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Article Profiling the Structural Determinants of Aryl Benzamide Derivatives as Negative Allosteric Modulators of mGluR5 by In Silico Study

Yujing Zhao 1, Jiabin Chen 1, Qilei Liu 2 and Yan Li 1,* 1 Key Laboratory of Industrial Ecology and Environmental Engineering, Faculty of Chemical, Environmental and Biological Science and Technology, Dalian University of Technology, Dalian 116024, China; [email protected] (Y.Z.); [email protected] (J.C.) 2 Institute of Chemical Process Systems Engineering, School of Chemical Engineering, Dalian University of Technology, Dalian 116024, China; [email protected] * Correspondence: [email protected]; Tel.: +86-15640888728



Received: 7 December 2019; Accepted: 16 January 2020; Published: 18 January 2020


Abstract: Glutamate plays a crucial role in the treatment of depression by interacting with the metabotropic glutamate receptor subtype 5 (mGluR5), whose negative allosteric modulators (NAMs) are thus promising antidepressants. At present, to explore the structural features of 106 newly synthesized aryl benzamide series molecules as mGluR5 NAMs, a set of ligand-based three-dimensional quantitative structure-activity relationship (3D-QSAR) analyses were firstly carried out applying comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) methods. In addition, receptor-based analysis, namely molecular docking and molecular dynamics (MD) simulations, were performed to further elucidate the binding modes of mGluR5 NAMs. As a result, the optimal CoMSIA model obtained shows that cross-validated 2 2 correlation coefficient Q = 0.70, non-cross-validated correlation coefficient R ncv = 0.89, predicted 2 correlation coefficient R pre = 0.87. Moreover, we found that aryl benzamide series molecules bind as mGluR5 NAMs at Site 1, which consists of amino acids Pro655, Tyr659, Ile625, Ile651, Ile944, Ser658, Ser654, Ser969, Ser965, Ala970, Ala973, Trp945, Phe948, Pro903, Asn907, Val966, Leu904, and Met962. This site is the same as that of other types of NAMs; mGluR5 NAMs are stabilized in the “linear” and “arc” configurations mainly through the H-bonds interactions, π–π stacking interaction with Trp945, and hydrophobic contacts. We hope that the models and information obtained will help understand the interaction mechanism of NAMs and design and optimize NAMs as new types of antidepressants.

Keywords: mGluR5; NAMs; depression; 3D-QSAR; molecular docking; molecular dynamics; “arc” configuration

1. Introduction Depression has a clinical manifestation of low mood, anhedonia and low energy levels, cognitive impairment, sleep disorders, sexual dysfunction, and gastrointestinal diseases, amongst other symptoms [1]. The combination of the primary and the secondary disabilities of chronic medical disease caused by depression makes it one of the most costly medical burdens in the world [2]. Actually, WHO has predicted that depression will become the second leading cause of disability worldwide by the year 2030 [3]. Therefore, the study of depression has received widespread attention from the whole world. Depression involves the influence of psychological, genetic, social environment, culture, and physiology [4]. Its etiology and pathogenesis are still unclear, and various mechanisms are still at the stage of hypothesis. Among them, the glutamate contained in the amino acid neurotransmitter

Molecules 2020, 25, 406; doi:10.3390/molecules25020406 www.mdpi.com/journal/molecules Molecules 2020, 25, 406 2 of 22 hypothesis is considered to be related to the pathophysiological factors of depression [5]. Glutamate is the major excitatory neurotransmitter in the mammalian central nervous system (CNS) [6]. It is not only widely distributed in the CNS but also highly concentrated in the cerebral cortex, hippocampus, and striatum, in which brain regions are all closely associated with mental activity [7]. Glutamate mediates its effects through interaction with ionic and metabotropic glutamate receptors. As a matter of fact, metabotropic glutamate receptors (mGluRs) are a family of 8 subtypes (mGluR1–mGluR8) that belong to the family of G-protein coupled receptors (GPCR) [8]. Among all mGlu receptors, abnormal glutamatergic signaling of mGluR5 leads to a series of CNS-related diseases such as depression, schizophrenia, X-brittle syndrome, and Parkinson’s disease [2]. Therefore, mGluR5 becomes an attractive therapeutic target. Structurally, mGlu receptors consist of a large amino-terminal venus fly-trap (VFT) extracellular domain (encompassing the orthosteric site), a seven-transmembrane domain (7TMD), and a cysteine-rich domain (CRD) linking the two [9]. Although several crystal structures of the VFT domain are usable for some mGluR subtypes, including mGluR1, mGluR5, mGluR3, and mGluR7, the orthosteric site has proven difficult to develop efficient subtype-selective ortho-ligands due to its highly conserved characteristic [10]. As a result, efforts have been made to identify allosteric modulators that bind in the less conserved 7TMD [10]. Compounds that bind to an allosteric site and do not itself activate the receptor but enhance the receptor response is called a positive allosteric modulator (PAM). Whereas, the compound that binds to allosteric sites and acts as a non-competitive antagonist is referred to as a negative allosteric modulator (NAM) [11]. As NAMs of mGluR5 are potential drugs for the treatment of many psychiatric disorders [8], including depression, the development of NAMs has become another fascinating area of drug research. Based on the rapid development of molecular biology, chemical informatics, and computer science, drug development has entered a new era, and computer-aided drug design (CADD) came into being. CADD can greatly accelerate the development of new drugs and is more economical. It has, therefore, become more widely used in the pharmaceutical industry. In this study, we employed a set of CADD methods, including the establishment of a three-dimensional quantitative structure-activity relationship (3D-QSAR) model using both the comparative molecular field analysis (CoMFA) [12] and comparative molecular similarity indices analysis (CoMSIA) [13] methods for the study of a series of newly synthesized [14–16] aryl benzamide NAMs of mGluR5. In addition, we also used molecular docking methods to verify the accuracy and stability of the 3D-QSAR model and then used molecular dynamics (MD) to complement the results of molecular docking. These two methods can help us better explore the possible binding conformation between antagonists and target proteins, and thus reveal the corresponding interaction mechanism. We hope that the developed 3D-QSAR model will provide theoretical guidance for the design and development of NAMs of mGluR5 as antidepressants.

2. Results

2.1. 3D-QSAR Statistical Analysis To derive a reliable model, three alignment methods are adopted, namely, alignment-I, -II, and -III. The optimal results based on partial least squares (PLS) statistical analysis are shown in Table1. In general, Q2 > 0.50 [17] is one of the best parameters to evaluate whether a good 3D-QSAR model is acceptable. According to statistical analysis results of PLS, in all CoMFA and CoMSIA models, both alignment-II and -III methods lead to statistically unacceptable models with Q2 < 0.5, but alignment-I obtains a more significant statistical value (Q2 = 0.47 and 0.70 for the optimal CoMFA and CoMSIA models, respectively). Therefore, only the optimal 3D-QSAR models obtained based on the alignment-I approach are analyzed further presently. As shown in Table1, the optimal CoMFA model established by using the steric and electrostatic field descriptors has a Q2 value of 0.47, which does not meet the requirements, indicating relatively poor internal predictability. However, the optimal CoMSIA model that was constructed from space, electrostatic, hydrophobic, hydrogen bonding (H-bond) Molecules 2020, 25, 406 3 of 22

2 2 donor, and H-bond acceptor descriptors has a Q value of 0.70, a high Rncv value of 0.89, a high F value of 120.28, and a low SEE value of 0.22 with 10 OPN, proving its good internal predictability. The relative contributions of steric, electrostatic, hydrophobic, H-bond donor, and H-bond acceptor fields are 14.0%, 29.1%, 34.5%, 2.5%, and 19.9%, respectively. Among them, the contribution of the hydrophobic field ranks first, indicating that the hydrophobic interaction plays a vital role in enhancing the inhibitory activity. In addition, a testing set composed of 24 compounds is used in 2 this study to verify the robustness of the models. Generally speaking, the criterion of Rpre > 0.6 and  2 2  2 Rncv Rpre /Rncv 0.1 indicates that the model has a good external prediction ability. While for the − ≤ 2 optimal CoMSIA model, it meets quite well this criterion with a high value of Rpre = 0.87 and small   difference of R2 R2 /R2 (= 0.02), indicating its proper external prediction ability. ncv − pre ncv

Table 1. Summary of optimal three-dimensional quantitative structure-activity relationship (3D-QSAR) results based on three different alignment methods.

Alignment-I Alignment-II Alignment-III PLS Statistics CoMFA CoMSIA CoMFA CoMSIA CoMFA CoMSIA Q2 0.47 0.70 0.03 0.05 0.35 0.23 2 Rncv 0.89 0.89 0.24 0.24 0.82 0.78 2 Rpre 0.66 0.87 0.49 0.50 0.99 0.96 SEE 0.29 0.22 0.69 0.68 0.75 0.39 SEP 0.45 0.39 0.69 0.76 0.77 0.82 F 96.44 120.28 14.76 29.71 9.13 30.55 OPN 8 10 2 1 7 7 Field Contribution/% Steric 43.5 14.0 47.0 9.0 22.2 10.3 Electrostatic 56.5 29.1 53.0 36.7 77.8 31.7 Hydrophobic - 34.5 36.9 31.2 H-bond donor - 2.5 1.3 2.1 H-bond acceptor - 19.9 16.1 24.7 Q2: cross-validated correlation coefficient, SEP: standard error of prediction, SEE: standard error of estimate, 2 2 OPN: optimum number of components, Rncv: non-cross-validated correlation coefficient, Rpre: predicted correlation   coefficient, F = R2 / 1 R2 . ncv − ncv In summary, in alignment-I, the optimal CoMSIA model exhibits good internal and external prediction abilities. Its correlation with the entire dataset is given in the way of a scatterplot, as shown in Figure1. It can be seen that all data points are evenly distributed around the regression line, indicating a goodMolecules correlation 2020, 25, x FOR PEER between REVIEW predicted and experimental biological activity4 data of 23 (pIC50).

pIC Figure 1. ScatterplotFigure 1. Scatterplot of predicted of predicted and experimental and experimentalpIC valuesvalues for the for traini theng training (brown diamonds) (brown diamonds) and testing (blue squares) compounds from the optimal50 comparative molecular similarity indices and testing (blueanalysis squares) (CoMSIA) compounds model. The solid from line is the the optimalregression comparativeline for the predicted molecular and experimental similarity indices pIC values among the training and testing sets. analysis (CoMSIA) model. The solid line is the regression line for the predicted and experimental pIC50 values among the training and testing sets. 2.2. CoMSIA Graphical Interpretation In this section, the optimal CoMSIA model is selected to construct the StDev*-Coeff contour maps for analysis of the field effects of the ligand molecules. To better illustrate and analyze the results of the Stdev*-Coeff contour maps, the most active molecule 74 from the entire dataset is superimposed with five fields (steric field, electrostatic field, hydrophobic field, H-bond donor field, H-bond acceptor field), as shown in Figure 2.

Molecules 2020, 25, x FOR PEER REVIEW 4 of 23

Figure 1. Scatterplot of predicted and experimental pIC values for the training (brown diamonds) and testing (blue squares) compounds from the optimal comparative molecular similarity indices Moleculesanalysis2020, 25(CoMSIA), 406 model. The solid line is the regression line for the predicted and experimental4 of 22 pIC values among the training and testing sets.

2.2. CoMSIA CoMSIA Graphical Graphical Interpretation Interpretation In thisthis section,section, the the optimal optimal CoMSIA CoMSIA model model is selectedis selected to construct to construct the StDev*-Coethe StDev*-Coeffff contour contour maps mapsfor analysis for analysis of the fieldof the eff fieldects ofeffects the ligand of the molecules. ligand molecules. To better To illustrate better illustrate and analyze and the analyze results the of resultsthe Stdev*-Coe of the Stdev*-Coeffff contour maps, contou ther most maps, active the moleculemost active74 frommolecule the entire 74 from dataset the isentire superimposed dataset is superimposedwith five fields with (steric five field, fields electrostatic (steric field, field, electros hydrophobictatic field, field, hydrophobic H-bond donor field, field, H-bond H-bond donor acceptor field, H-bondfield), as acceptor shown in field), Figure as2 shown. in Figure 2.

Figure 2. CoMSIA StDev*-Coeff contour map. (A) Steric contour map: the green contours represent regions where bulky groups increase the activity, the yellow contours represent regions where bulky groups decrease the activity; (B) electrostatic contour map: the blue contours indicate regions where electropositive features promote the activity, the red contours indicate regions where electronegative features promote the activity; (C) hydrophobic contour map: the white contours indicate regions where hydrophobic groups are detrimental for the activity, the yellow contours indicate regions where hydrophobic groups are beneficial for the activity; (D) H-bond donor contour map: the cyan contours represent regions where H-bond donors promote the activity, the purple contours represent regions where H-bond donors demote the activity; (E) H-bond acceptor contour map: the purple contours represent the regions where H-bond acceptors are beneficial for the activity, while the red contours represent the regions where H-bond acceptors are detrimental to the activity.

Figure2A shows the e ffect of the sterically hindered field on the molecular activity, where the green cloud indicates that the introduction of large substituents in this region will lead to the increase of the activity of the antagonist, which can be verified from the sample dataset in Table S1 of Supplementary material. For example, compounds 6 (pIC50 = 7.57) and 8 (pIC50 = 6.49) have similar structures. However, compound 6 has a greater steric hindrance than compound 8 on the substituent at position 20 of ring A, resulting in an increase in antagonistic activity. Further, the brownish-yellow cloud (20% contribution) indicates that the introduction of a sterically hindered group (i.e., a large substituent) in this region will decrease the antagonistic activity. For example, there is also a yellow cloud around ring C, which also shows that the addition of a substituent with a large steric hindrance will be detrimental to the antagonistic activity. As for molecules 41 (pIC50 = 6.67) and 42 (pIC50 = 5.98) as compound 42 has an extra fluorine group at position 4 of ring C than compound 41, the molecular activity is lowered. Molecules 2020, 25, x FOR PEER REVIEW 5 of 23

Figure 2. CoMSIA StDev*-Coeff contour map. (A) Steric contour map: the green contours represent regions where bulky groups increase the activity, the yellow contours represent regions where bulky groups decrease the activity; (B) electrostatic contour map: the blue contours indicate regions where electropositive features promote the activity, the red contours indicate regions where electronegative features promote the activity; (C) hydrophobic contour map: the white contours indicate regions where hydrophobic groups are detrimental for the activity, the yellow contours indicate regions where hydrophobic groups are beneficial for the activity; (D) H-bond donor contour map: the cyan contours represent regions where H-bond donors promote the activity, the purple contours represent regions where H-bond donors demote the activity; (E) H-bond acceptor contour map: the purple contours represent the regions where H-bond acceptors are beneficial for the activity, while the red contours represent the regions where H-bond acceptors are detrimental to the activity.

Figure 2A shows the effect of the sterically hindered field on the molecular activity, where the green cloud indicates that the introduction of large substituents in this region will lead to the increase of the activity of the antagonist, which can be verified from the sample dataset in Appendix A of

Supplementary material. For example, compounds 6 (pIC =7.57) and 8 (pIC =6.49) have similar structures. However, compound 6 has a greater steric hindrance than compound 8 on the substituent at position 20 of ring A, resulting in an increase in antagonistic activity. Further, the brownish-yellow cloud (20% contribution) indicates that the introduction of a sterically hindered group (i.e., a large substituent) in this region will decrease the antagonistic activity. For example, there is also a yellow cloud around ring C, which also shows that the addition of a substituent with a large steric hindrance pIC =6.67 pIC = will be detrimentalMolecules to2020 the, 25antago, 406 nistic activity. As for molecules 41 ( ) and 42 ( 5 of 22 5.98) as compound 42 has an extra fluorine group at position 4 of ring C than compound 41, the molecular activity is lowered. Figure 2B depictsFigure the2 Binfluence depicts theof the influence electrosta oftic the substituent electrostatic on substituent the potency on of the the potency molecules, of the molecules, where a positivewhere charge a positive group chargeat the blue group cloud at the (80% blue contribution) cloud (80% contribution)position or a negative position orcharge a negative charge group at the redgroup cloud at (20% the red contribu cloudtion) (20% position contribution) will facilitate position the will activity. facilitate As the seen activity. from the As figure, seen from the figure, the blue profilethe at blue the profileR2 substituent at the R2 of substituent ring C indi ofcates ring Cthat indicates a positively that a charged positively group charged at that group site at that site shall shall facilitate facilitatethe activity. the activity.This is consistent This is consistent with the with experimental the experimental results results for a compound for a compound activity activity sequence sequence of 35of (having35 (having fewer fewer fluorine fluorine atoms atoms on onthe the R2R2 substituent) substituent) > 36>.36 Whereas,. Whereas, a red a red profile profile is is observed at observed at positionspositions 4 4and and 5 5of of ring ring C, C, which which indicates indicates the the favor of negatively chargedcharged groupsgroups forfor improving the improving theactivity, activity, for forexample, example, compoundcompound 40 with a Cl Cl atom atom at at position position 5 5 (pIC (pIC50=7.38= 7.38) is) ismore more potent than potent than compoundcompound 4141 (pIC(pIC50=6.67= 6.67) with) with a amethyl methyl substituent substituent in in the the same same region. region. This This can can be be explained by explained by thethe existence existence of of an an electron-accept electron-acceptinging F F atom atom in in the the substituent substituent at at 4-position. 4-position. Regarding theRegarding equipotential the map equipotential of the hydropho map ofbic the field hydrophobic (as shown field in Figure (as shown 2C), inthe Figure white2 C), the white contours indicatecontours the position indicate thewhere position the wherehydrop thehilic hydrophilic group is groupfavored, is favored, and the and yellow the yellow outline outline indicates indicates the regionthe region of the of preferred the preferred hydrophobic hydrophobic group. group. From FromFigure Figure 2C, we2C, find we that find there that is there a large is a large yellow yellow cloud aroundcloud around ring A, ringwhich A, means which that means the hy thatdrophobic the hydrophobic group in the group vicinity in the is vicinityadvantageous is advantageous for for antagonisticantagonistic activity. activity.This may This explain may explain why whycompound compound 29 (29pIC(pIC =6.6250 = 6.62) )having having more more hydrophobic N hydrophobic groups ( O N ) atat thesethese regionsregions exhibitsexhibits higherhigher mGluR5mGluR5 antagonistantagonist activityactivity thanthan compound 94 compound 94 ((pICpIC50 ==6.056.05).). In addition, a largelarge graygray cloudcloud atat ringring BB revealsreveals thatthat thethehydrophobic hydrophobic groups herein groups hereinreduce reduce thethe activityactivity of the molecule. Th Thisis is is well well illustrated illustrated by by the the higher higher activities activities of of molecule 9 molecule 9 (pIC(pIC =7.6950 = 7.69) (without) (without any any substituent) substituent) than than molecule molecule 1010 (pIC(pIC50=5.85= 5.85) with) with a amethyl methyl group at the group at the samesame position. position. Figure 2D is anFigure equipotential2D is an equipotential map of the hydrogen map of the bond hydrogen donor bond field donorfor compound field for compound 74, where 74, where the the cyan cloudcyan represents cloud representsthe desired the while desired the magenta while the cloud magenta represents cloud representsthe undesired the undesiredregions of regions of the the H-bond donor,H-bond respectively. donor, respectively. A cyan cloud A cyan is cloud observed is observed covering covering the NH the of NH the of amide the amide group, group, indicating indicating thatthat NH NH plays plays a significant a significant role role in indonating donating H Hnear near ring ring C Cto to form form a aH-bond H-bond interaction interaction with NAMs. with NAMs. InIn addition, addition, a a large large cyan cyan cloud cloud near near ring ring C C indicates indicates that that the the presence presence of of a ahydrogen hydrogen bond donor bond donor substituentsubstituent will will increase increase the the antagonistic antagonistic activity. activity. This This fits fits well well with with the the experimental experimental results that results that compoundcompound 4242 (pIC(pIC 50=5.98= 5.98) with) with a a-CF -CF3 group3 group at at ring ring C C is is less less active active than than compound 39 (pIC50 = 7.02) without any group at the same position. Furthermore, almost no purple cloud is found in Figure2D, which means that unfavorable hydrogen bond donor interactions can be negligible. Figure2E is the equipotential map of the hydrogen bond acceptor field. The favorable and unfavorable regions of the H-bond acceptor substituent are respectively represented by the purple (80% contribution) and red (20% contribution) clouds. As seen from the figure, a purple cloud is observed below ring C, which is consistent with the N atom at the 1-position as the H-bond acceptor. In addition, a red cloud is observed near ring B suggesting that the introduction of an H-bond acceptor group in this area would adversely affect the molecule, which is in agreement with the fact that compound 23 is less potent than compound 25. The reason is that ring B of compound 23 has an F atom as a substituent, while the ring B of compound 25 has a methyl group as a substituent, respectively. Based on the above results of the 3D-QSAR study, we are able to determine several of the structural requirements described above for observed inhibitory activity (as shown in Figure3): (1) For ring A and the ether bond, the large sterically hindered group and the hydrophobic group contribute to the improvement of molecular activity. (2) For ring B, away from the R1 substituent, the negatively charged group facilitates the increase in molecular activity. In addition, ring B is close to the R1 substituent, the hydrophilic group is beneficial to the improvement of molecular activity. (3) For the portion of ring A away from the R2 substituent, and around the N atom of the molecular skeleton, the hydrogen bond donor group facilitates the improvement of molecular activity. (4) For ring A near the R2 substituent, the portion adjacent to the molecular skeleton, the hydrogen bond acceptor group facilitates the improvement of molecular activity. Molecules 2020, 25, x FOR PEER REVIEW 6 of 23

(pIC =7.02) without any group at the same position. Furthermore, almost no purple cloud is found in Figure 2D, which means that unfavorable hydrogen bond donor interactions can be negligible. Figure 2E is the equipotential map of the hydrogen bond acceptor field. The favorable and unfavorable regions of the H-bond acceptor substituent are respectively represented by the purple Molecules(80% contribution)2020, 25, 406 and red (20% contribution) clouds. As seen from the figure, a purple cloud6 of 22is observed below ring C, which is consistent with the N atom at the 1-position as the H-bond acceptor. In addition, a red cloud is observed near ring B suggesting that the introduction of an H-bond (5) For ring A, away from the R2 substituent and the molecular skeleton, the negatively charged acceptor group in this area would adversely affect the molecule, which is in agreement with the fact group and the hydrophilic group contribute to the improvement of the molecular activity. In addition, that compound 23 is less potent than compound 25. The reason is that ring B of compound 23 has an ring A is close to the R2 substituent, the positively charged group is advantageous for the improvement F atom as a substituent, while the ring B of compound 25 has a methyl group as a substituent, of molecular activity away from the molecular skeleton. respectively.

FigureFigure 3.3. KeyKey structuralstructural featuresfeatures ofof arylaryl benzamide-basedbenzamide-based mGluR5mGluR5 negativenegative allostericallosteric modulatorsmodulators (NAMs)(NAMs) moleculesmolecules thatthat impact impact their their inhibitory inhibitory e effects.ffects.

2.3. MolecularBased on Docking the above Studies results of the 3D-QSAR study, we are able to determine several of the structuralMolecular requirements docking isdescribed based on above the structure for observed of receptor inhibitory proteins activity and (as ligand shown small in Figure molecules, 3): in which(1) chemical For ring statistics A and the are ether used bond, to simulate the large the ster three-dimensionalically hindered group structure and the of compounds hydrophobic and group the interactioncontribute betweento the improvement molecules, andof molecular in this way, activity. the binding mode between receptors and ligands may be identified.(2) For ring Presently, B, away afrom computational the R1 substituent, docking th studye negatively was performed, charged group and thefacilitates resultant the bindingincrease patternin molecular using theactivity. most activeIn addition, molecule ring74 Bdocked is clos intoe to thethe humanR1 substituent, mGluR5 receptorthe hydrophilic as an example group isis shownbeneficial in Figure to the4 improvement. of molecular activity. As(3) shownFor the in portion Figure 4ofA, ring the sequenceA away from of mGluR5 the R2 receptor substituent, consists and essentially around the of N 7 (TM1-TM7)atom of the transmembranemolecular skeleton, helices, the in hydrogen which the bond binding donor site ofgr theoup ligand facilitates is embedded the improvement in the middle of portionmolecular of theactivity. helical domain. In fact, the binding cavity is an almost closed space resembling a “bone” shape, which(4) is mainlyFor ring composed A near the of two R2 sub-pocketssubstituent, (representedthe portion byadjacent S1 and to S2 the in the molecular present work)skeleton, and the an intermediatehydrogen bond connecting acceptor region group (Linker, facilitates Figure the 4improvementB). Wherein ring of molecular C is located activity. in sub-pocket S1, rings A and B(5) are For co-located ring A, away in another from the sub-pocket R2 substituent S2, and an thed the molecular molecular skeleton skeleton, peptide the negatively bond is located charged in thegroup linker and of thethe “bone”hydrophilic site. It cangroup be clearlycontribute observed to the that improvement the area of the of cavity the wheremolecular ring Cactivity. is located In isaddition, relatively ring narrow A is andclose thus to the tightly R2 substituent, confines this th ringe positively in the pocket, charged which group is consistent is advantageous with the resultsfor the obtainedimprovement from theof molecular previous QSAR activity equipotential away from mapthe molecular (Figure2A), skeleton. where a large brownish-yellow cloud is observed around ring C, indicating that the large sterically hindered group is not conducive to the 2.3. Molecular Docking Studies increase in activity. Comparatively, the space of sub-pocket S2 where rings A, B, and the ether bond are locatedMolecular is much wider,docking which is based is also on consistent the structure with of the receptor green contourproteins present and ligand around small rings molecules, A and B inin Figurewhich2 chemicalA. Therefore, statistics both are the used 3D-QSAR to simulate and docking the three-dimensional results indicate that structure the large of compounds sterically hindered and the groupsinteraction within between the space molecules, of sub-pocket and in S2 this where way, rings the A,binding B, and mode the ether between bond arereceptors located and contribute ligands to the increase of molecular activity, whereas bulky substituents around ring C disfavor the activity. Molecules 2020, 25, x FOR PEER REVIEW 7 of 23 may be identified. Presently, a computational docking study was performed, and the resultant binding pattern using the most active molecule 74 docked into the human mGluR5 receptor as an example is shown in Figure 4. As shown in Figure 4A, the sequence of mGluR5 receptor consists essentially of 7 (TM1-TM7) transmembrane helices, in which the binding site of the ligand is embedded in the middle portion of the helical domain. In fact, the binding cavity is an almost closed space resembling a “bone” shape, which is mainly composed of two sub-pockets (represented by S1 and S2 in the present work) and an intermediate connecting region (Linker, Figure 4B). Wherein ring C is located in sub-pocket S1, rings A and B are co-located in another sub-pocket S2, and the molecular skeleton peptide bond is located in the linker of the “bone” site. It can be clearly observed that the area of the cavity where ring C is located is relatively narrow and thus tightly confines this ring in the pocket, which is consistent with the results obtained from the previous QSAR equipotential map (Figure 2A), where a large brownish- yellow cloud is observed around ring C, indicating that the large sterically hindered group is not conducive to the increase in activity. Comparatively, the space of sub-pocket S2 where rings A, B, and the ether bond are located is much wider, which is also consistent with the green contour present around rings A and B in Figure 2A. Therefore, both the 3D-QSAR and docking results indicate that the large sterically hindered groups within the space of sub-pocket S2 where rings A, B, and the ether Moleculesbond are2020 located, 25, 406 contribute to the increase of molecular activity, whereas bulky substituents around7 of 22 ring C disfavor the activity.

Figure 4. ((AA)) Binding Binding pattern pattern of of compound compound 74 withwith mGluR5; mGluR5; ( B)) and and ( (CC)) depict depict surface surface structures structures and perspective views of the cavity of the bindingbinding sites.sites.

To revealreveal thethe bindingbinding conformation of the ligandligand and the corresponding mechanism of action, we examined the docked complex ofof the mGluR5 receptor inin detail.detail. As shown in Figure 5 5,, thethe bindingbinding cavity isis mainlymainly composed composed of of 20 20 amino amino acid acid residues residues (within (within the 4.5the Å 4.5 range Å range of the ligand),of the ligand), i.e., Pro655, i.e., Tyr659,Pro655, Ile625,Tyr659, Ile651, Ile625, Ile944, Ile651, Ser658, Ile944, Ser654,Ser658, Ser969,Ser654, Ser965,Ser969, Ala970,Ser965, Ala970, Ala973, Ala973, Trp945, Trp945, Phe948, Phe948, Val900, Pro903,Val900, Pro903, Asn907, Asn907, Leu904, Leu904, Gln864, Gln864, Met962, Met962, and Val966. and Val966. In this bindingIn this binding mode, threemode, significant three significant factors arefactors observed, are observed, i.e., hydrophobicity, i.e., hydrophobicity, H-bond, H-bond, and π– andπ stacking π–π stacking interactions, interactions, which which contribute contribute to the intimateto the intimate interaction interaction of the NAMs of the with NAMs the targetwith receptor.the target Among receptor. the Among 20 amino the acids, 20 amino hydrophobic acids, residueshydrophobic account residues for about account 65%, whichfor about greatly 65%, facilitates which greatly the induction facilitates of the the active induction conformation of the active of the antagonist.conformation In fact,of the sub-pocket antagonist. S2 whereIn fact, rings sub-po A andcket B areS2 locatedwhere isrings a hydrophobic A and B pocketare located consisting is a ofhydrophobic 12 key amino pocket acid consisting residues, of 9 12 of key which amino are hydrophobicacid residues, ones9 of which such asare Trp945, hydrophobic Leu904, ones Phe948, such andas Trp945, Ile651. Leu904, The proportion Phe948, ofand hydrophobic Ile651. The aminoproporti acidon residuesof hydrophobic in the sub-pocket amino acid S1 residues is 50%. Thesein the resultssub-pocket are alsoS1 is essentially 50%. These in results agreement are also with esse thentially hydrophobic in agreement contour with distribution the hydrophobic of the CoMSIA contour model (Figure2C). That is, as shown in this figure, the existence of hydrophobic groups around ring A contributes to the improvement of molecular activity. For example, compounds 74 and 59 are similar in structure. The reason why their inhibitory activities both rank among the top five (compound 74, pIC50 = 8.13, compound 59, pIC50 = 7.86) depends largely on the presence of a hydrophobic group at ring A. All of these findings indicate that hydrophobicity is the most critical contribution to enhancing the inhibitory activity. Besides the hydrophobic interactions, the H-bond and π–π stacking interactions also play key roles in the binding of the aryl benzamide derivatives with the receptor. As a matter of fact, as shown in Table1, the total contribution of hydrogen-bond descriptors of the optimal CoMSIA model reaches 22.4%, clearly underlining the important role of this weak binding force to the potency of mGluR5 NAMs. In line with this, in present docking results (Figure5), five H-bonds are formed so as to consolidate the molecular conformation. The N atom on ring C forms a H-bond with the side chain of Ser969 (-N HO-, 2.85 Å), and the linked ether group -O- form another H-bond with the side chain of ··· Met962 (-O HS-, 3.23 Å). Further, the O atom on peptide bond forms H-bonds with the side chain ··· of Ser969 (-O -OH, 2.10 Å) and Tyr945 (-O -OH, 3.14 Å), respectively. The F atom on ring A also ··· ··· form an H-bond with the side chain of Asn907 (-F -NH, 2.50 Å). This is in good agreement with the ··· presence of large cyan and small red contours above the link chain in Figure2D, E). For example, Molecules 2020, 25, 406 8 of 22 among these five H-bonds, the F atom on ring A and the O atom on the ether bond as the H-bond acceptor substituents are favorable for the inhibitory activity. Furthermore, ring B forms a face-to-face π–π stacking interaction with amino acid residue Trp945, which also helps immobilize the antagonist within the protein cavity. In short, hydrogen bonding and π–π stacking interactions are essential for NAM to exert inhibitory activity. When anchored in the pocket, molecule 74 is inserted into the transmembrane helices in an “arc”-likeMolecules 2020 conformation, 25, x FOR PEER (Figure REVIEW5 ), wherein ring C and the amide group form a branch, ring A8 andof 23 the ether group constitute another branch, and ring B constitutes the apex of the “arc,” respectively. distribution of the CoMSIA model (Figure 2C). That is, as shown in this figure, the existence of The vertex of the “arc” and the branch where ring A is located extend to the hydrophobic pocket, hydrophobic groups around ring A contributes to the improvement of molecular activity. For with the branch’s tip fixed by an H-bond formed with residue Asn907. The benzene ring at the apex example, compounds 74 and 59 are similar in structure. The reason why their inhibitory activities then further anchors the “arc” by π–π stacking face to face with the residue Trp945. As to the other both rank among the top five (compound 74, pIC50 = 8.13, compound 59, pIC50 = 7.86) depends largely branch that is composed of ring C and the molecular skeleton peptide bond, it is anchored by an on the presence of a hydrophobic group at ring A. All of these findings indicate that hydrophobicity H-bonding interaction with residue Ser969, Tyr659. is the most critical contribution to enhancing the inhibitory activity.

FigureFigure 5. 5. PutativePutative interaction interaction between between compound compound 7474 andand mGluR5. mGluR5. The The amino amino ac acidid residues residues are are shown shown byby the the stick stick model, model, in in which which the the molecular molecular carbon carbon and and the the amino amino acid acid residu residuee carbon carbon are are represented represented byby pink pink and and green, green, respecti respectively;vely; the the N, N, O, O,S, F S, atoms F atoms are arerepres representedented by navy by navy blue, blue, red, orange, red, orange, and yellow,and yellow, respectively; respectively; the theH-bond H-bond force force and and the the π–π– stackπ stack are are represented represented by by blue blue and and purple purple dotted dotted lines,lines, respectively. respectively.

BesidesIn summary, the hydrophobic the above hydrophobic interactions, interaction, the H-bond H-bond and π and–π stackingπ–π stacking interactions interaction also play play a key key rolesrole inin stabilizingthe binding the of optimalthe aryl benzamide binding conformation derivatives ofwith ligand the receptor. to mGluR5. As a In matter addition, of fact, the as results shown of indocking Table 1, and the 3D-QSAR total contribution models complement of hydrogen-bon and validated descriptors each other,of the providingoptimal CoMSIA useful informationmodel reaches for 22.4%,the rational clearly design underlining of more the effective important mGluR5 role antagonistsof this weak in binding the future. force to the potency of mGluR5 NAMs. In line with this, in present docking results (Figure 5), five H-bonds are formed so as to consolidate2.4. Molecular the Dynamics molecular Studies conformation. The N atom on ring C forms a H-bond with the side chain of Ser969MD simulation(-N···HO-, 2.85 is proof Å), and and the supplements linked ether the group 3D-QSAR -O- form method another and H-bond molecular with docking the side analysis, chain ofwhich Met962 belongs (-O···HS-, to the 3.23 dynamic Å). Further, simulation the O process.atom on Itpeptide can not bond only forms effectively H-bonds track with and the represent side chain the ofdynamic Ser969 behavior,(-O···-OH, but 2.10 also Å) visually and Tyr945 displays (-O···-OH, the static 3.14 graphics Å), respectively. in the system. The In F thisatom work, on ring a 20,000 A also ps formMD simulationan H-bond waswith performed the side chain using of theAsn907 docking (-F···-NH, complex 2.50 ofÅ). the This mGluR5 is in good receptor agreement as the with starting the presencemolecular of structure large cyan to and explore small dynamic red contours images ab ofove the the conformational link chain in Figure diversity 2D, of E). ligand-receptor For example, amongbinding. these Additionally, five H-bonds, to reveal the F the atom “positional on ring stability”A and the of O the atom initial on complexthe ether conformation, bond as the H-bond the root acceptormean square substituents deviation are (RMSD) favorabl ofe the for skeletal the inhibitory atoms ofactivity. the trajectory Furthermore, was calculated, ring B forms which a endsface-to- up facewith π a– rangeπ stacking of 0.25 interaction to 0.40 Å relative with amino to the initialacid residue structure Trp945, of the compositewhich also as helps depicted immobilize in Figure 6theA. antagonist within the protein cavity. In short, hydrogen bonding and π–π stacking interactions are essential for NAM to exert inhibitory activity. When anchored in the pocket, molecule 74 is inserted into the transmembrane helices in an “arc”-like conformation (Figure 5), wherein ring C and the amide group form a branch, ring A and the ether group constitute another branch, and ring B constitutes the apex of the “arc,” respectively. The vertex of the “arc” and the branch where ring A is located extend to the hydrophobic pocket, with the branch’s tip fixed by an H-bond formed with residue Asn907. The benzene ring at the apex then further anchors the “arc” by π–π stacking face to face with the residue Trp945. As to the other branch that is composed of ring C and the molecular skeleton peptide bond, it is anchored by an H- bonding interaction with residue Ser969, Tyr659. Molecules 2020, 25, x FOR PEER REVIEW 9 of 23

In summary, the above hydrophobic interaction, H-bond and π–π stacking interaction play a key role in stabilizing the optimal binding conformation of ligand to mGluR5. In addition, the results of docking and 3D-QSAR models complement and validate each other, providing useful information for the rational design of more effective mGluR5 antagonists in the future.

2.4. Molecular Dynamics Studies MD simulation is proof and supplements the 3D-QSAR method and molecular docking analysis, which belongs to the dynamic simulation process. It can not only effectively track and represent the dynamic behavior, but also visually displays the static graphics in the system. In this work, a 20,000 ps MD simulation was performed using the docking complex of the mGluR5 receptor as the starting molecular structure to explore dynamic images of the conformational diversity of ligand-receptor Moleculesbinding.2020 Additionally,, 25, 406 to reveal the “positional stability” of the initial complex conformation, the9 ofroot 22 mean square deviation (RMSD) of the skeletal atoms of the trajectory was calculated, which ends up with a range of 0.25 to 0.40 Å relative to the initial structure of the composite as depicted in Figure As6A. shown As shown in this in this figure, figure, the the RMSD RMSD of theof the system system remains remains around around 0.35 0.35 Å Å after after the the initial initial 10,00010,000 psps freefree balancebalance andand isis relativelyrelatively stablestable throughoutthroughout thethe subsequentsubsequent simulations,simulations, indicatingindicating aa balancedbalanced conformationconformation ofof thethe complexcomplex structurestructure ofof thethe docking.docking. InIn addition,addition, FigureFigure6 6CC shows shows the the structure structure of of thethe protein-ligandprotein-ligand complex complex in thein the MD MD simulation simulation (blue) (blue) superimposed superimposed by the initialby the docking initial structuredocking (green)structure in the(green) last 1in ns. the As last shown 1 ns. in FigureAs shown6C, the in structureFigure 6C, extracted the structure from the extracted MD simulation from the agrees MD wellsimulation with the agrees docking well model with the of the docking complex, model and of both the ofcomplex, them adopt and both have of an them “arc” adopt conformation. have an “arc” This alsoconformation. proves the This rationality also proves of our the previous rationality docking of our model. previous docking model.

FigureFigure 6.6. ((A)) TimeTime evolutionevolution ofof thethe rootroot meanmean squaresquare deviationdeviation (RMSD)(RMSD) ofof thethe skeletalskeletal atomatom fromfrom thethe initialinitial dockingdocking structurestructure during during the the 20,000 20,000 ps ps molecular molecular dynamic dynamic (MD) (MD) simulation; simulation; (B )(B the) the structure structure of theof the receptor receptor with with the dockedthe docked ligand ligand within within the lipid the bilayerlipid bilayer at the lastat the 1 ns last of MD1 ns stimulation.of MD stimulation. Protein, ligand,Protein, and ligand, lipid moleculesand lipid aremolecules shown asare ribbons, shown lines, as ribbons, and spheres, lines, respectively; and spheres, (C )respectively; superposition (C of) thesuperposition MD structure of the at the MD last structure 1 ns (blue) at the and last initial 1 ns (blue) structure and of initial the docked structure compound of the docked74 (green). compound 74 (green). Figure7 depicts all the binding interactions observed in the last 10 ns MD simulation. As seen fromFigure this figure, 7 depicts the binding all the modebinding of compoundinteractions74 obseexhibitingrved in inthe the last MD 10 modelns MD is simulation. almost the sameAs seen as thefrom docking this figure, results the in binding terms of mode key amino of compound acids and 74π exhibiting–π stacking in interactions. the MD model Specifically, is almost the the major same aminoas the acidsdocking in the results docking in terms model of constituting key amino the acids binding and cavityπ–π stacking are all observed interactions. in the Specifically, MD simulation, the includingmajor amino the acids hydrophobic in the docking amino acidmodel residues constituting Ile625, the Ile651, binding Ile944, cavity Ala973, are Ala970,all observed Pro655, in the Pro903, MD Val966,simulation, Phe948, including Leu904, the Trp945, hydrophobic and Met962, amino and acid hydrophilic residues Ile625, amino Ile651, acid residues Ile944, Ala973, Ser658, Ala970, Ser654, Tyr659,Pro655, Ser969,Pro903, Ser965, Val966, and Phe948, Asn907. Leu904, Furthermore, Trp945, basicand Met962, face-to-face and πhydrophilic–π stacking amino between acid ring residues B and Trp945Ser658, isSer654, also retained Tyr659, in Ser969, the MD Ser965, results. and Asn907. Furthermore, basic face-to-face π–π stacking betweenIn addition, ring B and because Trp945 multiple is also retained H-bonds in are the observed MD results. in the results of MD simulations, to more precisely quantify H-bonds, the occurrence frequency of H-bonds in the last 10 ns is summarized in Table2. In the MD simulation results, though the composition of H-bonds at di fferent times (ns) changes slightly, as seen from Table2, the H-bond interactions in the MD results are basically consistent with the docking results. Especially, three ammonia acid residues, i.e., Tyr659, Ser969, and Asn907, all participate in the H-bonding interactions in both the docking and MD results. Among them, Tyr659 appears most frequently in the MD results and participates in the formation of two H-bonds, i.e., the H-bond between the O atom on peptide bond and Tyr659 (-O -OH) and the one between the N ··· atom on ring C and Tyr659 (-O -N), with an occurrence percentage of 90% and 30%, respectively. ··· In these two H-bonds, the former one also appears in the docking results, indicating its essential role in stabilizing the bioactive conformation of the molecule. For Ser969, it also helps the formation of two H-bonds, which include the H-bond with the O atom on the peptide bond (-O -OH) and the one ··· with the N atom on ring C (-O -N)). These two H-bonds are both observed in the docking results, ··· Molecules 2020, 25, x FOR PEER REVIEW 10 of 23

In addition, because multiple H-bonds are observed in the results of MD simulations, to more precisely quantify H-bonds, the occurrence frequency of H-bonds in the last 10 ns is summarized in Table 2. In the MD simulation results, though the composition of H-bonds at different times (ns) changes slightly, as seen from Table 2, the H-bond interactions in the MD results are basically consistent with the docking results. Especially, three ammonia acid residues, i.e., Tyr659, Ser969, and Asn907, all participate in the H-bonding interactions in both the docking and MD results. Among them, Tyr659 appears most frequently in the MD results and participates in the formation of two H- bonds, i.e., the H-bond between the O atom on peptide bond and Tyr659 (-O···-OH) and the one between the N atom on ring C and Tyr659 (-O···-N), with an occurrence percentage of 90% and 30%, respectively. In these two H-bonds, the former one also appears in the docking results, indicating its essentialMolecules 2020 role, 25 in, 406 stabilizing the bioactive conformation of the molecule. For Ser969, it also helps10 ofthe 22 formation of two H-bonds, which include the H-bond with the O atom on the peptide bond (-O···- OH)proving and the the importance one with the of Ser969N atom in on the ring H-bonding C (-O···-N)). network These of two mGluR5-aryl H-bonds benzamideare both observed NAMs. in As the for dockingAsn907, itresults, interacts proving with the the F atomimportance on ring of A andSer969 in thisin waythe H-bonding forms a H-bond network (-F -N),of mGluR5-aryl which helps ··· benzamidering A deviate NAMs. from As the for original Asn907, linear it interacts conformation with the and F deflectatom on towards ring A Asn907,and in this thereby way formingforms a H- an bondarc conformation. (-F···-N), which It ishelps noteworthy ring A deviate that a H-bondfrom the between original thelinear O atomconforma on peptidetion and bond deflect and towards Trp945 Asn907,(-N -OH) thereby formed forming in MD an simulation arc conformation. is not observed It is noteworthy in the docking that a results, H-bond which between may the be O due atom to theon ··· peptidefact that bond it is the and average Trp945 property (-N···-OH) of theformed ensemble in MD of simulation last 10 ns’ MDis not simulated observed bioactive in the docking conformations results, whichinstead may of the be propertydue to the at afact specific that timeit is the of the aver ligand-mGluR5age property of complex the ensemble that is depictedof last 10 in ns’ Table MD2. simulatedDue to the bioactive relatively conformations high occurrence instead percentage of the (50%) property of this at H-bond,a specific Trp945 time of may the be ligand-mGluR5 also crucial for complexstabilizing that the is active depicted conformation in Table 2. ofDue the to complex. the relatively high occurrence percentage (50%) of this H- bond,All Trp945 the abovemay be may also explain crucial for why stabilizing the MD the simulated active conformation structure of theof the protein-ligand complex. complex overlapsAll the well above with itsmay initial explain docked why structure, the MD with simula no majorted structure difference of isthe observed. protein-ligand In summary, complex the overlapsconformation well with of the its ligand initial remainsdocked structure, stable at thewith active no major site ofdifference the receptor, is observed. and it still In summary, exhibits “arc” the conformationactive conformation of the ligand under theremains action stable of hydrophobicity, at the active site H-bond, of the andreceptor,π–π stacking and it still interaction. exhibits “arc” active conformation under the action of hydrophobicity, H-bond, and π–π stacking interaction.

Figure 7. AllAll the the binding binding interactions interactions observed observed in in the la lastst 10 ns MD simulation simulation.. Amino Amino acid acid residues residues inin the active sitesite areare representedrepresented as as sticks; sticks; the the molecular molecular carbon carbon and and the the amino amino acid acid residue residue carbon carbon are arerepresented represented by greenby green and and yellow, yellow, respectively. respectively. The The N, O, N, S, O, F, S, and F, Cland atoms Cl atoms are represented are represented by dark by darkblue, red,blue, orange, red, orange, cyan, and cyan, dark and green, dark respectively. green, respectively. The H-bond The and H-bond the π–π stackingand the interactionsπ–π stacking are interactionsrepresented are as blue represented and purple as blue dashed and lines, purple respectively. dashed lines, respectively.

Table 2. The H-bond interactions in MD simulation results within the last 10 ns.

H-Bond Amino Acid Occurrence Percent H-Bond Interactions and Their Constituents Code Residue of H-Bonds (%) The H-bond between the O atom on the peptide bond and Tyr659 HB1 Tyr659 90 (-O -OH) ··· HB2 Tyr659 30 The H-bond between the N atom on ring C and Tyr659 (-O -N) ··· The H-bond between the O atom on the peptide bond and Ser969 HB3 Ser969 70 (-O -OH) ··· HB4 Ser969 10 The H-bond between the N atom on ring C and Ser969 (-O -N) ··· The H-bond between the O atom on the peptide bond and Trp945 HB5 Trp945 50 (-N -OH) ··· HB6 Asn907 30 The H-bond between the F atom on ring A and Asn907 (-F -N) ··· Molecules 2020, 25, 406 11 of 22

3. Discussion In the early development stage of mGluR5 NAMs, the alkyne subunit in antagonists is regarded as a key structural motif for the molecules to possess a high affinity to mGluR5 [18]. However, an alkyne moiety is metabolically unstable and often cause unfavorable side effects [18]. Therefore, two structural motifs, i.e., amide and urea functional groups, are proposed as the replacements of alkyne linkages in mGluR5 NAMs [18]. So far, altogether eight known mGluR5 NAMs have entered and/or are about to enter clinical trials, i.e., Mavoglurant [19–21], Basimglurant [8,21], MTEP [8], HTL14242 [20], Dipraglurant [21], Fenobam [21,22], Thiazole-2-carboxamides (6bc, 6bj) [18]. It is worth mentioning that, for a long time, the important structural information on mGluR5 NAMs was to combine mutation studies with homology modeling and/or a series of ligand analogs. The reason is that the crystal structure of mGluR5-7TM has not been available. It was not until 2014 that Andrew S. Doré [23] obtained the crystal structure of mGluR5-7TM. As, usually, the results of homology modeling are somewhat or slightly different from the actual structures of the target proteins, to further explore the interaction features of various NAMs with mGluR5 receptor, we have summarized the docking/MD results of the above mentioned known mGluR5 NAMs (with molecular structures shown in Figure8) by using the crystal structure of mGluR5, which is shown in Table3.

Table 3. The summary of various types of NAMs-mGluR5 docking/MD results.

Representative No Year Template Binding Interactions Crucial Residues Authors Molecule mGluR5 Asn747, Ser805, Ser809, 1 Mavoglurant 2015 H-bond Harpsøe et al. [19] (PDB 4OO9) Tyr659, Thr781 Gly624, Ile625, Gly628, Basimglurant, mGluR5 Pro655, Met802, Ser805, 2 2015 H-bond Feng et al. [8] MTEP (PDB 4OO9) Val806, Ser809, Asn747, Tyr659 Thiazole-2- Pro655, Tyr659, Val806, mGluR5 Hydrophobic 3 carboxamides 2016 Ser809, Ala813, Ala810, Vu et al. [18] (PDB 4OO9) interaction, H-bond (6bc, 6bj) Ile625 Gly624, Ile625, Gly628, mGluR5 Mavoglurant, Hydrophobic Pro655, Ser805, Val806, 4 2017 (PDB 4OO9, Bian et al. [20] HTL14242 interaction, H-bond Ser809, Asn747, Trp785, 5CGD) Tyr659, Thr735 Ile625, Pro655, Ala810, Mavoglurant, mGluR5 Ser654, Ser809, Asn747, Dipraglurant, (PDB 4OO9, Hydrophobic Ile651, Tyr659, Ser805, 5 2018 Fu et al. [21] Basimglurant, 5CGC, interaction, H-bond Trp785, Phe788, Leu744, Fenobam 5CGD) Met802, Val806, Ser658, Val740 Tyr659, Ser809, Trp785, Hydrophobic mGluR5 Phe788, Ser805, Ile651, Christopher et al. 6 Fenobam 2018 interaction, H-bond, (PDB 6FFH) Leu744, Ile784, Met802, [22] π–π stacking Val806, Pro655

Up to now, two mGluR5 receptor binding regions (the orthosteric binding region in the VFT and the allosteric binding region in the 7TM) have been reported [24]. In fact, we find that the studies on the NAM site almost all focus on the transmembrane allosteric binding region [8,19–22], which can be explained by the fact that the allosteric binding pocket is less conserved compared to the orthosteric binding region, thus helping the identification of selective ligands [9]. In structure, the 8 antagonists belong to the acetylenic chemotype and non-acetylenic chemotype, as shown in Figure8. Despite their structural diversity, they bind to the same cavity. By comparing the key amino acids that make up the cavity in Table2, we find that, as a matter of fact, in the 7TMs of mGluR5 only one binding pocket (Site 1) is identified in charge of interacting with them. Although they all binding to the same site, these NAMs exhibit different spatial conformations due to their diverse structures. For better comparison of the ligand-receptor binding modes of the antagonists, we performed molecular docking analyses on all above acetylenic and non-acetylenic Molecules 2020, 25, 406 12 of 22

molecules using PDB 6FFI as mGluR5 template (as same as the present work). Table4 summarizes Molecules 2020, 25, x FOR PEER REVIEW 12 of 23 the results of these studies. Our docking results show that these molecules do bind to the same cavity in 7TM, and this site consists of two chambers (sub-pocket S1 and sub-pocket S2) and a narrow orthosteric binding region, thus helping the identification of selective ligands [9]. In structure, the 8 passage connecting them. As in structure, the skeleton of all NAMs is composed of polycyclic antagonists belong to the acetylenic chemotype and non-acetylenic chemotype, as shown in Figure 8. structures, the aromatic ring of the molecules often stretches to one of these two sub-pockets. Factually, Despite their structural diversity, they bind to the same cavity. By comparing the key amino acids by observing our docking results, we can find that the active conformation of all molecules can be that make up the cavity in Table 2, we find that, as a matter of fact, in the 7TMs of mGluR5 only one divided into two categories, namely “linear” and “arc” configuration, as shown in Figures9 and 10. binding pocket (Site 1) is identified in charge of interacting with them.

Figure 8. StructureStructure of of eight eight known known mGluR5 mGluR5 negative negative allo allostericsteric modulators modulators (NAMs) that have entered and are about to enter clinical trials.

Although they allTable binding 4. Interaction to the same features site, ofth representativeese NAMs exhibit mGluR5 different NAMs. spatial conformations

due to their diverse structures. For better comparison of the ligand-receptor binding modesBinding of the No Category Representative Molecule Site Type Binding Interactions antagonists, we performed molecular docking analyses on all above acetylenic and non-acetylenicConformation molecules1 Acetylenicusing PDB 6FFI Mavoglurantas mGluR5 template Site 1(as same Hydrophobic, as the present H-bond, π –work).π interactions Table 4 summarizes Arc 2 Acetylenic Basimglurant Site 1 Hydrophobic, H-bond Arc the results3 Acetylenic of these studies. Our MTEP docking results Site show 1 that Hydrophobic, these molecules H-bond, π–π dointeractions bind to the same Linear cavity in 7TM,4 and Acetylenic this site consists Dipraglurant of two chambers Site 1 (sub-pocket Hydrophobic, S1 H-bond,and sub-pocketπ–π interactions S2) and Arca narrow Thiazole-2-carboxamides 5 Non-acetylenic Site 1 Hydrophobic, H-bond, π–π interactions Linear passage connecting them. As(6bc) in structure, the skeleton of all NAMs is composed of polycyclic Thiazole-2-carboxamides structures,6 Non-acetylenic the aromatic ring of the moleculesSite 1often Hydrophobic,stretches H-bond,to oneπ –ofπ interactions these two sub-pockets. Linear (6bj) Factually,7 Non-acetylenic by observing our docking Fenobam results, we Site can 1 find that the Hydrophobic, active conformation H-bond of all Linearmolecules can 8be divided Non-acetylenic into two categories, HTL14242 namely “linear” Site 1 and Hydrophobic, “arc” configuration, H-bond, π–π interactions as shown in ArcFigures 9 and 10. Observation of the docking results reveals that molecules thiazole-2-carboxamide (6bc), (6bj), MTEP and Fenobam areTable in 4. a “linear”Interaction configuration. features of representative Takes ligand mGluR5 Thiazole-2-carboxamides NAMs. (6bj) as an example. Its structural feature is that the body is chain-shaped, and at each end of the chainBinding one No Category Representative Molecule Site Type Binding Interactions aromatic ring is located. Each of the aromatic rings is located in a sub-pocket of the cavity. AsConformation shown in 1 Figure9 Acetylenic, in its active conformation, Mavoglurant hydrophobic, Site H-bond, 1 Hydrophobic, and π–π stacking H-bond, π interactions–π interactionsare involved, Arc 2 wherein Acetylenic hydrophobic interaction Basimglurant of residues Ala973, Site 1 Ala970, Ile625, Hydrophobic, Ile944, H-bond and Pro655 is identified Arc 3 Acetylenic MTEP Site 1 Hydrophobic, H-bond, π–π interactions Linear 4 in the Acetylenic vicinity of the thiazole Dipraglurant in the sub-chamber Site 1 S1. Hydrophobic, Furthermore, H-bond, residues π–π interactions Ser654, Tyr659, Arc and Ser969 form a pluralityThiazole-2-carboxamides of H-bonds (-O S-, 3.43 Å; -O O-, 3.42 Å; -N O-, 2.69 Å) with the sulfur, 5 Non-acetylenic ··· Site 1 ··· Hydrophobic, H-bond,··· π–π interactions Linear oxygen, and nitrogen atoms(6bc) in thiazole, respectively in S1. In sub-pocket S2, π–π stacking between Thiazole-2-carboxamides 6 pyridylNon-acetylenic rings ofresidue Trp945 and 6bj is observedSite 1 in a Hydrophobic, face-to-face H-bond, manner. π–π Besides,interactions hydrophobic Linear (6bj) 7 interaction Non-acetylenic of hydrophobic residues Fenobam Trp945, Leu904, Site 1 Val966, and Phe948,Hydrophobic, is also H-bond identified in the vicinity Linear 8 of the Non-acetylenic pyridyl group in sub-pocket HTL14242 S2. It is the Site above 1 interactionHydrophobic, featuresH-bond, π– thatπ interactions keep 6bj in a linear Arc conformation. In terms of other “linear”-type mGluR5 antagonists, this characteristic is also observed. That is, the molecules are chain-like in skeleton, and there are two aromatic rings at both ends of the Molecules 2020, 25, 406 13 of 22 chain which enter the two sub-pockets of S1 and S2, respectively when binding to S1. In addition, the skeleton chain linking the two end aromatic rings is comparatively short, generally less than 4 chemical bonds. Thus, it is assumed that NAMs with the above structural characteristics may adopt linear conformations to bind stably with the mGluR5 receptor. Molecules 2020, 25, x FOR PEER REVIEW 13 of 23

Figure 9.9. “Linear”“Linear” binding binding modes modes of compoundsof compounds Fenobam, Fenoba MTEP,m, MTEP, 6bc, and6bc, 6bj and in the6bj allostericin the allosteric binding sitebinding 1 complexed site 1 complexed with mGluR5. with mGluR5. The H-bond The andH-bondπ–π stackingand π–πinteractions stacking interactions are observed are asobserved dark blue as anddark purple blue and dashed purple lines, dashed respectively. lines, respectively. Molecules 2020, 25, 406 14 of 22

Molecules 2020, 25, x FOR PEER REVIEW 14 of 23

FigureFigure 10. 10. “Arc”“Arc” binding binding modes modes of compounds of compounds Mavogl Mavoglurant,urant, Basimglurant, Basimglurant, Dipraglurant, Dipraglurant, and HTL14242and HTL14242 in the in allosteric the allosteric binding binding site 1 complexed site 1 complexed with mGluR5. with mGluR5. The H-bond The and H-bond π–π andstackingπ–π interactionsstacking interactions are observed are observedas dark blue as dark and bluepurple and dashed purple lines, dashed respectively. lines, respectively.

ObservationWhereas, molecules of the docking Dipalglurant, results Mavoglurant, reveals that Basimglurant,molecules thiazole-2-carboxamide HTL14242, and Compound (6bc), 74(6bj),are MTEPobserved and in Fenobam an “arc” configurationare in a “linear” in theconfiguration. binding complex. Takes ligand In structure, Thiazole-2-carboxamides actually, their skeletons (6bj) areas analso example. chain-like Its (Figurestructural 10 ).feature However, is that unlike the body those is NAMs chain-shaped, with linear and active at each conformation, end of the chain not onlyone aromaticaromatic ring rings is exist located. at both Each ends of the of the aromatic chain, butrings also is located more than in a one sub-pocket aromatic of ring the exists cavity. at oneAs shown end of inthe Figure chain. 9, In in interaction its active forces, conformation, similar to hydrophobic, the “linear” configuration, H-bond, and hydrophobic,π–π stacking H-bond, interactions and πare–π involved,stacking interactions wherein hydrophobic are also identified interaction contributing of residu toes theAla973, “arc” Ala970, configuration. Ile625, Ile944, Taking and Dipalglurant Pro655 is identifiedas an example, in the wherevicinity hydrophobic of the thiazole interactions in the sub-chamber around sub-pockets S1. Furthermore, S1 and residues S2, and Ser654, a π–π stacking Tyr659, andinteraction Ser969 form between a plurality the pyridyl of H-bonds group and(-O ···S-, residue 3.43 Trp945 Å; -O···O-, in a 3.42 face-to-face Å; -N···O-, way 2.69 in S2Å) arewith all the observed. sulfur, oxygen,The essential and nitrogen difference atoms between in thiazole, the “linear” respectively and “arc” in S1. configurations In sub-pocket isS2, that π–π the stacking polycyclic between ring pyridylstructure rings in the of “arc”residue configuration Trp945 and provides 6bj is observed more opportunity in a face-to-face to form manne H-bondr. Besides, interactions. hydrophobic Actually, interactionin the Dipalglurant-mGlu5 of hydrophobic residues complex, Trp945, four H-bonds Leu904, are Val966, identified and inPhe948, sub-pocket is also S1. identified In addition in the to vicinity of the pyridyl group in sub-pocket S2. It is the above interaction features that keep 6bj in a linear conformation. In terms of other “linear”-type mGluR5 antagonists, this characteristic is also Molecules 2020, 25, 406 15 of 22 three H-bonds formed by the amino acid residues Ser969 (-O NH-, 3.44 Å), Ser658 (-O NH-, 3.19 ··· ··· Å), and Tyr659 (-O N-, 2.51 Å), it also includes a H-bond formed by a water molecule (-O OH-, ··· ··· 2.89 Å). For compounds Mavoglurant, Basimglurant, HTL 14,242, and Compound 74, the above characteristics are also observed. Therefore, the molecule is long-chain on the backbone and has one or more aromatic rings at each end of the long chain. The heteroatoms on the polycyclic ring can form a plurality of hydrogen bonds with the surrounding amino acid residues, and the molecule having these characteristics is “arc” conformation. To sum up: (1) mGluR5 NAMs all bind at Site 1 (Figure 11A) despite the different spatial conformations of the molecule. The most important residues involved in Site 1 are Tyr659, Pro655, Ser809, and Ser805. (2) This site consists of three main regions including the two chambers (sub-pocket S1 and sub-pocket S2) and a narrow passage connecting them, as shown in Figure 11B, C. (3) The ligand-receptor binding modes are mainly stabilized by the complex interactions including hydrophobic (Pro655, Ile625, Ile651, Ala973, Ala970, Val966, Trp945, and Met962), H-bond (Ser969, Tyr659, Ser658, Ser654, Met962, Asn907) and π–π-stacking (Trp945) interactions. (4) The polycyclic binding ring determines two different molecular active conformations, namely the “linear” and “arc” docking modes, as shown in Figure 12. (5) The aryl benzamide derivatives studied in our work also bind to Site 1 as mGluR5 NAMs, in an “arc” conformation stabilized by hydrophobic, H-bond, and π–π stacking interactions. All these findings, we hope, may be of help for providing insights for the future exploitation of novel mGluR5 NAMs with high inhibitory activities. Molecules 2020, 25, x FOR PEER REVIEW 16 of 23

Figure 11. ((AA)) NAMs’ NAMs’ binding binding Site 1. ( (BB)) The The cavity cavity structure structure of compound 6bj at Site 1. ( (C) The The cavity cavity structure of compound DipalglurantDipalglurant atat SiteSite 1.1.

Figure 12. Binding modes of NAMs with mGluR5. (A) The “linear” docking modes of compound 6bj. (B) The “arc” docking modes of compound Dipalglurant.

Evaluating a 3D-QSAR model depends not only on how well it fits the data but more importantly on how well it predicts the data. Contour maps, molecular docking, and MD simulation results provide guidance for designing domain-specific substituents on compound scaffolds that are critical for improving the inhibitory potency of mGluR5 NAMs. Therefore, presently, to further validate these conclusions, based on the structural skeleton of molecule 74 (as a template), a total of 8 novel aryl benzamide derivatives were designed and their potential activities against mGluR5 were predicted as well adopting the optimal CoMSIA model. Table 5 summarizes the chemical structures of these molecules and their corresponding predicted activities. Unsurprisingly, all of them (NC1~NC8) present higher pIC50 values than that of the template compound 74 (pIC50 = 8.13), suggesting their great potential to be potent mGluR5 NAMs. Molecules 2020, 25, x FOR PEER REVIEW 16 of 23

MoleculesFigure2020 11., 25 ,(A 406) NAMs’ binding Site 1. (B) The cavity structure of compound 6bj at Site 1. (C) The cavity16 of 22 structure of compound Dipalglurant at Site 1.

Figure 12. Binding modes of NAMs with mGluR5. ( (AA)) The The “linear” “linear” docking docking modes modes of of compound compound 6bj. 6bj. ((B)) TheThe “arc”“arc” dockingdocking modesmodes ofof compoundcompound Dipalglurant.Dipalglurant.

Evaluating aa 3D-QSAR 3D-QSAR model model depends depends not onlynot ononly how on well how it fitswell the it data fits butthe more data importantly but more onimportantly how well on it how predicts well theit predicts data.Contour the data. maps,Contour molecular maps, molecular docking, docking, and MD and simulation MD simulation results provideresults provide guidance guidance for designing for designing domain-specific domain-specific substituents substituents on compound on compound scaffolds scaffolds that are that critical are forcritical improving for improving the inhibitory the inhibitory potency of potency mGluR5 of NAMs. mGluR5 Therefore, NAMs. presently, Therefore, to furtherpresently, validate to further these conclusions,validate these based conclusions, on the structuralbased on the skeleton structural of molecule skeleton74 of(as molecule a template), 74 (as aa totaltemplate), of 8 novel a total aryl of benzamide8 novel aryl derivativesbenzamide werederivatives designed were and designed their potential and their activities potential against activities mGluR5 against were mGluR5 predicted were aspredicted well adopting as well theadopting optimal the CoMSIA optimal model.CoMSIA Table model.5 summarizes Table 5 summarizes the chemical the chemical structures structures of these moleculesof these molecules and their corresponding and their corresponding predicted activities. predicted Unsurprisingly, activities. allUnsurprisingly, of them (NC1~NC8) all of present them higher(NC1~NC8) pIC50 presentvalues thanhigher that pIC of50 the values template than compoundthat of the74 template(pIC50 = compound8.13), suggesting 74 (pIC their50 = 8.13), great potentialsuggestingMolecules to Moleculestheir be 2020 potent, 25great 2020, x FOR, mGluR5 25potential ,PEER x FOR REVIEW PEER NAMs. to REVIEW be potent mGluR5 NAMs. 17 of 23 17 of 23 MoleculesMolecules 2020, 25 2020,2020 x FOR,, 2525 ,,PEER xx FORFOR REVIEW PEERPEER REVIEWREVIEW 17 of 2317 17 ofof 2323

Table 5.Table Structures 5. Structures and predicted and predicted activities activities of newly of newlydesigned designed mGluR5 mGluR5 NAMs. NAMs. TableTable 5. Structures 5.Table Structures 5. StructuresStructures and and predicted predicted andand predictedpredicted activitiesactivities activitiesactivities of of newly newly ofof designednewlynewly designed designeddesigned mGluR5 mGluR5 mGluR5mGluR5 NAMs. NAMs. NAMs.NAMs. No No StructuresStructures and pIC and50 ValuespIC50 Values No No Structures Structures and pIC and50 ValuespIC50 Values No Structures and pIC50 Values50 No Structures and pIC50 Values50 NoNo No StructuresStructuresStructures andand pIC and50 ValuespICValues50 ValuesValues No No No No Structures Structures Structures Structures and pIC and andand50 pIC ValuespICpIC5050 ValuesValues H C H C H3C H3C H3C 3 H3C H3C 3 H3C H3C H3C H3C H3C

H H O O H H O O H H O O H H O O H3C H3CN NH HN O O H3C H3CN NHN HN O O H3C H 3CN N N N N H3C H 3CN NN N N N NC1 NC13 3 N NC2N NC23 3 N N NC1NC1 NC1 N NC2NNC2 NC2 N N O O O O O O O O O

Cl Cl Cl Cl Cl Cl Cl Cl Cl Cl F F Cl Cl Cl Cl Cl Cl F FF Cl Cl 50 50 pIC50 =pIC 8.401 = 8.401 pIC50 =pIC 8.39850 = 8.398 pIC50 =pIC 8.4015050 == 8.4018.401 pIC50 =pIC 8.39850 = 8.398 pIC50 = 8.401 pIC50pIC =pIC 8.3985050 == 8.398 Cl Cl H3C H3C Cl H3C H3C Cl H3C H3C Cl

H H O O H H O O H H O O H H O O H3C H3CN NH HN O O H3C H3CN NHN HN O O H3C H3NC N N H3C H3CN N N NC3 NC3H3C H3CN N N N NC4N NC4H3C H3CN NN N N N NC3NC3 NC3 N NC4NNC4 NC4 N N O O O O O O O O O O Cl Cl O O F F Cl Cl F FF Cl Cl Cl Cl Cl Cl Cl Cl Cl Cl Cl Cl pIC50 =pIC 8.38750 = 8.387 50 50 pIC50 =pIC 8.38750 = 8.387 pIC50 =pIC 8.381 = 8.381 pICpIC5050 =pIC 8.3878.38750 = 8.387 pIC50pIC =pIC 8.381505050 = == 8.3818.381 Cl Cl H3C H3C Cl Cl H3C H3C Cl Cl H3C H3C

H H O O H H O O H H O O H H O O H3C H3CN NH HN O O HN HN O O H3C H3NC N N N N N H3C H3CN N N N N N NN N N N NC5NC5 NC5 N NC6NNC6 NC6 N N N N NC5 NC5 NC6 NC6H3C H3C O O H3C H 3C S OS O O O 3 3 S OS O F F F F O S S F F F FF F F F Cl Cl F F Cl Cl F FF pIC50 = 8.37650 pIC50 =pIC 8.34650 = 8.346 pIC5050 =pIC8.37650 = 8.376 pIC50pIC =pIC 8.3465050= = 8.346 pIC50 =pIC 8.37650 == 8.3768.376 pIC50 =pIC 8.34650 = 8.346 Cl Cl Cl Cl Cl Cl Cl ClCl

H O O H H O O H H O O H H O O HN HN O O H3C H3CN NHN HN O O N N N N H3C H 3CN NN N N NC7 NC7 N N N N NNC8 NC83 3 N N NC7 NC7 N N N NNNC8 NC8 N N H3C H3C H3C H3C S OS O H3C H3C S OS O O O F F S OS O F F O O F F F FF F F F F F F Cl Cl F F F F Cl Cl F F pIC50 =pIC 8.33550 = 8.335 Cl Cl F F pIC50 =pIC 8.33550 = 8.335 50 50 pIC50 =pIC 8.33550 = 8.335 pIC50 =pIC 8.331 = 8.331 pIC50 =pIC 8.3315050 == 8.3318.331 4. Materials4. Materials and Methods and Methods 4. Materials4.4. MaterialsMaterials and Methods andand MethodsMethods 4.1. Dataset4.1. Dataset and Biological and Biological Activities Activities 4.1. Dataset4.1.4.1. DatasetDataset and Biological andand BiologicalBiological Activities ActivitiesActivities A totalA of total newly of newly synthesized synthesized 106 aryl 106 benzamide aryl benzamide series seriesof mGluR5 of mGluR5 non-competitive non-competitive antagonists antagonists A totalA of total newly of newly synthesized synthesized 106 aryl 106 benzamide arylaryl benzamidebenzamide series seriesofseries mGluR5 ofof mGluR5mGluR5 non-competitive non-competitivenon-competitive antagonists antagonistsantagonists [14–16][14–16] are adopted are adopted to build to abuild dataset a dataset for developing for developing a reliable a reliable 3D-QSAR 3D-QSAR model model in this in study. this study. Their Their [14–16][14–16][14–16] are adopted areare adoptedadopted to build toto abuildbuild dataset aa datasetdataset for developing forfor developingdeveloping a reliable aa reliablereliable 3D-QSAR 3D-QSAR3D-QSAR model modelmodel in this in instudy. thisthis study. study.Their TheirTheir experimentalexperimental activities activities are converted are converted into corresponding into corresponding pIC (pIC–logIC (–logIC) values) values that are that then are used then used experimentalexperimentalexperimental activities activitiesactivities are converted areare convertedconverted into corresponding intointo correspondingcorresponding pIC ( pIC–logIC ((–logIC–logIC) values)) valuesvalues that are thatthat then areare used thenthen usedused as dependentas dependent variables variables for QSAR for QSAR modeling. modeling. The datase The dataset is randomlyt is randomly divided divided into a intotraining a training set of 82set of 82 as dependentasas dependentdependent variables variablesvariables for QSAR forfor QSAR QSARmodeling. modeling.modeling. The datase TheThe datasedataset is randomlytt isis randomlyrandomly divided divideddivided into a intotraininginto aa trainingtraining set of 82setset ofof 8282 compoundscompounds and a andtesting a testing set of set24 compoundsof 24 compounds at a ratio at a ofratio approximately of approximately 3:1. To 3:1. ensure To ensure that the that the compoundscompoundscompounds and a andandtesting aa testingtesting set of setset24 compoundsofof 2424 compoundscompounds at a ratio atat aa of ratioratio approximately ofof approximatelyapproximately 3:1. To 3:1.3:1. ensure ToTo ensureensure that the thatthat thethe predictivepredictive power power of the of model the model be effectively be effectively evaluated, evaluated, the testing the testing set molecules set molecules are selected are selected predictivepredictive power power of the of model the model be effectively be effectively evaluated, evaluated, the testing the testing set molecules set molecules are selected are selected followingfollowing the rules: the rules:(1) their (1) pIC their valuespIC values are randomly are randomly but evenly but evenly distributed distributed over the over entire the entirerange range followingfollowingfollowing the rules: thethe rules:(1)rules: their (1)(1) pIC theirtheir values pIC valuesvalues are randomly areare randomlyrandomly but evenly butbut evenlyevenly distributed distributeddistributed over the overover entire thethe entireentirerange rangerange of testof values; test values; (2) their (2) structuretheir structure covers covers the larges the tlarges possiblet possible diversity diversity of the ofdataset, the dataset, so the soderived the derived of testof values; test values; (2) their (2) structuretheir structure covers covers the larges the tlarges possiblett possiblepossible diversity diversitydiversity of the ofofdataset, thethe dataset,dataset, so the sosoderived thethe derivedderived modelsmodels can represent can represent the true the characteristics true characteristics of all compoundsof all compounds from bothfrom the both biological the biological activity activity and and modelsmodels can represent can represent the true the characteristics true characteristics of all compoundsof alll compoundscompounds from both fromfrom the bothboth biological thethe biologicalbiological activity activityactivity and andand MoleculesMolecules 2020, 25 2020, x FOR, 25 ,PEER x FOR REVIEW PEER REVIEW 17 of 23 17 of 23

Table 5.Table Structures 5. Structures and predicted and predicted activities activities of newly of designednewly designed mGluR5 mGluR5 NAMs. NAMs.

No No StructuresStructures and pIC and50 ValuespIC50 Values No No Structures Structures and pIC and50 ValuespIC50 Values

H3C H3C H3C H3C

H H O O H H O O H3C H3CN N N H3C H3CN N N NC1 NC1 N NC2N NC2 N N

O O O O

Cl Cl Cl Cl F F Cl Cl pIC50 =pIC 8.40150 = 8.401 pIC50 =pIC 8.39850 = 8.398

Cl H3C H3C Cl

H H O O H H O O H3C H3CN N N H3C H3CN N N NC3 NC3 N NC4N NC4 N N

O O O O F F Cl Cl Cl Cl Cl Cl pIC50 =pIC 8.38750 = 8.387 pIC50 =pIC 8.38150 = 8.381

Cl Cl H3C H3C

H H O O H H O O Molecules 2020, 25H3,C 406 H3CN N N N N 17 of 22 NC5 NC5 N NC6N NC6 N N N N H C H C O O 3 3 S OS O F F F F Table 5. Cont. Cl Cl F F

pIC50 =pIC 8.37650 = 8.376 pIC50 =pIC 8.34650 = 8.346 No Structures and pIC50 Values No Structures and pIC50 Values

Cl Cl Cl Cl

H H O O H H O O N N H3C H3CN N N NC7NC7 NC7 N N N NNC8NC8 NC8 N N H3C H3C S OS O O O F F F F

F F Cl Cl F F 50 pIC50 = 8.335 pICpIC50 = 8.3358.335 pIC50pIC =pIC 8.3315050 == 8.331

4. Materials4. Materials and Methods and Methods 4. Materials and Methods 4.1. Dataset4.1. Dataset and Biological and Biological Activities Activities 4.1. Dataset and Biological Activities A totalA of total newly of newlysynthesized synthesized 106 aryl 106 benzamide aryl benzamide series ofseries mGluR5 of mGluR5 non-competitive non-competitive antagonists antagonists A[14–16] total[14–16] are of adopted newlyare adopted to synthesizedbuild to abuild dataset a dataset 106for developing aryl for developing benzamide a reliable a reliable 3D-QSAR series 3D-QSAR of model mGluR5 model in this instudy. non-competitive this study. Their Their antagonistsexperimentalexperimental[14–16 activities] are activities adopted are converted are to converted build into acorresponding into dataset corresponding for pIC developing (pIC–logIC (–logIC a) values reliable) values that 3D-QSAR are that then are used then model used in this study.as dependentas Their dependent experimentalvariables variables for QSAR activitiesfor QSARmodeling. modeling. are convertedThe datase The dataset is into randomlyt corresponding is randomly divided divided intopIC a traininginto( a logtraining set IC of 82)set values of 82 50 − 50 that arecompounds thencompounds used and as a dependent andtesting a testing set variablesof set24 compoundsof 24 for compounds QSAR at a modeling.ratio at a of ratio approximately of The approximately dataset 3:1. is To randomly 3:1. ensure To ensure that divided the that intothe predictivepredictive power power of the of model the model be effectively be effectively evaluated, evaluated, the testing the testing set molecules set molecules are selected are selected a training set of 82 compounds and a testing set of 24 compounds at a ratio of approximately 3:1. followingfollowing the rules: the (1)rules: their (1) pIC their valuespIC values are randomly are randomly but evenly but evenly distributed distributed over the over entire the entirerange range To ensure that the predictive power of the model be effectively evaluated, the testing set molecules of test ofvalues; test values; (2) their (2) structure their structure covers covers the larges the tlarges possiblet possible diversity diversity of the ofdataset, the dataset, so the soderived the derived are selectedmodelsmodels followingcan represent can represent the the rules: true the characteristics (1) true their characteristicspIC50 of valuesall compoundsof al arel compounds randomly from both from but the both evenly biological the biological distributed activity activity and over and the entire range of test values; (2) their structure covers the largest possible diversity of the dataset, so the derived models can represent the true characteristics of all compounds from both the biological activity and the molecular structures. The information of all 106 molecules is provided in the support information in Table S1.

4.2. Molecular Modeling and Alignment Before 3D-QSAR modeling, it is necessary to perform conformational optimization for the chemicals from the sample dataset. For generating the optimal 3D-QSAR model, three different alignment rules are adopted, i.e., alignment-I, -II, and -III. The first alignment strategy is an atom-based approach, where all optimized molecules are stacked into the template using the Align Database module in SYBYL. The most potent antagonist (molecule 74) is selected as a template for molecular alignment to construct all 3D-QSAR statistical models (Figure 13B). Alignment-II (Figure 13C) is a receptor-based approach, which is generated based on the active conformation of template compound 74 that is derived from the docking simulation. The third alignment rule (Figure 13D) is still a receptor-based approach. In this method, all active conformations of the whole dataset molecules are first extracted from the dock studies, and then subjected to the process of alignment-I, i.e., the conformation of molecule 74 is also chosen as the template molecule to be superposed by all other molecules using receptor-based conformations. Figure 13A depicts the common skeleton (peptide bond) of compound 74 in the molecular superposition which is shown by the purple stick. In addition, the superposed models built based on alignment-I, II, and III are shown in Figure 13B, C, D, respectively. Then the modeling process is performed. The partial atomic charge is calculated using the Gasteiger–Hückel method [25], and the energy is minimized using the Tripos molecular mechanics field and the Powell conjugate gradient minimization algorithm [26], where the convergence criteria 1 1 are set to 0.05 kcal mol− Å to ensure the stability of the conformation. · · − Molecules 2020, 25, x FOR PEER REVIEW 19 of 23

4.2. Molecular Modeling and Alignment Before 3D-QSAR modeling, it is necessary to perform conformational optimization for the chemicals from the sample dataset. For generating the optimal 3D-QSAR model, three different alignment rules are adopted, i.e., alignment-I, -II, and -III. The first alignment strategy is an atom- based approach, where all optimized molecules are stacked into the template using the Align Database module in SYBYL. The most potent antagonist (molecule 74) is selected as a template for molecular alignment to construct all 3D-QSAR statistical models (Figure 13B). Alignment-II (Figure 13C) is a receptor-based approach, which is generated based on the active conformation of template compound 74 that is derived from the docking simulation. The third alignment rule (Figure 13D) is still a receptor-based approach. In this method, all active conformations of the whole dataset molecules are first extracted from the dock studies, and then subjected to the process of alignment-I, i.e., the conformation of molecule 74 is also chosen as the template molecule to be superposed by all other molecules using receptor-based conformations. Figure 13A depicts the common skeleton (peptide bond) of compound 74 in the molecular superposition which is shown by the purple stick. InMolecules addition,2020, 25the, 406 superposed models built based on alignment-I, II, and III are shown in Figure18 13B, of 22 C, D, respectively.

Figure 13. Molecular alignment ofof allall compoundscompounds in in the the dataset. dataset. ( A(A)) The The most most e ffeffectiveective compound compound74 74is isused used as aas template. a template. The commonThe common structure structure is marked is marked by the purple by the stick. purple The moleculestick. The is molecule represented is representedby a rod model by ina rod which model the moleculesin which the C, H,molecules N, O, Cl, C, and H, F N, are O, represented Cl, and F are by white,represented cyan, navyby white, blue, cyan,red, green, navy andblue, blue, red, respectively.green, and blue, Alignment-I, respectively. -II, andAlignment-I, -III of all -II, the and compounds -III of all arethe shown compounds in panels are (shownB), (C), in and panels (D), ( respectively.B), (C), and (D), respectively.

4.3. CoMFAThen the and modeling CoMSIA Studiesprocess is performed. The partial atomic charge is calculated using the Gasteiger–HückelAfter molecular method stacking, [25], alland molecules the energy are is placedminimized in a using 3D lattice the Tripos with a molecular grid spacing mechanics of 2 Å. Twofield and different the Powell 3D-QSAR conjugate methods, gradient CoMFA minimizati and CoMSIA,on algorithm are [26], used where to compare the convergence and analyze criteria the kcal ∙ mol ∙Å quantitativeare set to 0.05 relationships between to ensure the three-dimensional the stability of the structural conformation. features and the biological activity of the molecules. 4.3. CoMFA and CoMSIA Studies The CoMFA field is analyzed by using a sp3 C atom probe with a formal charge of +1.0 for each latticeAfter point molecular and a Van stacking, der Waals all molecules (VdW) radius are placed of 1.52 in a Å 3D [27 lattice]. The with stereo a grid and spacing electrostatic of 2 Å. fields Two differentare calculated 3D-QSAR using methods, the CoMFA CoMFA standard and CoMSIA, method withare used energy to compare cut-off valuesand analyze of 30.0 thekcal quantitative/mol [28]. relationshipsCoMSIA is an between extension the of CoMFA,three-dimensional and the CoMSIA structur similarityal features index and descriptor the biological is derived activity using of the molecules.same grid box as in the CoMFA calculation. Five different similar descriptors for steric, electrostatic, hydrophobic, and H-bond donors and acceptors are calculated by using a probe atomic charge +1.0, radius 1.0 Å, and hydrophobicity +1.0. The Gaussian function is used to estimate the mutual distance between each molecular atom and the probe atom, and there is no cut-off limit in the CoMSIA study. A statistically significant 3D-QSAR model is obtained by using PLS regression analysis, where the relationship between the experimental inhibitory activity pIC50 and CoMFA and CoMSIA descriptors are analyzed [29,30]. In the PLS analysis, we use the leave-one-out method to evaluate the reliability of the model by calculating the conventional correlation coefficient (Q2), the standard prediction error (SEP), and the optimum number of components (OPN). Using the molecules of all training sets in 2 combination with OPN, Pearson coefficients (Rncv), standard error of estimate (SEE), and F value are 2 2 calculated by non-cross-validation analysis [31]. Among them, Q and Rncv are used as statistical indicators for the model prediction ability. An independent testing set is used to evaluate the predictive power of the CoMFA/CoMSIA methods. The CoMFA/CoMSIA similarity index, Q2 and predicted 2 2 R (Rpre) values in the above process are calculated. Finally, the CoMFA/CoMSIA results are visually presented in the form of an equipotential map. Molecules 2020, 25, 406 19 of 22

4.4. Molecular Docking To study the interaction between the receptor and its ligand, we use Gold (Genetic Optimization for Ligand Docking) 5.1 for molecular docking analysis. It is a flexible docking technique where the protein is considered rigid, and all degrees of freedom of the ligand are explored (i.e., translational, rotational, and conformational) [32]. Using the PDB entry 6FFI protein structure (resolution, 2.6 Å; method, X-ray diffraction) as a template. Then we studied the docking with water molecules but found that there was no force on the water molecules. So, all water molecules are removed from the protein prior to docking, the ligand is also extracted, and hydrogen atoms are added to the receptor. In the docking, we use the corresponding scoring software to judge the complementarity and affinity of the ligand and the receptor. Scores are used to filter all acquired conformations to determine the most reliable conformation.

4.5. Molecular Dynamics To obtain an accurate protein-ligand binding model and to verify the docking results, in this paper, the CHARMM-GUI website (http://www.charmm-gui.org/?doc=input/membrane.bilayer)[33] is used to construct a phospholipid bilayer (POPC) to mimic the cell transmembrane environment at the 7TM site of the mGluR5 protein, and MD simulation is performed using the GROMACS software package [34]. The system of compound 74 with mGluR is solvated by applying TIP3 water model and enough chloride ions are added to reach zero charge [35]. First, the unconstrained energy minimization of the simulated system is performed using the steepest descent algorithm and conjugate gradient algorithm. Then the system is balanced at 300 K via 100 ps MD simulations. Finally, a 20 ns simulation is carried out in 2 fs time steps to maintain the stability of the entire system. Both energy minimization and MD simulation are performed under periodic boundary conditions with a temperature set of 300 K and atmospheric pressure. The electrostatic interactions are calculated using the Particle Mesh Ewald (PME) method [11] during the simulation. The Linear Constraint Solver (LINCS) algorithm [36] constrains the covalent bond involving the H atom. The cutoff distances for calculating the Van der Waals interaction and Coulomb interaction are set to 1.2 and 1.2 nm, respectively. The isothermal 5 1 compression rate is set to 4.5 10 bar− by the Parrinello–Rahman protocol while the pressure is × − maintained at 1.0 bar [37] and the temperature is kept constant using a Berendsen thermostat.

5. Conclusions In the current work, a comprehensive computer study of a total of 106 newly synthesized mGluR5-NAMs is conducted. Using a combination of 3D-QSAR, molecular docking, and MD simulation, the relationship between the structure of these compounds and their activity, and their interaction with target proteins are explored. Our findings are summarized below: 2 2 The optimal CoMSIA model developed is statistically predictable, Q = 0.70, Rncv = 0.89, and 2 Rpre = 0.87, demonstrating its significant internal and external predictive capabilities. The key structural determinants that influence the activity of mGluR5-NAM molecules based on the corresponding contour maps are identified. Furthermore, the aryl benzamide derivatives herein exhibit an approximate “arc” conformation and bind to the Site 1 of the mGluR5 receptor. Hydrophobic, H-bond, and π–π interactions are the main interactions with the cavity. Through our study, the binding patterns and characteristics of ligand small molecules and metabotropic glutamate receptor proteins are identified, and the mechanism of their binding is explored and interpreted, which provide guidance for the synthesis of new glutamic related neurologic diseases.

Supplementary Materials: The following are available online. Table S1: molecular structures and pIC50 values of aryl benzamide derivatives. Author Contributions: Y.Z. established the model, implemented the methods, and analyzed the results, and wrote this paper. J.C. helped perform the 3D-QSAR model. Q.L. revised the English grammar of the paper. Y.L. Molecules 2020, 25, 406 20 of 22 provided ideas and text corrections for the paper. The final draft read and approved by all authors. All authors have read and agreed to the published version of the manuscript Funding: This research was funded by [National Natural Science Foundation of China] grant number [81530100] and [U1603285]. And the APC was funded by [Yujing Zhao]. Acknowledgments: This work is supported by the National Natural Science Foundation of China [grant number 81530100] and the National Natural Science Foundation of China [grant number U1603285]. Conflicts of Interest: The authors declare no conflict of interest.

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Sample Availability: Samples of the compounds 1–106 are not available from the authors.

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