidin-4-yl}benzenesulfonamide (84). Brown oil, 110 mg (yield 74%) following chromatographic purification over silica gel with CH2Cl2/MeOH (9/ 0.5 v/v); LC/MS purity 98%, tR = 4.94, C19H22Br2N2O3S, MW 518.26, monoisotopic mass 515.97, [M + H]+ 517.1, [M + H]+ 519.1. 1 H NMR (300 MHz, CDCl3) δ 1.46−1.56 (m, 2H), 1.70−1.80 (m, 2H), 2.10−2.20 (m, 2H), 2.72 (t, J = 5.7 Hz, 2H), 2.78−2.83 (m, 2H), 3.13−3.23 (m, 1H), 4.00 (t, J = 5.7 Hz, 2H), 5.10 (br s, 1H), 6.77−6.81 (m, 1H), 7.02−7.08 (m, 2H), 7.09−7.15 (m, 1H), 7.39− 7.51 (m, 2H), 7.73 (dd, J = 7.7, 1.5 Hz, 1H), 8.16 (dd, J = 7.7, 1.5 Hz, 1H). 13C NMR (75 MHz, CDCl3) δ 32.5, 50.8, 52.2, 56.8, 66.2, 113.5, 117.9, 119.7, 122.8, 123.9, 127.9, 130.5, 131.2, 133.7, 135.1, 140.0, 159.4.

■

■

molecular dynamic simulations and homology modeling, and additional synthesis procedures (PDF) PDB and SDF chemical structure files (ZIP) Molecular formula strings and some data (CSV)

AUTHOR INFORMATION

Corresponding Author

*E-mail: [email protected]. ORCID

Rafał Kurczab: 0000-0002-9555-3905 Andrzej J. Bojarski: 0000-0003-1417-6333 Notes

The authors declare no competing financial interest.

■

ACKNOWLEDGMENTS The study was supported by the National Science Center Grant DEC-2014/15/D/NZ7/01782.

■

REFERENCES

(1) Lu, Y.; Wang, Y.; Zhu, W. Nonbonding Interactions of Organic Halogens in Biological Systems: Implications for Drug Discovery and Biomolecular Design. Phys. Chem. Chem. Phys. 2010, 12 (18), 4543− 4551. (2) Baumli, S.; Endicott, J. A.; Johnson, L. N. Halogen Bonds Form the Basis for Selective P-TEFb Inhibition by DRB. Chem. Biol. 2010, 17 (9), 931−936. (3) Kolár,̌ M.; Hostaš, J.; Hobza, P. The Strength and Directionality of a Halogen Bond Are Co-Determined by the Magnitude and Size of the σ-Hole. Phys. Chem. Chem. Phys. 2014, 16 (21), 9987−9996. (4) Beale, T. M.; Chudzinski, M. G.; Sarwar, M. G.; Taylor, M. S. Halogen Bonding in Solution: Thermodynamics and Applications. Chem. Soc. Rev. 2013, 42 (4), 1667−1680. (5) Kolár,̌ M.; Hobza, P. On Extension of the Current Biomolecular Empirical Force Field for the Description of Halogen Bonds. J. Chem. Theory Comput. 2012, 8 (4), 1325−1333. (6) Koebel, M. R.; Schmadeke, G.; Posner, R. G.; Sirimulla, S. AutoDock VinaXB: Implementation of XBSF, New Empirical Halogen Bond Scoring Function, into AutoDock Vina. J. Cheminf. 2016, 8 (1), 27. (7) Hardegger, L. A.; Kuhn, B.; Spinnler, B.; Anselm, L.; Ecabert, R.; Stihle, M.; Gsell, B.; Thoma, R.; Diez, J.; Benz, J.; Plancher, J. M.; Hartmann, G.; Banner, D. W.; Haap, W.; Diederich, F. Systematic Investigation of Halogen Bonding in Protein-Ligand Interactions. Angew. Chem., Int. Ed. 2011, 50 (1), 314−318. (8) Zimmermann, M. O.; Lange, A.; Boeckler, F. M. Evaluating the Potential of Halogen Bonding in Molecular Design: Automated Scaffold Decoration Using the New Scoring Function Xbscore. J. Chem. Inf. Model. 2015, 55 (3), 687−699. (9) Grabowski, S. J. Hydrogen and Halogen Bonds Are Ruled by the Same Mechanisms. Phys. Chem. Chem. Phys. 2013, 15 (19), 7249− 7259. (10) Kozuch, S.; Martin, J. M. L. Halogen Bonds: Benchmarks and Theoretical Analysis. J. Chem. Theory Comput. 2013, 9 (4), 1918− 1931. (11) Clark, T.; Hennemann, M.; Murray, J. S.; Politzer, P. Halogen Bonding: The ??-Hole: Proceedings of “Modeling Interactions in Biomolecules II”, Prague, September 5th-9th, 2005. J. Mol. Model. 2007, 13 (2), 291−296. (12) Eskandari, K.; Zariny, H. Halogen Bonding: A Lump−hole Interaction. Chem. Phys. Lett. 2010, 492 (1−3), 9−13. (13) Brinck, T.; Murray, J. S.; Politzer, P. Surface Electrostatic Potentials of Halogenated Methanes as Indicators of Directional Intermolecular Interactions. Int. J. Quantum Chem. 1992, 44 (S19), 57−64.

ASSOCIATED CONTENT

* Supporting Information S

The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acs.jmedchem.8b00828. Algorithm flowchart for XSAR subset generation, XSAR matrix, affnity changes, interaction points, details of 8730



Article

(14) Bauzá, A.; Mooibroek, T. J.; Frontera, A. The Bright Future of Unconventional σ/π-Hole Interactions. ChemPhysChem 2015, 16 (12), 2496−2517. (15) Farina, A.; Meille, S. V.; Messina, M. T.; Metrangolo, P.; Resnati, G.; Vecchio, G. Resolution of Racemic 1,2-Dibromohexafluoropropane through Halogen-Bonded Supramolecular Helices. Angew. Chem., Int. Ed. 1999, 38 (16), 2433−2436. (16) Messina, M. T.; Metrangolo, P.; Panzeri, W.; Ragg, E.; Resnati, G. Perfluorocarbon-Hydrocarbon Self-Assembly. Part 3. Liquid Phase Interactions between Perfluoroalkylhalides and Heteroatom Containing Hydrocarbons. Tetrahedron Lett. 1998, 39 (49), 9069−9072. (17) Legon, A. C. Prereactive Complexes of Dihalogens XY with Lewis Bases B in the Gas Phase: A Systematic Case for the Halogen Analogue B···XY of the Hydrogen Bond B···HX. Angew. Chem., Int. Ed. 1999, 38 (18), 2686−2714. (18) Langton, M. J.; Robinson, S. W.; Marques, I.; Félix, V.; Beer, P. D. Halogen Bonding in Water Results in Enhanced Anion Recognition in Acyclic and Rotaxane Hosts. Nat. Chem. 2014, 6 (12), 1039−1043. (19) Zapata, F.; Benítez-Benítez, S.; Sabater, P.; Caballero, A.; Molina, P. Modulation of the Selectivity in Anions Recognition Processes by Combining Hydrogen- and Halogen-Bonding Interactions. Molecules 2017, 22 (12), 2273. (20) Brown, A.; Beer, P. D. Halogen Bonding Anion Recognition. Chem. Commun. 2016, 52 (56), 8645−8658. (21) Metrangolo, P.; Neukirch, H.; Pilati, T.; Resnati, G. Halogen Bonding Based Recognition Processes: A World Parallel to Hydrogen Bonding †. Acc. Chem. Res. 2005, 38 (5), 386−395. (22) Meyer, F.; Dubois, P. Halogen Bonding at Work: Recent Applications in Synthetic Chemistry and Materials Science. CrystEngComm 2013, 15 (16), 3058−3071. (23) Fanfrlík, J.; Kolár,̌ M.; Kamlar, M.; Hurný, D.; Ruiz, F. X.; Cousido-Siah, A.; Mitschler, A.; Ř ezác,̌ J.; Munusamy, E.; Lepšík, M.; Matějíček, P.; Veselý, J.; Podjarny, A.; Hobza, P. Modulation of Aldose Reductase Inhibition by Halogen Bond Tuning. ACS Chem. Biol. 2013, 8 (11), 2484−2492. (24) Hernandes, M.; Cavalcanti, S. M.; Moreira, D. R.; de Azevedo Junior, W.; Leite, A. C. Halogen Atoms in the Modern Medicinal Chemistry: Hints for the Drug Design. Curr. Drug Targets 2010, 11 (3), 303−314. (25) Ford, M. C.; Ho, P. S. Computational Tools to Model Halogen Bonds in Medicinal Chemistry. J. Med. Chem. 2016, 59, 1655−1670. (26) Wilcken, R.; Zimmermann, M. O.; Lange, A.; Joerger, A. C.; Boeckler, F. M. Principles and Applications of Halogen Bonding in Medicinal Chemistry and Chemical Biology. J. Med. Chem. 2013, 56, 1363−1388. (27) Sirimulla, S.; Bailey, J. B.; Vegesna, R.; Narayan, M. Halogen Interactions in Protein-Ligand Complexes: Implications of Halogen Bonding for Rational Drug Design. J. Chem. Inf. Model. 2013, 53 (11), 2781−2791. (28) Lovenberg, T. W.; Baron, B. M.; de Lecea, L.; Miller, J. D.; Prosser, R. A.; Rea, M. A.; Foye, P. E.; Racke, M.; Slone, A. L.; Siegel, B. W.; Danielson, P. E.; Sutcliffe, J. G.; Erlander, M. G. A Novel Adenylyl Cyclase-Activating Serotonin Receptor (5-HT7) Implicated in the Regulation of Mammalian Circadian Rhythms. Neuron 1993, 11 (3), 449−458. (29) Kvachnina, E.; Liu, G.; Dityatev, A.; Renner, U.; Dumuis, A.; Richter, D. W.; Dityateva, G.; Schachner, M.; Voyno-Yasenetskaya, T. A.; Ponimaskin, E. G. 5-HT7 Receptor Is Coupled to G Subunits of Heterotrimeric G12-Protein to Regulate Gene Transcription and Neuronal Morphology. J. Neurosci. 2005, 25 (34), 7821−7830. (30) Matthys, A.; Haegeman, G.; Van Craenenbroeck, K.; Vanhoenacker, P. Role of the 5-HT7 Receptor in the Central Nervous System: From Current Status to Future Perspectives. Mol. Neurobiol. 2011, 43 (3), 228−253. (31) Nikiforuk, A. Targeting the Serotonin 5-HT7 Receptor in the Search for Treatments for CNS Disorders: Rationale and Progress to Date. CNS Drugs 2015, 29 (4), 265−275.

(32) Hashemi-Firouzi, N.; Komaki, A.; Soleimani Asl, S.; Shahidi, S. The Effects of the 5-HT7 Receptor on Hippocampal Long-Term Potentiation and Apoptosis in a Rat Model of Alzheimer’s Disease. Brain Res. Bull. 2017, 135, 85−91. (33) Costa, L.; Sardone, L. M.; Lacivita, E.; Leopoldo, M.; Ciranna, L. Novel Agonists for Serotonin 5-HT7 Receptors Reverse Metabotropic Glutamate Receptor-Mediated Long-Term Depression in the Hippocampus of Wild-Type and Fmr1 KO Mice, a Model of Fragile X Syndrome. Front. Behav. Neurosci. 2015, 9, 65. (34) Ruocco, L. A.; Treno, C.; Gironi Carnevale, U. A.; Arra, C.; Boatto, G.; Nieddu, M.; Pagano, C.; Illiano, P.; Barbato, F.; Tino, A.; Carboni, E.; Laviola, G.; Lacivita, E.; Leopoldo, M.; Adriani, W.; Sadile, A. G. Prepuberal Stimulation of 5-HT7-R by LP-211 in a Rat Model of Hyper-Activity and Attention-Deficit: Permanent Effects on Attention, Brain Amino Acids and Synaptic Markers in the FrontoStriatal Interface. PLoS One 2014, 9 (4), e83003. (35) Guscott, M.; Bristow, L. J.; Hadingham, K.; Rosahl, T. W.; Beer, M. S.; Stanton, J. A.; Bromidge, F.; Owens, A. P.; Huscroft, I.; Myers, J.; Rupniak, N. M.; Patel, S.; Whiting, P. J.; Hutson, P. H.; Fone, K. C.; Biello, S. M.; Kulagowski, J. J.; McAllister, G. Genetic Knockout and Pharmacological Blockade Studies of the 5-HT7 Receptor Suggest Therapeutic Potential in Depression. Neuropharmacology 2005, 48 (4), 492−502. (36) Sarkisyan, G.; Roberts, A. J.; Hedlund, P. B. The 5-HT(7) Receptor as a Mediator and Modulator of Antidepressant-like Behavior. Behav. Brain Res. 2010, 209 (1), 99−108. (37) Wesołowska, A.; Tatarczyńska, E.; Nikiforuk, A.; ChojnackaWój cik, E. Enhancement of the Anti-Immobility Action of Antidepressants by a Selective 5-HT7 Receptor Antagonist in the Forced Swimming Test in Mice. Eur. J. Pharmacol. 2007, 555 (1), 43−47. (38) Freret, T.; Paizanis, E.; Beaudet, G.; Gusmao-Montaigne, A.; Nee, G.; Dauphin, F.; Bouet, V.; Boulouard, M. Modulation of 5-HT7 Receptor: Effect on Object Recognition Performances in Mice. Psychopharmacology (Berl). 2014, 231 (2), 393−400. (39) Nikiforuk, A.; Kos, T.; Fijal, K.; Holuj, M.; Rafa, D.; Popik, P. Effects of the Selective 5-HT7 Receptor Antagonist SB-269970 and Amisulpride on Ketamine-Induced Schizophrenia-like Deficits in Rats. PLoS One 2013, 8 (6), e66695. (40) Gaulton, A.; Bellis, L. J.; Bento, a P.; Chambers, J.; Davies, M.; Hersey, A.; Light, Y.; McGlinchey, S.; Michalovich, D.; Al-Lazikani, B.; Overington, J. P. ChEMBL: A Large-Scale Bioactivity Database for Drug Discovery. Nucleic Acids Res. 2012, 40 (Database Issue), D1100−D1107. (41) Zajdel, P.; Kurczab, R.; Grychowska, K.; Satała, G.; Pawłowski, M.; Bojarski, A. J. The Multiobjective Based Design, Synthesis and Evaluation of the Arylsulfonamide/Amide Derivatives of Aryloxyethyl- and Arylthioethyl- Piperidines and Pyrrolidines as a Novel Class of Potent 5-HT(7) Receptor Antagonists. Eur. J. Med. Chem. 2012, 56, 348−360. (42) Kurczab, R. The Evaluation of QM/MM-Driven Molecular Docking Combined with MM/GBSA Calculations as a Halogen-Bond Scoring Strategy. Acta Crystallogr., Sect. B: Struct. Sci., Cryst. Eng. Mater. 2017, 73 (2), 188−194. (43) Kim, Y.; Tae, J.; Lee, K.; Rhim, H.; Choo, I. H.; Cho, H.; Park, W.-K.; Keum, G.; Choo, H. Novel N-Biphenyl-2-Ylmethyl 2Methoxyphenylpiperazinylalkanamides as 5-HT7R Antagonists for the Treatment of Depression. Bioorg. Med. Chem. 2014, 22 (17), 4587−4596. (44) Kołaczkowski, M.; Marcinkowska, M.; Bucki, A.; Pawłowski, M.; Mitka, K.; Jaśkowska, J.; Kowalski, P.; Kazek, G.; Siwek, A.; Wasik, A.; Wesołowska, A.; Mierzejewski, P.; Bienkowski, P. Novel Arylsulfonamide Derivatives with 5-HT 6 /5-HT 7 Receptor Antagonism Targeting Behavioral and Psychological Symptoms of Dementia. J. Med. Chem. 2014, 57 (11), 4543−4557. (45) Yoon, J.; Yoo, E. A.; Kim, J.-Y.; Pae, A. N.; Rhim, H.; Park, W.K.; Kong, J. Y.; Park Choo, H.-Y. Preparation of Piperazine Derivatives as 5-HT7 Receptor Antagonists. Bioorg. Med. Chem. 2008, 16 (10), 5405−5412. 8731



Article

(60) JChem, version 18.3.0; ChemAxon, 2018; https://chemaxon. com/. (61) Sud, M. MayaChemTools: An Open Source Package for Computational Drug Discovery. J. Chem. Inf. Model. 2016, 56 (12), 2292−2297. (62) The UniProt Consortium.. Reorganizing the Protein Space at the Universal Protein Resource (UniProt). Nucleic Acids Res. 2012, 40 (Database Issue), D71−D75. (63) Š ali, A.; Blundell, T. L. Comparative Protein Modelling by Satisfaction of Spatial Restraints. J. Mol. Biol. 1993, 234 (3), 779−815. (64) Nowak, M.; Kołaczkowski, M.; Pawłowski, M.; Bojarski, A. J. Homology Modeling of the Serotonin 5-HT 1A Receptor Using Automated Docking of Bioactive Compounds with Defined Geometry. J. Med. Chem. 2006, 49 (1), 205−214. (65) Rataj, K.; Witek, J.; Mordalski, S.; Kosciolek, T.; Bojarski, A. J. Impact of Template Choice on Homology Model Efficiency in Virtual Screening. J. Chem. Inf. Model. 2014, 54 (6), 1661−1668. (66) Schrödinger Release 2017-1: Canvas; Schrödinger, LLC: New York, NY, 2017. (67) Schrödinger Release 2017-3: LigPrep; Schrödinger, LLC: New York, NY, 2017. (68) Schrödinger Release 2017-3: Epik; Schrödinger, LLC: New York, NY, 2017. (69) Harder, E.; Damm, W.; Maple, J.; Wu, C.; Reboul, M.; Xiang, J. Y.; Wang, L.; Lupyan, D.; Dahlgren, M. K.; Knight, J. L.; Kaus, J. W.; Cerutti, D. S.; Krilov, G.; Jorgensen, W. L.; Abel, R.; Friesner, R. A. OPLS3: A Force Field Providing Broad Coverage of Drug-like Small Molecules and Proteins. J. Chem. Theory Comput. 2016, 12 (1), 281− 296. (70) Schrödinger Release 2017-3: Glide; Schrödinger, LLC: New York, NY, 2017. (71) Cho, A. E.; Guallar, V.; Berne, B. J.; Friesner, R. Importance of Accurate Charges in Molecular Docking: Quantum Mechanical/ Molecular Mechanical (QM/MM) Approach. J. Comput. Chem. 2005, 26 (9), 915−931. (72) Philipp, D. M.; Friesner, R. A. Mixed Ab Initio QM/MM Modeling Using Frozen Orbitals and Tests with Alanine Dipeptide and Tetrapeptide. J. Comput. Chem. 1999, 20 (14), 1468−1494. (73) Murphy, R. B.; Philipp, D. M.; Friesner, R. A. A Mixed Quantum Mechanics/Molecular Mechanics (QM/MM) Method for Large-Scale Modeling of Chemistry in Protein Environments. J. Comput. Chem. 2000, 21 (16), 1442−1457. (74) Bochevarov, A. D.; Harder, E.; Hughes, T. F.; Greenwood, J. R.; Braden, D. A.; Philipp, D. M.; Rinaldo, D.; Halls, M. D.; Zhang, J.; Friesner, R. A. Jaguar: A High-Performance Quantum Chemistry Software Program with Strengths in Life and Materials Sciences. Int. J. Quantum Chem. 2013, 113 (18), 2110−2142. (75) Perdew, J.; Chevary, J.; Vosko, S.; Jackson, K.; Pederson, M.; Singh, D.; Fiolhais, C. Erratum: Atoms, Molecules, Solids, and Surfaces: Applications of the Generalized Gradient Approximation for Exchange and Correlation. Phys. Rev. B: Condens. Matter Mater. Phys. 1993, 48 (7), 4978−4978. (76) Slater, J. C. A Simplification of the Hartree-Fock Method. Phys. Rev. 1951, 81 (3), 385−390. (77) Becke, A. D. Density-Functional Thermochemistry. III. The Role of Exact Exchange. J. Chem. Phys. 1993, 98 (7), 5648−5652. (78) Becke, A. D. Density-Functional Exchange-Energy Approximation with Correct Asymptotic Behavior. Phys. Rev. A: At., Mol., Opt. Phys. 1988, 38 (6), 3098−3100. (79) Varadwaj, P. R.; Varadwaj, A.; Jin, B.-Y. Halogen Bonding Interaction of Chloromethane with Several Nitrogen Donating Molecules: Addressing the Nature of the Chlorine Surface σ-Hole. Phys. Chem. Chem. Phys. 2014, 16 (36), 19573−19589. (80) Politzer, P.; Murray, J.; Janjić, G.; Zarić, S. σ-Hole Interactions of Covalently-Bonded Nitrogen, Phosphorus and Arsenic: A Survey of Crystal Structures. Crystals 2014, 4 (1), 12−31. (81) Politzer, P.; Lane, P.; Concha, M. C.; Ma, Y.; Murray, J. S. An Overview of Halogen Bonding. J. Mol. Model. 2007, 13 (2), 305−311.

(46) Modica, M. N.; Intagliata, S.; Pittalà, V.; Salerno, L.; Siracusa, M. A.; Cagnotto, A.; Salmona, M.; Romeo, G. Synthesis and Binding Properties of New Long-Chain 4-Substituted Piperazine Derivatives as 5-HT1A and 5-HT7 Receptor Ligands. Bioorg. Med. Chem. Lett. 2015, 25 (7), 1427−1430. (47) Canale, V.; Guzik, P.; Kurczab, R.; Verdie, P.; Satała, G.; Kubica, B.; Pawłowski, M.; Martinez, J.; Subra, G.; Bojarski, A. J.; Zajdel, P. Solid-Supported Synthesis, Molecular Modeling, and Biological Activity of Long-Chain Arylpiperazine Derivatives with Cyclic Amino Acid Amide Fragments as 5-HT(7) and 5-HT(1A) Receptor Ligands. Eur. J. Med. Chem. 2014, 78, 10−22. (48) Auffinger, P.; Hays, F.; Westhof, E.; Ho, P. S. Halogen Bonds in Biological Molecules. Proc. Natl. Acad. Sci. U. S. A. 2004, 101 (48), 16789−16794. (49) Saraogi, I.; Vijay, V. G.; Das, S.; Sekar, K.; Guru Row, T. N. C− halogen...π Interactions in Proteins: A Database Study. Cryst. Eng. 2003, 6 (2), 69−77. (50) Imai, Y. N.; Inoue, Y.; Nakanishi, I.; Kitaura, K. Cl-π Interactions in Protein-Ligand Complexes. Protein Sci. 2008, 17 (7), 1129−1137. (51) Heightman, T. D.; Berdini, V.; Braithwaite, H.; Buck, I. M.; Cassidy, M.; Castro, J.; Courtin, A.; Day, J. E. H.; East, C.; Fazal, L.; Graham, B.; Griffiths-Jones, C. M.; Lyons, J. F.; Martins, V.; Muench, S.; Munck, J. M.; Norton, D.; O’Reilly, M.; Palmer, N.; Pathuri, P.; Reader, M.; Rees, D. C.; Rich, S. J.; Richardson, C.; Saini, H.; Thompson, N. T.; Wallis, N. G.; Walton, H.; Wilsher, N. E.; Woolford, A. J.-A.; Cooke, M.; Cousin, D.; Onions, S.; Shannon, J.; Watts, J.; Murray, C. W. Fragment-Based Discovery of a Potent, Orally Bioavailable Inhibitor That Modulates the Phosphorylation and Catalytic Activity of ERK1/2. J. Med. Chem. 2018, 61 (11), 4978−4992. (52) Smith, T. P.; Windsor, I. W.; Forest, K. T.; Raines, R. T. Stilbene Boronic Acids Form a Covalent Bond with Human Transthyretin and Inhibit Its Aggregation. J. Med. Chem. 2017, 60 (18), 7820−7834. (53) Fedorov, O.; Huber, K.; Eisenreich, A.; Filippakopoulos, P.; King, O.; Bullock, A. N.; Szklarczyk, D.; Jensen, L. J.; Fabbro, D.; Trappe, J.; Rauch, U.; Bracher, F.; Knapp, S. Specific CLK Inhibitors from a Novel Chemotype for Regulation of Alternative Splicing. Chem. Biol. 2011, 18 (1), 67−76. (54) Trbovic, N.; Cho, J.-H.; Abel, R.; Friesner, R. A.; Rance, M.; Palmer, A. G. Protein Side-Chain Dynamics and Residual Conformational Entropy. J. Am. Chem. Soc. 2009, 131 (2), 615−622. (55) Canale, V.; Kurczab, R.; Partyka, A.; Satała, G.; Lenda, T.; Jastrzębska-Więsek, M.; Wesołowska, A.; Bojarski, A. J.; Zajdel, P. Towards New 5-HT7 Antagonists among Arylsulfonamide Derivatives of (Aryloxy)ethyl-alkyl Amines: Multiobjective Based Design, Synthesis, and Antidepressant and Anxiolytic Properties. Eur. J. Med. Chem. 2016, 108, 334−346. (56) Lange, A.; Günther, M.; Büttner, F. M.; Zimmermann, M. O.; Heidrich, J.; Hennig, S.; Zahn, S.; Schall, C.; Sievers-Engler, A.; Ansideri, F.; Koch, P.; Laemmerhofer, M.; Stehle, T.; Laufer, S. A.; Boeckler, F. M. Targeting the Gatekeeper MET146 of C-Jun NTerminal Kinase 3 Induces a Bivalent Halogen/Chalcogen Bond. J. Am. Chem. Soc. 2015, 137 (46), 14640−14652. (57) Monsma, F. J.; Shen, Y.; Ward, R. P.; Hamblin, M. W.; Sibley, D. R. Cloning and Expression of a Novel Serotonin Receptor with High Affinity for Tricyclic Psychotropic Drugs. Mol. Pharmacol. 1993, 43 (3), 320−327. (58) Zimmermann, M. O.; Lange, A.; Wilcken, R.; Cieslik, M. B.; Exner, T. E.; Joerger, A. C.; Koch, P.; Boeckler, F. M. HalogenEnriched Fragment Libraries as Chemical Probes for Harnessing Halogen Bonding in Fragment-Based Lead Discovery. Future Med. Chem. 2014, 6 (6), 617−639. (59) Wilcken, R.; Liu, X.; Zimmermann, M. O.; Rutherford, T. J.; Fersht, A. R.; Joerger, A. C.; Boeckler, F. M. Halogen-Enriched Fragment Libraries as Leads for Drug Rescue of Mutant P53. J. Am. Chem. Soc. 2012, 134 (15), 6810−6818. 8732



Article

(82) Politzer, P.; Murray, J. S.; Concha, M. C. Halogen Bonding and the Design of New Materials: Organic Bromides, Chlorides and Perhaps Even Fluorides as Donors. J. Mol. Model. 2007, 13 (6−7), 643−650. (83) Dunning, T. H., Jr Gaussian Basis Sets for Use in Correlated Molecular Calculations. I. The Atoms Boron through Neon and Hydrogen. J. Chem. Phys. 1989, 90 (1989), 1007−1023. (84) Lomize, M. A.; Pogozheva, I. D.; Joo, H.; Mosberg, H. I.; Lomize, A. L. OPM Database and PPM Web Server: Resources for Positioning of Proteins in Membranes. Nucleic Acids Res. 2012, 40 (D1), D370−D376. (85) Wang, S.; Wacker, D.; Levit, A.; Che, T.; Betz, R. M.; McCorvy, J. D.; Venkatakrishnan, A. J.; Huang, X.-P.; Dror, R. O.; Shoichet, B. K.; Roth, B. L. D4 Dopamine Receptor High-Resolution Structures Enable the Discovery of Selective Agonists. Science (Washington, DC, U. S.) 2017, 358 (6361), 381−386. (86) Zajdel, P.; Canale, V.; Partyka, A.; Marciniec, K.; Kurczab, R.; Satała, G.; Siwek, A.; Jastrzębska-Więsek, M.; Wesołowska, A.; Kos, T.; Popik, P.; Bojarski, A. J. Arylsulfonamide Derivatives of (Aryloxy)Ethylpiperidines as Selective 5-HT7 Receptor Antagonists and Their Psychotropic Properties. MedChemComm 2015, 6 (7), 1272−1277. (87) Canale, V.; Kurczab, R.; Partyka, A.; Satała, G.; Słoczyńska, K.; Kos, T.; Jastrzębska-Więsek, M.; Siwek, A.; Pękala, E.; Bojarski, A. J.; Wesołowska, A.; Popik, P.; Zajdel, P. N-Alkylated Arylsulfonamides of (Aryloxy)Ethyl Piperidines: 5-HT(7) Receptor Selectivity versus Multireceptor Profile. Bioorg. Med. Chem. 2016, 24 (2), 130−139.

8733


Amino Acid Hot Spots of Halogen Bonding: A Combined Theoretical

Recommend Documents