Availableonlineatwww.sciencedirect.com BCIENCEODDIRECTO Bioorganic edicinal Chemistry ELSEVIER Bioorganic Medicinal Chemistry 14(2006)601-610 Molecular modeling and 3D-QSAR studies of indolomorphinan derivatives as kappa opioid antagonists Wei li. a yun ta You-Li Zheng and Zhui-Bai Qiu Department of Medicinal Chemistry, School of Pharmacy, Fudan University, 138 Yixueyuan Road, Shanghai 200032, China sChool of Pharmacy, East China University of Science Technology, 130 Meilong Road, Shanghai 200237, China Received 6 July 2005: revised 19 August 2005: accepted 20 August 2005 Available online 3 October 2005 Abstract-Molecular modeling and 3D-Qsar studies were performed on 31 indolomorphinan derivatives to evaluate their antag- onistic behaviors on k opioid receptor and provide information for further modification of this kind of compounds. Best predictions were obtained with CoMFA standard model =0.693, N=4, r=0.900)and CoMSIA combined model (q=0.617, N=4, r=0.607 for CoMFA and r=0.701 for CoMSIA. In addition, the 3D structure of human k opioid receptor was constructed based on the crystal structure of bovine rhodopsin, and the ComsIa contour plots were then mapped into the structural model of K opioid receptor-GNTI complex to identify key residues, which might account for K antagonist potency and selectivity. The roles of non- conserved Glu297 and conserved Lys 227 of human k opioid receptor were then discussed c 2005 Elsevier Ltd. All rights reserved 1. Introduction the 5-substitutes of naltrindole, a potent 8 selective antagonist, led to the discovery of a more potent K selec There are three well-adopted subtypes of opioid recep- tive antagonist, 5-guanidine naltrindole(GNTI, 2). 1 tors:A, K, and 8. However, to date the precise role There were also some other types of k selective antago- of k opioid receptor has not been well established yet nists such as JDTic, 3(3)and KAA-1(4) 4 recently It appears that K opioid receptor exerts its physiological reported by Carroll and co-workers, which also owned roles by participating in pain process and regulating im- a second basic group(Fig. I mune systems.4 where selective K opioid antagonists could provide powerful tools to investigate detailed In order to elucidate the mechanism of action of known interactions between the receptor and ligand. Mean- kappa antagonists and design new kappa selective while, K selective antagonists also showed some clinical antagonists, molecular modeling and 3D-QSAR studi were conducted here. At first, 39 indolomorphinan derivatives, potent kappa selective antagonists by add- Nor-BNI (norbinaltorphimine, 1)is the first highly ing a basic or neutral group in the 5-position of naltrin- selective x opioid antagonist reported by portoghese dole, were collected from the literature. They were then et al, who believed that it was the second basic group divided into two groups: 31 compounds as training set that conferred K potency and selectivity of the com- and the other eight ones as test set. The training set pound. The hypothesis was confirmed in their subse- was used to build 3D-QSAR models with CoMFA quent work by structure simplification of nor-BNI.(comparative molecular field analysis) and CoMSIA The basic group was further identified interacting with (comparative molecular similarity indices analysis) Glu297 of k opioid receptor by site-directed mutagene- methods, while the test set was used to validate the sis. Moreover, the attachment of a basic group to 3D-QSAR models further. Meanwhile, due to unavail- ability of experimental structure, the 3D structure of kappa opioid receptor was constructed based on the Opioid receptor; Indolomorphinan derivatives: 3D rystal structure of bovine rhodopsin. The interaction lecular Modeling: CoMFA; CoMSIA mode of GNTI with kappa receptor was hence obtained ding authors.Tel:+862154237595:fax:+862154237264 Finally, the contour plots of CoMfA were mapped into tel. +86 21 54237419 (YT); e-mail addresses the binding site of kappa receptor to identify key hoo. com. cn; zbqiu(@shmu educn residues that might account for ligand binding and 0968-0896 ter 2005 Elsevier Ltd. All rights reserved. doi: 10.1016j.bmc. 200
Molecular modeling and 3D-QSAR studies of indolomorphinan derivatives as kappa opioid antagonists Wei Li,a Yun Tang,a,b,* You-Li Zhenga and Zhui-Bai Qiua,* a Department of Medicinal Chemistry, School of Pharmacy, Fudan University, 138 Yixueyuan Road, Shanghai 200032, China b School of Pharmacy, East China University of Science & Technology, 130 Meilong Road, Shanghai 200237, China Received 6 July 2005; revised 19 August 2005; accepted 20 August 2005 Available online 3 October 2005 Abstract—Molecular modeling and 3D-QSAR studies were performed on 31 indolomorphinan derivatives to evaluate their antagonistic behaviors on j opioid receptor and provide information for further modification of this kind of compounds. Best predictions were obtained with CoMFA standard model (q2 = 0.693, N = 4, r 2 = 0.900) and CoMSIA combined model (q2 = 0.617, N = 4, r 2 = 0.904). Both models were further validated by an external test set of eight compounds with satisfactory predictions: r 2 = 0.607 for CoMFA and r 2 = 0.701 for CoMSIA. In addition, the 3D structure of human j opioid receptor was constructed based on the crystal structure of bovine rhodopsin, and the CoMSIA contour plots were then mapped into the structural model of j opioid receptor–GNTI complex to identify key residues, which might account for j antagonist potency and selectivity. The roles of nonconserved Glu297 and conserved Lys227 of human j opioid receptor were then discussed. 2005 Elsevier Ltd. All rights reserved. 1. Introduction There are three well-adopted subtypes of opioid receptors: l, j, and d. 1,2 However, to date the precise role of j opioid receptor has not been well established yet. It appears that j opioid receptor exerts its physiological roles by participating in pain process and regulating immune systems3,4 where selective j opioid antagonists could provide powerful tools to investigate detailed interactions between the receptor and ligand. Meanwhile, j selective antagonists also showed some clinical potentials.5,6 Nor-BNI (norbinaltorphimine, 1) is the first highly selective j opioid antagonist reported by Portoghese et al.,7 who believed that it was the second basic group that conferred j potency and selectivity of the compound.8 The hypothesis was confirmed in their subsequent work9 by structure simplification of nor-BNI. The basic group was further identified interacting with Glu297 of j opioid receptor by site-directed mutagenesis.10 Moreover, the attachment of a basic group to the 50 -substitutes of naltrindole, a potent d selective antagonist, led to the discovery of a more potent j selective antagonist, 50 -guanidine naltrindole (GNTI, 2).11 There were also some other types of j selective antagonists such as JDTic12,13 (3) and KAA-1(4) 14 recently reported by Carroll and co-workers, which also owned a second basic group (Fig. 1). In order to elucidate the mechanism of action of known kappa antagonists and design new kappa selective antagonists, molecular modeling and 3D-QSAR studies were conducted here. At first, 39 indolomorphinan derivatives, potent kappa selective antagonists by adding a basic or neutral group in the 50 -position of naltrindole, were collected from the literature. They were then divided into two groups: 31 compounds as training set and the other eight ones as test set. The training set was used to build 3D-QSAR models with CoMFA (comparative molecular field analysis) and CoMSIA (comparative molecular similarity indices analysis) methods, while the test set was used to validate the 3D-QSAR models further. Meanwhile, due to unavailability of experimental structure, the 3D structure of kappa opioid receptor was constructed based on the crystal structure of bovine rhodopsin. The interaction mode of GNTI with kappa receptor was hence obtained. Finally, the contour plots of CoMFA were mapped into the binding site of kappa receptor to identify key residues that might account for ligand binding and 0968-0896/$ - see front matter 2005 Elsevier Ltd. All rights reserved. doi:10.1016/j.bmc.2005.08.052 Keywords: j Opioid receptor; Indolomorphinan derivatives; 3DQSAR; Molecular Modeling; CoMFA; CoMSIA. * Corresponding authors. Tel.: +86 21 54237595; fax: +86 21 54237264 (Z.B.Q.); tel.: +86 21 54237419 (Y.T.); e-mail addresses: ytang234@yahoo.com.cn; zbqiu@shmu.edu.cn Bioorganic & Medicinal Chemistry 14 (2006) 601–610
W. Li et al./ Bioorg. Med. Chem. 14(2006)601-610 NH2 nor-BNI 1 GNTI 2 DTIc 3 (-)-KAA14 Figure 1. Some potent highly selective K antagonists reported recently selectivity. The studies provided us helpful information The most crucial step in performing CoMFA and CoM- on how to modify indolomorphinan derivatives SIA is to determine the bioactive conformations of the compounds so that all compounds could be aligned together. Though nor-BNI was not employed to estab 2. Materials and methods lish 3D-QSAR models here, its structure was selected as the template for structural alignment from the align 2.1. Data set ment facility in SYBYL, due to its selective x antagonistic potency and quite rigid structure. The final structura Thirty nine compounds were collected from several re- alignment is shown in Figure 2 ports of the Lewis group>-(Table 1). Most of these compounds were indolomorphinan analogs of GNTI 2.3. PLS analys (2)and showed significant k antagonistic potency. though they were also antagonists against H and As usual, PLS(partial least squares)method was used to subtypes, too. All compounds were evaluated in com- establish and validate comfa and comsia models petition binding assays with [H U69593 in cloned here The binding affinity K, values were converted into human k opioid receptor transfected into Chinese pK, values, to describe the biological activities. CoMFA hamster ovary (CHO)cells. Similar assays were also was set at standard values, with a sp carbon atom with intI by Jones and Portoghese. In one positive charge used to probe steric and electrostatic his study, gnti was only considered as a reference fields. The standard cutoff value was set to 30 kcal/mol structure to eliminate evaluation errors between differ- CoMSIA fields were set in their default opinions ent groups. Eight compounds were randomly selected from the 39 molecules to make a test set for further LOo (leave one out)cross-validation method was used model validation, and the rest of the 31 compounds to evaluate the initial models. The cross-validated coef. served as the training set ficient q was calculated using the following equation 2. 2. Molecular modeling and structural alignment 矿=10-20073)2 All calculations were carried out on a r14000 SGI Fuel actu workstation using molecular modeling software package where ,pred, actual, and ,mean are predicted, actual, and SYBYL V6.9.0 All compounds were constructed in sYBYL mean values of the target property (pKi), respectively, based on the crystal structure of nor-BNI- since these and press (7pred-7actua)" is the sum of predictive compounds may similarly bind to k opioid receptor. sum of squares. The optimum number of components Considering the vital role of a basic group for k selectiv- was then given, and CoMFA and CoMSIA models were ity and antagonistic potency, the basic groups of all hence derived corresponding to the optimum number compounds were fixed near the protonated nitrogen The parameters of confidence intervals were further esti atom of nor-BNI supposing they shared similar binding mated by bootstrap in 10 runs. The column filtering box models to K opioid receptor. All compounds were pre was kept unchecked during all operations. tonated and assigned with Gasteiger-Huckel charges For some more flexible compounds, systematic searches 2. 4. Validation of CoMFA and CoMSIa models were performed with an interval of 10 on rotatory bonds to ensure their low energy conformations. Final- In addition to LOO method to validate the CoMFA and ly, they were minimized with Tripos force field. CoMSIA models, a test set made up of eight compounds
selectivity. The studies provided us helpful information on how to modify indolomorphinan derivatives. 2. Materials and methods 2.1. Data set Thirty nine compounds were collected from several reports of the Lewis group15–18 (Table 1). Most of these compounds were indolomorphinan analogs of GNTI (2) and showed significant j antagonistic potency, though they were also antagonists against l and d subtypes, too. All compounds were evaluated in competition binding assays with [3 H] U69593 in cloned human j opioid receptor transfected into Chinese hamster ovary (CHO) cells. Similar assays were also performed on GNTI by Jones and Portoghese.19 In this study, GNTI was only considered as a reference structure to eliminate evaluation errors between different groups. Eight compounds were randomly selected from the 39 molecules to make a test set for further model validation, and the rest of the 31 compounds served as the training set. 2.2. Molecular modeling and structural alignment All calculations were carried out on a R14000 SGI Fuel workstation using molecular modeling software package SYBYL V6.9 SYBYL V6.9. 20 All compounds were constructed in SYBYL based on the crystal structure of nor-BNI21 since these compounds may similarly bind to j opioid receptor. Considering the vital role of a basic group for j selectivity and antagonistic potency, the basic groups of all compounds were fixed near the protonated nitrogen atom of nor-BNI supposing they shared similar binding models to j opioid receptor. All compounds were protonated and assigned with Gasteiger–Hu¨ckel charges. For some more flexible compounds, systematic searches were performed with an interval of 10 on rotatory bonds to ensure their low energy conformations. Finally, they were minimized with Tripos force field.22 The most crucial step in performing CoMFA and CoMSIA is to determine the bioactive conformations of the compounds so that all compounds could be aligned together. Though nor-BNI was not employed to establish 3D-QSAR models here, its structure was selected as the template for structural alignment from the alignment facility in SYBYL, due to its selective j antagonistic potency and quite rigid structure. The final structural alignment is shown in Figure 2. 2.3. PLS analysis As usual, PLS (partial least squares) method was used to establish and validate CoMFA and CoMSIA models here. The binding affinity Ki values were converted into pKi values, to describe the biological activities. CoMFA was set at standard values, with a sp3 carbon atom with one positive charge used to probe steric and electrostatic fields. The standard cutoff value was set to 30 kcal/mol. CoMSIA fields were set in their default opinions. LOO (leave one out) cross-validation method was used to evaluate the initial models. The cross-validated coef- ficient q2 was calculated using the following equation: q2 ¼ 1:0 P cðcpred cactualÞ 2 P cðcactual cmeanÞ 2 where cpred, cactual, and cmean are predicted, actual, and mean values of the target property (pKi), respectively, and PRESS ¼ P cðcpred cactualÞ 2 is the sum of predictive sum of squares. The optimum number of components was then given, and CoMFA and CoMSIA models were hence derived corresponding to the optimum number. The parameters of confidence intervals were further estimated by bootstrap in 10 runs. The column filtering box was kept unchecked during all operations. 2.4. Validation of CoMFA and CoMSIA models In addition to LOO method to validate the CoMFA and CoMSIA models, a test set made up of eight compounds N H O O N OH OH OH HO N nor-BNI 1 HO O N OH N H N H NH2 NH GNTI 2 N OH CH3 JDTic H3C NH O H N HO 3 (-)-KAA1 OH N H3C HN N O 4 Figure 1. Some potent highly selective j antagonists reported recently. 602 W. Li et al. / Bioorg. Med. Chem. 14 (2006) 601–610
W. Li et al./ Bioorg. Med. Chem. 14(2006)601-610 Table 1. Structures and binding affinities in the training set and test set △ Ki(nM) 122 244 十++ 234568911 HHHHHHHHH 0.95±0.04 0.86±0.20 200000200022222222211 3.26±0.12 n.OH 即P 6.96±0.85 2.72±0.39 B4518902346 PM 2.38±0.37 HoooNN L57±0.80 0.68±0.30 H 0.29±0.10 0.30±0.20 iBu L.39±0.14 6.33±040 NH-l.Hex 8.13±2.67 44±0.0 n-Hept 5.6l±0.37 1243 000 191±3.9 Test set ocHH NO 61±045 161583 0202 B1HH 430 n-Pr 1.60±0.28
Table 1. Structures and binding affinities in the training set and test set Compound n R1 R2 Ki (nM) Training set 1 2 H H 1.42 ± 0.17 2 2 H Cl 2.41 ± 0.22 3 2 H NO2 2.14 ± 0.34 4 2 H NH2 0.95 ± 0.04 5 0 H H 0.86 ± 0.20 6 0 H Cl 0.66 ± 0.05 8 0 H NH2 0.63 ± 0.10 9 0 H OH 3.26 ± 0.12 10 0 H m-OH 2.74 ± 0.74 11 2 H H 0.49 ± 0.00 13 0 n-Bu n-Bu 6.96 ± 0.85 14 0 n-Pr n-Pr 2.72 ± 0.39 15 0 n-Pr CPM 2.38 ± 0.37 17 2 O Et 1.57 ± 0.80 18 2 O Pr 0.85 ± 0.40 19 2 O Bu 0.68 ± 0.30 20 2 NH Me 0.29 ± 0.10 22 2 NH Pr 0.25 ± 0.10 23 2 NH n-Bu 0.30 ± 0.20 24 2 NH i-Bu 1.39 ± 0.14 26 2 O NH–n-Bu 6.33 ± 0.40 27 2 O NH–n-Hex 8.13 ± 2.67 29 1 NH n-Pen 1.44 ± 0.04 30 1 NH n-Hept 5.61 ± 0.37 31 0 O n-Hept 21.89 ± 7.11 32 0 O Bn 10.33 ± 0.66 34 0 O (CH2)4Ph 6.18 ± 0.47 35 0 O p-MeO–Bn 2.11 ± 0.35 36 10.1 ± 0.65 38 CH3 H 183 ± 16 39 H Pr 191 ± 3.9 Test set 7 0 H NO2 1.61 ± 0.45 12 2 n-Bu n-Bu 4.80 ± 0.02 16 0 Bn CPM 3.91 ± 0.60 21 2 NH Et 0.28 ± 0.10 25 2 O NH–Et 12.32 ± 1.29 28 1 NH n-Pr 1.60 ± 0.28 33 0 O (CH2)2Ph 2.21 ± 0.35 37 H H 120 ± 9.6 W. Li et al. / Bioorg. Med. Chem. 14 (2006) 601–610 603
W. Li et al./ Bioorg. Med. Chem. 14(2006)601-610 2.5. Homology modeling of kappa opioid receptor The sequence of human kappa opioid receptor was re trieved from the Swiss Prot database(Accession N P41 145). 3The sequences of bovine rhodopsin and hu Pror k and 8 opioid receptors were obtained from Swiss- L,too,for sequence alignments(see Fig. 3). The crystal structure of bovine rhodopsin was retrieved from Protein Data Bank(PDB entry code 1F88),24 which served as the template to generate the structural of kappa opioid receptor. At first the 7 TM fra were constructed by mutating the corresponding m in the template into target residue in kappa receptor Residue Ala106 was inserted into the target structure The extracellular loop 2(EL2), connecting TM4 and TM5, was built on the basis of EL2 of rhodopsin. The 2. Superposition of 39 molecules including compounds in the other extra- and intra-cellular loop regions were built set and test set based on the template of nor-BNI, a potent and with loop search function of sYBYL/Biopolymer module ctive antagonist. (The structure of nor-BNI was removed for The N-and C-terminal regions were extended from the purposes of clarity. ransmembrane regions for 10 residues, not completely built.a disulfide bond was formed between the side was used for model validation. Similar to cross-validat chains of residues Cys131 and Cys210. After all done, ed g values of LOO method, the predictive performance adding all side chains and hydrogen atoms and loading of models on the test set was estimated by predictive Kollman All-Atom charges, the initial structure was values, which is expressed in the following equation energy minimized for 5000 steps with Kollman All-At predictive 2 SSD-press om force field. 25 SSD The protonated GNTi was docked into the minimized where SSD is the sum of squared deviation between the structure of kappa receptor manually, by putting the pK, values of test set molecules and PRESS is the sum of protonated nitrogen toward the side chain of residue squared deviations between the observed and the pre- Asp138 and the guanidine group close to the side chain dicted pKi values of Glu297. The whole complex structure was then min- bRo 35 WQFS hKOR 57 AIPV hMOK68⊥A⊥ h DOR 47 ALAI IL2 SLHGYEVEGPTG FEATLGGEIA LAIERYVVVCKPMSNFRFG-ENH 152 hKOR ISIDYYNMETS HPVKALDERTPLK 174 hMOR ISIDYYNMETS HPVKALDERTPRN 185 LMETWPFGEL Ls工 DYYNMETS IAVCHPVKALDERTPAK 164 EL2 Hairpin bOho 153 WSRYI PEGMQC-SCGIDYYTPHEET 211 hKOR 175 KVREDVDVIECSLQFPDDDXSWWD 234 186 TTKYRQG-S-IDCT'LTESHPTW hDOR 165 MAVTRPRDGA-VV-CMLQFPSPSW 工L3 bRno 212 KEAAAQQQESATTQKAEKEVTRMVIIMVIAFT hKoR 235 RLKSVRLLSG-SREKDRN TRLVLVVVAV 293 hMOR 243 SVRMLSG-SKEKDRNLRRITRMVLVVVAVE h DOR 221 LRSVRLLSG-SKEKDRS 280 bRo 272 328 hKOR 294 ILYA RCERDEC FPLKMRM 352 hMOR 302 RCEREFC 360 RCERQLCRKPCGRP 340 Sequence alignments of three subtypes of human opioid receptors with bovine rhodopsin(N- and C-terminals omitted). The among them were highlight- b(EL), and intra-cellular loop (IL)regions were labeled correspondingly. In transmembrane regions,identical mbrane(TM)extracellular in dark blue, while residues in opioid receptors analogous to those in rhodopsin were colored in red
was used for model validation. Similar to cross-validated q2 values of LOO method, the predictive performance of models on the test set was estimated by predictive r 2 values, which is expressed in the following equation: predictive r 2 ¼ SSD PRESS SSD where SSD is the sum of squared deviation between the pKi values of test set molecules and PRESS is the sum of squared deviations between the observed and the predicted pKi values. 2.5. Homology modeling of kappa opioid receptor The sequence of human kappa opioid receptor was retrieved from the SwissProt database (Accession No. P41145).23 The sequences of bovine rhodopsin and human l and d opioid receptors were obtained from SwissProt, too, for sequence alignments (see Fig. 3). The crystal structure of bovine rhodopsin was retrieved from Protein Data Bank (PDB entry code 1F88),24 which served as the template to generate the structural model of kappa opioid receptor. At first the 7 TM fragments were constructed by mutating the corresponding residue in the template into target residue in kappa receptor. Residue Ala106 was inserted into the target structure. The extracellular loop 2 (EL2), connecting TM4 and TM5, was built on the basis of EL2 of rhodopsin. The other extra- and intra-cellular loop regions were built with loop search function of SYBYL/Biopolymer module. The N- and C-terminal regions were extended from the transmembrane regions for 10 residues, not completely built. A disulfide bond was formed between the side chains of residues Cys131 and Cys210. After all done, adding all side chains and hydrogen atoms and loading Kollman All-Atom charges, the initial structure was energy minimized for 5000 steps with Kollman All-Atom force field.25 The protonated GNTI was docked into the minimized structure of kappa receptor manually, by putting the protonated nitrogen toward the side chain of residue Asp138 and the guanidine group close to the side chain of Glu297. The whole complex structure was then minFigure 2. Superposition of 39 molecules including compounds in the training set and test set based on the template of nor-BNI, a potent and j selective antagonist. (The structure of nor-BNI was removed for purposes of clarity.) Figure 3. Sequence alignments of three subtypes of human opioid receptors with bovine rhodopsin (N- and C-terminals omitted). The transmembrane (TM) extracellular loop (EL), and intra-cellular loop (IL) regions were labeled correspondingly. In transmembrane regions, identical residues among them were highlighted in dark blue, while residues in opioid receptors analogous to those in rhodopsin were colored in red. 604 W. Li et al. / Bioorg. Med. Chem. 14 (2006) 601–610
W. Li et al. Bioorg. Med. Chem. 14(2006)601-610 imized for 5000 steps again, with Tripos force field. charged carbon atom in the center in MMFF94 and Del The minimized GNTI-receptor complex structure was Re methods. Huckel calculation was questionable be- used for further analysis cause of wrong nitrogen charge assignment And one po- sitive ch harge were distributed among three nearby groups 2.6. QSAR coefficient contour maps with the central carbon little contribution to this charge in the Pullman method. However, Gasteiger-Huickel and CoMFA and CoMSIA results were visualized by st Dev" Gasteiger calculations led to different results, in which Coeff contours. The molecule-5 was visualized as the the positive charge spread along the whole guanidine reference structure. Both CoMFA and CoMSIA plots group. The carbon atom and three nearby groups made were contoured by actual values. All the molecules used almost equal contributions to this one positive charge for QSAR analysis were aligned onto the GNTI struc- Considering that the uniform charge distribution may ture in the GNTI-receptor complex, which led to the be mostly preferable for guanidine group, Gasteiger mapping of CoMSIa plots onto the bound receptor Huckel method was finally used in this study model. Key residues, which should account for K selec mmo ty and potency were hence recognized on the receptor 3. 2. CoMFA and CoMSIA models and validation The best predictions were obtained with CoMFA stan dard model(q-=0.693, N=4)and CoMSIA combined 3. Results and discussion model with all descriptors (-=0.617. N=4)Table 2) their predictive performance on the test was r=0.607 3. 1. Charge assignment of guanidine group by CoMFA and r=0.701 by CoMSIA, which indicated that the built 3D-QSAR models were reliable and able to Charge assignment is crucial to a successful QSAR anal- predict binding affinities of new derivatives accurately S, especially when molecules under investigation were CoMSIA models with different field combinations were positive charged or negative charged. In this study, also evaluated by Loo and test set methodologies and many compounds contained a guanidine group with hese models also showed good predictive values table +l charge, which was quite different from those men- 2), which indicated that the CoMsia models were less tioned in any other 3D-QSAR studies in the literature. affected by fields employed. The conventional fit values Different from other groups like-NH3, there are four on training set and prediction values on the test set made central atoms, rather than one or two atoms, to share by the two models are shown in Table 3. The relationshi the one positive charge. How to correctly assign the curves between observed values versus conventional fit one positive charge on each atom in guanidine group values (prediction values) on the training set( test set) is quite challenging. Many methods led to different re- are also displayed in Figure 5 sults, and different charge assignments led to different or even converse 3D-QSAR contours 3. 3. CoMFA and CoMSIA contours At first compounds with amino group in the study were set CoMFA and CoMSIA contour plots showed that there in their protonated type and fixed near the protonated was a large blue contour around the second basic group nitrogen atom of nor-BNI. Various methods were used (Fig 6a), which was in good agreement with previous re- uate rges such as Gasteiger, 26 ports, 10, and indicated that the positive-charged Gasteiger-Huickel, Del Re, 27 MMFF94, 28, 29 Huckel, 0 group in this region helped to increase binding affinity and Pullman methods( Fig 4). Based on different charge However, a red contour was also observed in CoMSIA ssignments on the guanidine group by various methods, contour plots(Fig. 6b), which brought complexity to the positive charges was focused on the central carbon this region. a detailed analysis on electrostatic, steric atom, and the nearby three groups(each group composed and hydrophobic(Fig. 6c) interactions of this kind of of one nitrogen atom and two hydrogen atoms)showed a compounds is discussed below in combination with the little negative charge to neutralize the highly positive K opioid receptor model. Unlike other fields describing asteiger-Huckel Del-Re MMFF94 Huckel Pullman C+0.384 +0286 1419 1.200-0.360+0.059 H+0.194 +0.261 +0.208+0.4500.000 +0.208 -0.285 -0.555-0967+04530.102 Figure 4. Different charge distributions of guanidine group calculated by multiple methods available in SYBYL. Carbon atoms were colored in gray. nitrogen atoms in blue, and hydrogen atoms in white
imized for 5000 steps again, with Tripos force field.22 The minimized GNTI–receptor complex structure was used for further analysis. 2.6. QSAR coefficient contour maps CoMFA and CoMSIA results were visualized by stDev* Coeff contours. The molecule-5 was visualized as the reference structure. Both CoMFA and CoMSIA plots were contoured by actual values. All the molecules used for QSAR analysis were aligned onto the GNTI structure in the GNTI–receptor complex, which led to the mapping of CoMSIA plots onto the bound receptor model. Key residues, which should account for j selectivity and potency were hence recognized on the receptor model. 3. Results and discussion 3.1. Charge assignment of guanidine group Charge assignment is crucial to a successful QSAR analysis, especially when molecules under investigation were positive charged or negative charged. In this study, many compounds contained a guanidine group with +1 charge, which was quite different from those mentioned in any other 3D-QSAR studies in the literature. Different from other groups like –NH3 +, there are four central atoms, rather than one or two atoms, to share the one positive charge. How to correctly assign the one positive charge on each atom in guanidine group is quite challenging. Many methods led to different results, and different charge assignments led to different or even converse 3D-QSAR contours. At first compounds with amino group in the study were set in their protonated type and fixed near the protonated nitrogen atom of nor-BNI. Various methods were used to calculate guanidine group charges such as Gasteiger,26 Gasteiger–Hu¨ckel, Del Re,27 MMFF94,28,29 Hu¨ckel,30 and Pullman31 methods (Fig. 4). Based on different charge assignments on the guanidine group by various methods, the positive charges was focused on the central carbon atom, and the nearby three groups (each group composed of one nitrogen atom and two hydrogen atoms) showed a little negative charge to neutralize the highly positive charged carbon atom in the center in MMFF94 and Del Re methods. Hu¨ckel calculation was questionable because of wrong nitrogen charge assignment. And one positive charge were distributed among three nearby groups with the central carbon little contribution to this charge in the Pullman method. However, Gasteiger–Hu¨ckel and Gasteiger calculations led to different results, in which the positive charge spread along the whole guanidine group. The carbon atom and three nearby groups made almost equal contributions to this one positive charge. Considering that the uniform charge distribution may be mostly preferable for guanidine group, Gasteiger– Hu¨ckel method was finally used in this study. 3.2. CoMFA and CoMSIA models and validation The best predictions were obtained with CoMFA standard model (q2 = 0.693, N = 4) and CoMSIA combined model with all descriptors (q2 = 0.617. N = 4) (Table 2); their predictive performance on the test was r 2 = 0.607 by CoMFA and r 2 = 0.701 by CoMSIA, which indicated that the built 3D-QSAR models were reliable and able to predict binding affinities of new derivatives accurately. CoMSIA models with different field combinations were also evaluated by LOO and test set methodologies and these models also showed good predictive values (Table 2), which indicated that the CoMSIA models were less affected by fields employed. The conventional fit values on training set and prediction values on the test set made by the two models are shown in Table 3. The relationship curves between observed values versus conventional fit values (prediction values) on the training set (test set) are also displayed in Figure 5. 3.3. CoMFA and CoMSIA contours CoMFA and CoMSIA contour plots showed that there was a large blue contour around the second basic group (Fig. 6a), which was in good agreement with previous reports8,10,11 and indicated that the positive-charged group in this region helped to increase binding affinity. However, a red contour was also observed in CoMSIA contour plots (Fig. 6b), which brought complexity to this region. A detailed analysis on electrostatic, steric, and hydrophobic (Fig. 6c) interactions of this kind of compounds is discussed below in combination with the j opioid receptor model. Unlike other fields describing Gasteiger Gasteiger-Hückel Del-Re MMFF94 Hückel Pullman C +0.384 +0.286 +1.419 +1.200 -0.360 +0.059 H +0.194 +0.261 +0.208 +0.450 0.000 +0.208 N -0.183 -0.285 -0.555 -0.967 +0.453 -0.102 Figure 4. Different charge distributions of guanidine group calculated by multiple methods available in SYBYL. Carbon atoms were colored in gray, nitrogen atoms in blue, and hydrogen atoms in white. W. Li et al. / Bioorg. Med. Chem. 14 (2006) 601–610 605
W. Li et al./ Bioorg. Med. Chem. 14(2006)601-610 Table 2. CoMFA and CoMSIA analysis on indolomorphinan K antagonists SEEks Pred. r 0.238 0.188 0.607 CoMSIA(steric 0.217 0.150 0.579 CoMSIA(steric 0.286 50.707 0.228 0.886 CoMSIA(steric hydrophobic donor) 0.6033 0.840 0.294 0.627 CoMSIA(steric +electro hydrophobic acceptor) 0.620 6 0.961 0.155 97.745 0.108 0.980 0.45l 0.9040.233 6.16 Pred CoMSIA)predictive g values on the test se Table 3. Actual versus conventional fit values(predicted values)activities of CoMFA(standard) and CoMSIA (all descriptors) models on the aining set(test set) Actual pk CoMFa CoMSIA Conventional fit.c Conventional fit Res Mo molecule. 8.848 8.837 0.011 8.943 -0.095 Molecule-2 8.618 0.20 -0.271 Molecule- 3 8.670 0.158 -0.249 Molecule-4 0.012 219 Molecule- 6 Molecule-& Molecule. 9 -0.393 0.238 Molecule. 10 8.834 8.673 -O111 Molecule. 11 9.310 9.315 9.155 0.155 Molecule- 13 8.157 l57 0.000 -0.264 8.446 0.119 8.481 Molecule- 15 8.596 0.02 0.114 Molecule.17 8.804 9.084 -0.280 8978 -0.174 Molecule- 18 -0.067 9.042 Molecule.19 9.214 -0.046 Molecule- 20 9.538 8.997 0.541 9.223 315 Molecule-2 9.266 9.39 Molecule- 23 9.359 9.460 Molecule- 24 8.857 9.156 04 Molecule- 26 0.02 8.175 0.024 Molecule- 27 8.031 0.059 7.962 0.128 Molecule- 29 8.842 8.645 0.133 Molecule.30 -0.307 0.420 Molecule.31 7.722 7.759 Molecule.32 0.415 8.172 -0.186 Molecule- 34 0.128 Molecule. 35 8.676 0.175 8.384 0.292 Molecule- 36 8.135 Molecule. 38 6.738 0.022 Molecule- 39 6.670 (b) Test set OMA b CoMSIA ctual pk Predicted pKi -0.150 8.547 0.246 8.677 -0.358 8.549 -0.142 0.218 1583 8.656 6.743 0.178 CoMSIA model combined with all descriptors cOnventional fitted value Difference between actual and fitted (predicted) pki values Predicted pKi valu
Table 3. Actual versus conventional fit values (predicted values) activities of CoMFA (standard) and CoMSIA (all descriptors) models on the training set (test set) Compound Actual pKi CoMFAa CoMSIAb Conventional fit.c Resd Conventional fitc Resd (a) Training set Molecule-1 8.848 8.837 0.011 8.943 0.095 Molecule-2 8.618 8.826 0.208 8.889 0.271 Molecule-3 8.670 8.828 0.158 8.919 0.249 Molecule-4 9.022 8.867 0.155 9.034 0.012 Molecule-5 9.066 8.847 0.219 8.796 0.270 Molecule-6 9.181 8.967 0.214 8.802 0.379 Molecule-8 9.201 8.947 0.254 8.922 0.279 Molecule-9 8.487 8.880 0.393 8.725 0.238 Molecule-10 8.562 8.834 0.272 8.673 0.111 Molecule-11 9.310 9.315 0.005 9.155 0.155 Molecule-13 8.157 8.157 0.000 8.421 0.264 Molecule-14 8.565 8.446 0.119 8.481 0.084 Molecule-15 8.623 8.596 0.027 8.509 0.114 Molecule-17 8.804 9.084 0.280 8.978 0.174 Molecule-18 9.071 9.138 0.067 9.042 0.029 Molecule-19 9.168 9.214 0.046 9.108 0.059 Molecule-20 9.538 8.997 0.541 9.223 0.315 Molecule-22 9.602 9.266 0.336 9.391 0.211 Molecule-23 9.523 9.359 0.164 9.460 0.063 Molecule-24 8.857 9.156 0.299 9.331 0.474 Molecule-26 8.199 8.221 0.022 8.175 0.024 Molecule-27 8.090 8.031 0.059 7.962 0.128 Molecule-29 8.842 8.645 0.197 8.709 0.133 Molecule-30 8.251 8.558 0.307 8.671 0.420 Molecule-31 7.660 7.722 0.062 7.759 0.099 Molecule-32 7.986 8.401 0.415 8.172 0.186 Molecule-34 8.209 8.218 0.009 8.081 0.128 Molecule-35 8.676 8.501 0.175 8.384 0.292 Molecule-36 7.996 8.100 0.104 8.135 0.139 Molecule-38 6.738 6.635 0.103 6.715 0.022 Molecule-39 6.719 6.642 0.077 6.670 0.049 (b) Test set CoMFAa CoMSIAb Compound Actual pKi Predicted pKi e Resd Predicted pKi e Resd Molecule-7 8.793 8.943 0.150 8.547 0.246 Molecule-12 8.319 8.659 0.340 8.677 0.358 Molecule-16 8.408 8.806 0.398 8.549 0.142 Molecule-21 9.553 9.180 0.373 9.335 0.218 Molecule-25 7.909 8.671 0.761 8.415 0.505 Molecule-28 8.796 8.537 0.259 8.675 0.121 Molecule-33 8.656 7.977 0.679 7.832 0.824 Molecule-37 6.921 6.630 0.291 6.743 0.178 a CoMFA standard model. b CoMSIA model combined with all descriptors. c Conventional fitted value. d Difference between actual and fitted (predicted) pKi values. e Predicted pKi value. Table 2. CoMFA and CoMSIA analysis on indolomorphinan j antagonists Model q2 N r2 SEE F SEEbs q2 bs Pred. r 2 CoMFA(std) 0.693 4 0.900 0.238 58.245 0.188 0.924 0.607 CoMSIA(steric + electro) 0.738 6 0.923 0.217 47.651 0.150 0.964 0.579 CoMSIA(steric + electro + hydrophobic) 0.591 3 0.849 0.286 50.707 0.228 0.886 0.603 CoMSIA(steric + electro + hydrophobic + donor) 0.603 3 0.840 0.294 47.372 0.234 0.895 0.627 CoMSIA(steric + electro + hydrophobic + acceptor) 0.620 6 0.961 0.155 97.745 0.108 0.980 0.451 CoMSIA(all descriptors) 0.617 4 0.904 0.233 61.168 0.198 0.886 0.701 q2 —leave one out (LOO) cross-validated correlation coefficient, N—optimum number of components, r 2 —noncross-validated correlation coefficient, SEE—standard error of estimate, F—F-test value, SEEbs—standard error of estimate by boot strapping analysis, q2 bs—mean r 2 by boot strapping analysis (in 10 runs), Pred. r 2 —CoMFA (CoMSIA) predictive q2 values on the test set. 606 W. Li et al. / Bioorg. Med. Chem. 14 (2006) 601–610
W. Li et al. Bioorg. Med. Chem. 14(2006)601-610 a10.0 b100 益90 7 606.57.07.58.08.5909.5 Actual pKi Figure 5. Plot of observed pki versus conventional fit predictions (predicted activities) of training set(a) and test set(b). Blue rhombs show tional fit(predictions) of CoMFA standard model and pink triangles show those of CoMsia (all descriptors)model compound binding requirements, H-bond fields describe tions with GNTi, were consistent with experimental evi- oroperties located on the receptor. In H-bond donor dence. In addition to the interactions reported in the field contours, areas colored in cyan where H-bond literature, two novel hydrogen bonds were identified acceptors on the receptor are predicted to favor binding in our model. The oxygen bridge between C4 and C5 and areas colored in purple where H-bond acceptors on in GNTI structure formed a hydrogen bond with tor field contours, it shows regions where H-bond do- the guanidine with Tyr312. Therefore, the GNTI-receen the receptor are predicted to reduce binding. For accep- nors on the receptor are predicted to enhance tor complex structure was a reasonable model magenta) and reduce(red)binding capacity. H-bond CoMSIA contours and identify the binding features of donor/acceptor contour maps(Fig. 6d and e) suggested ndolomorphinan derivatives that there should be one H-acceptor and one H-donor favored for binding in the receptor 3.5. Mapping COMSIA contours in the GNTI-receptor complex model 3. 4. Homology modeling of kappa opioid receptor and GNTI-receptor complex Based on the above 3D-QSAR models, the CoMSIA contours were further mapped into a K opioid receptor Currently there is only one crystal structure, that is, bo- model(Fig. 7a), the blue mesh(Fig. 7b)generated by ine rhodopsin, belonging to the superfamily of G-pro- CoMSIA, meant positive charge would be favorable ein coupled receptors (GPCR). Because the seven for binding affinity, was exactly located around nega transmembrane regions are highly conserved among tive-charged residue Glu297, which indicated the impor- the whole family, the structure of bovine rhodopsin tance of residue Glu297 in antagonist binding and was has been hence widely used as a template to model 3D consistent with experimental evidence. The correspond tructures of any other GPCR (for a recent review, ing residue of Glu297 in K subtype was Trp284 in 8 and please see Ref. 32). Excellent results, such as successful Lys305 in H subtypes, respectively, quite different among virtual screening, have already been obtained based on them. Therefore, Glu297 would not only contribute to rhodopsin-derived structural models. 33,34 Kappa opioid the binding affinity, but also to the selectivity. Besides it is reasonable to construct its structural model based idue Tyr 312 might get involved in antagonist binding, on the unique crystal structure of bovine rhodopsin. too. However, when this residue was replaced by ala nine, > the binding affinities of GNTI and nor-BNI to Because of the long sequence and importance of the k opioid receptor increased slightly, so it is hard to ad extracellular loop 2, it is difficult to model a reliable dress the role of Tyr312 upon antagonist binding exactly ructure by the loop search method. There was only here. one residue number difference between the two sequenc es,hence, the EL2 of rhodopsin was used as a template Meanwhile, the red meshes, meant negative to model the eL2 region of the receptor favorable to binding affinity, were observed to he positive-charged residue Lys 227(Fig. 7b) The binding site of GNTI was determined according uggested Lys227 plays a role in antagonist binding. experimental evidence, such as site-directed mutagenesis This result was further confirmed by H-bond acceptor sults. Because the supposed binding pocket of the contours(Fig. 6e) where one H-bond donor favored receptor was not enough to accommodate gnti, the li- for binding may exist in this area of the receptor obvi gand was docked into the binding pocket of the model ously, this H-bond donor refers to the residue Lys 227 manually, and some side chains of residues around Nevertheless, this residue is highly conserved among GNTI were adjusted to release more space for the li all the three opioid receptors, and the assumption that gand. After energy minimization, the ligand-receptor I naltrexone derived compounds bind to opioid recep- complex was formed. Binding residues including tors in similar modes was widely accepted. 3 > Com- Aspl38, Glu297, and Tyr312, which formed key interac- pounds interacting with this residue may also increase
compound binding requirements, H-bond fields describe properties located on the receptor. In H-bond donor field contours, areas colored in cyan where H-bond acceptors on the receptor are predicted to favor binding and areas colored in purple where H-bond acceptors on the receptor are predicted to reduce binding. For acceptor field contours, it shows regions where H-bond donors on the receptor are predicted to enhance (magenta) and reduce (red) binding capacity. H-bond donor/acceptor contour maps (Fig. 6d and e) suggested that there should be one H-acceptor and one H-donor favored for binding in the receptor. 3.4. Homology modeling of kappa opioid receptor and GNTI–receptor complex Currently there is only one crystal structure, that is, bovine rhodopsin, belonging to the superfamily of G-protein coupled receptors (GPCR). Because the seven transmembrane regions are highly conserved among the whole family, the structure of bovine rhodopsin has been hence widely used as a template to model 3D structures of any other GPCR (for a recent review, please see Ref. 32). Excellent results, such as successful virtual screening, have already been obtained based on rhodopsin-derived structural models.33,34 Kappa opioid receptor belongs to the superfamily of GPCR, therefore, it is reasonable to construct its structural model based on the unique crystal structure of bovine rhodopsin. Because of the long sequence and importance of the extracellular loop 2, it is difficult to model a reliable structure by the loop search method. There was only one residue number difference between the two sequences, hence, the EL2 of rhodopsin was used as a template to model the EL2 region of the receptor. The binding site of GNTI was determined according to experimental evidence, such as site-directed mutagenesis results. Because the supposed binding pocket of the receptor was not enough to accommodate GNTI, the ligand was docked into the binding pocket of the model manually, and some side chains of residues around GNTI were adjusted to release more space for the ligand. After energy minimization, the ligand–receptor complex was formed. Binding residues including Asp138, Glu297, and Tyr312, which formed key interactions with GNTI, were consistent with experimental evidence. In addition to the interactions reported in the literature,11 two novel hydrogen bonds were identified in our model. The oxygen bridge between C4 and C5 in GNTI structure formed a hydrogen bond with Tyr139. Another hydrogen bond was observed between the guanidine with Tyr312. Therefore, the GNTI–receptor complex structure was a reasonable model to map CoMSIA contours and identify the binding features of indolomorphinan derivatives. 3.5. Mapping CoMSIA contours in the GNTI–receptor complex model Based on the above 3D-QSAR models, the CoMSIA contours were further mapped into a j opioid receptor model (Fig. 7a), the blue mesh (Fig. 7b) generated by CoMSIA, meant positive charge would be favorable for binding affinity, was exactly located around negative-charged residue Glu297, which indicated the importance of residue Glu297 in antagonist binding and was consistent with experimental evidence. The corresponding residue of Glu297 in j subtype was Trp284 in d and Lys305 in l subtypes, respectively, quite different among them. Therefore, Glu297 would not only contribute to the binding affinity, but also to the selectivity. Besides, this blue region extended to Tyr312, indicating that residue Tyr312 might get involved in antagonist binding, too. However, when this residue was replaced by alanine,35 the binding affinities of GNTI and nor-BNI to j opioid receptor increased slightly, so it is hard to address the role of Tyr312 upon antagonist binding exactly here. Meanwhile, the red meshes, meant negative charge favorable to binding affinity, were observed to match the positive-charged residue Lys227 (Fig. 7b), which suggested Lys227 plays a role in antagonist binding. This result was further confirmed by H-bond acceptor contours (Fig. 6e) where one H-bond donor favored for binding may exist in this area of the receptor. Obviously, this H-bond donor refers to the residue Lys227. Nevertheless, this residue is highly conserved among all the three opioid receptors, and the assumption that all naltrexone derived compounds bind to opioid receptors in similar modes was widely accepted.35 Compounds interacting with this residue may also increase 6.0 7.0 8.0 9.0 10.0 6.0 7.0 8.0 9.0 10.0 Actual pKi Conventional fit 6.0 6.5 7.0 7.5 8.0 8.5 9.0 9.5 10.0 6.0 6.5 7.0 7.5 8.0 8.5 9.0 9.5 10.0 Actual pKi Predicted pKi a b Figure 5. Plot of observed pKi versus conventional fit predictions (predicted activities) of training set (a) and test set (b). Blue rhombs show conventional fit (predictions) of CoMFA standard model and pink triangles show those of CoMSIA (all descriptors) model. W. Li et al. / Bioorg. Med. Chem. 14 (2006) 601–610 607
W. Li et al./ Bioorg. Med. Chem. 14(2006)601-610 CoMFA CoMSIA b color Legend COMFA CoMSIA d color Legend enhance affinity, whereas blue contours predict positive charge enhance affinity; (b) gree edict bulky group enhance yellow contours predict less bulky group is favored for affinity; (c) yellow contours pred m predict hydrophilic group enhance affinity; (d) cyan contours predict H-bond acceptors enhance binding, whereas purple contours reduce binding:(e)magenta contours predict H-bond donors on the receptor enhance bi as red contours reduce binding
Figure 6. CoMFA (standard model) and CoMSIA (all descriptors model) stdev* coeff Contour plots: (a) red contours predict negative charge enhance affinity, whereas blue contours predict positive charge enhance affinity; (b) green contours predict bulky group enhance affinity, whereas yellow contours predict less bulky group is favored for affinity; (c) yellow contours predict hydrophobes enhance affinity, whereas white contours predict hydrophilic group enhance affinity; (d) cyan contours predict H-bond acceptors on the receptor enhance binding, whereas purple contours reduce binding; (e) magenta contours predict H-bond donors on the receptor enhance binding, whereas red contours reduce binding. 608 W. Li et al. / Bioorg. Med. Chem. 14 (2006) 601–610
W. Li et al. Bioorg. Med. Chem. 14(2006)601-610 Figure 7.(a) Structural aligned compounds were mapped to the modeled K opioid receptor-GNTI complex: (b) electrostatic contours: red mesh predict negative charge enhance affinity, whereas blue mesh predict positive charge enhance affinity; (c) steric contours: green mesh predict bulk favored for binding affinity, whereas yellow mesh predict bulky group reduce binding( d) hydrophobic contours: yellow mesh predict hydrophob group enhance binding, whereas white mesh predict hydrophilic group enhance binding (Nearby hydrophobic residues are colored in yellow and polar residues in white their binding affinity to other two opioid receptor extended along bi gorge. It was worthy to note Hence, it is reasonable to suppose that residue Lys22 hat one yellow ar ing less bulky for binding affin mainly contributes to the binding affinity but has noth ing to do with the selectivity et al. 3s pied near Glu297 and Tyr312. Just as Metzger explained in mutation studies on K opioid recep- tor, the replacement of the bulky Tyr3 12 with less bulky Some compounds(compounds 17-19)containing only alanine may actually facilitate binding neutral amide groups rather than basic groups also showed considerable k binding affinity and antagonist We also mapped hydrophobic contours(Fig. 7d)into potency. They displayed their antagonist potency in the K receptor. Polar residues around regions were col SIGTP,S assays by compound 17(Ke=0.48 nM), ored in white and hydrophobic residues in yellow compound 18(K=0.35 nM), and compound 19 Several hydrophilic areas were proposed where ontrol). 15 However, these compounds showed almost were present. Furthermore, Phe 214 and Pro215 of sec- no selectivity between k and 8, especially in antagonist ond extracellular loop(EL-2)may also account for potency assays in vitro. Therefore, it was obvious that hydrophobic interactions with antagonists in the K a basic group interacting with Glu297 was essential to the selectivity of K antagonist, and once again residue Lys227 had nothing to do with the selectivity. In addi- Combining all the above mapping information, it was tion, Black> proposed that the highly electronegative believed that, when modifying the 5-position of naltrin up may oup, better a small 8 receptor (identical to Lys227 in K receptor) by H- basic group toward Glu297 and Tyr3 12 would increase ond and thus increase their binding affinity to 8 recep- the binding affinity and K selectivity on the other side, tor, which was also in good agreement with our results. a big and hydrophobic fragment with partial negative charge would form hydrophobic interactions with resi In steric contour meshes(Fig. 7c), green ones, indicating dues Phe214 and Pro215 and hydrogen-bonding interac- that more bulky would be favorable for binding afinit tion with Lys 227
their binding affinity to other two opioid receptors. Hence, it is reasonable to suppose that residue Lys227 mainly contributes to the binding affinity but has nothing to do with the selectivity. Some compounds (compounds 17–19) containing only neutral amide groups rather than basic groups also showed considerable j binding affinity and antagonist potency. They displayed their antagonist potency in [ 35S]GTPcS assays by compound 17 (Ke = 0.48 nM), compound 18 (Ke = 0.35 nM), and compound 19 (Ke = 0.46 nM), respectively (nor-BNI Ke = 0.04 nM as control).15 However, these compounds showed almost no selectivity between j and d, especially in antagonist potency assays in vitro. Therefore, it was obvious that a basic group interacting with Glu297 was essential to the selectivity of j antagonist, and once again residue Lys227 had nothing to do with the selectivity. In addition, Black15 proposed that the highly electronegative oxygen atom in amide group may interact with Lys214 in d receptor (identical to Lys227 in j receptor) by Hbond and thus increase their binding affinity to d receptor, which was also in good agreement with our results. In steric contour meshes (Fig. 7c), green ones, indicating that more bulky would be favorable for binding affinity, extended along binding gorge. It was worthy to note that one yellow area, being less bulky for binding affinity, occupied near Glu297 and Tyr312. Just as Metzger et al.35 explained in mutation studies on j opioid receptor, the replacement of the bulky Tyr312 with less bulky alanine may actually facilitate binding. We also mapped hydrophobic contours (Fig. 7d) into the j receptor. Polar residues around regions were colored in white and hydrophobic residues in yellow. Several hydrophilic areas were proposed where Tyr312, Glu297, and space along the gorge tunnel were present. Furthermore, Phe214 and Pro215 of second extracellular loop (EL-2) may also account for hydrophobic interactions with antagonists in the j opioid receptor. Combining all the above mapping information, it was believed that, when modifying the 50 -position of naltrindole, a less bulky but hydrophilic group, better a small basic group toward Glu297 and Tyr312 would increase the binding affinity and j selectivity; on the other side, a big and hydrophobic fragment with partial negative charge would form hydrophobic interactions with residues Phe214 and Pro215 and hydrogen-bonding interaction with Lys227. Figure 7. (a) Structural aligned compounds were mapped to the modeled j opioid receptor–GNTI complex; (b) electrostatic contours: red mesh predict negative charge enhance affinity, whereas blue mesh predict positive charge enhance affinity; (c) steric contours: green mesh predict bulk favored for binding affinity, whereas yellow mesh predict bulky group reduce binding; (d) hydrophobic contours: yellow mesh predict hydrophobic group enhance binding, whereas white mesh predict hydrophilic group enhance binding. (Nearby hydrophobic residues are colored in yellow and polar residues in white.) W. Li et al. / Bioorg. Med. Chem. 14 (2006) 601–610 609
W. Li et al./ Bioorg. Med. Chem. 14(2006)601-610 4. Conclusion Rothman.R. B H. Dersch. C. M.: Cantrell. B. E. Zimmerman. D. M.: Carroll. F.L.J. Med Chem. 2003. 46 3D-QSAR models of indolomorphinan derivatives 3127 were obtained by CoMFA and CoMSIA methods 13. Carroll, I. Thomas, J. B; Dykstra, L. A: Granger, A.L Both of them showed good predictive capabilities for Allen, R. M. Howard, J. L: Pollard, G. T. Aceto M. D antagonist binding upon K opioid receptors. Based on Harris. L. S. Eur. J. Pharmacol. 2004. 501. 111 the results, CoMSIA contours were further mapped 14. Thomas. J. B. Atkinson. R. N: Namdev. N. Roth into the K opioid receptor-GNTI complex model and R. B: Gigstad, K. M.: Fix, S. E: Mascarella, S. W: Burgess, J. P. Vi several key residues were identified. The results demon Cantrell. B. E: Zimmerman. D.M.. Carroll. F. I.J. Med strated that a second basic group was essential to K Chem.2002,45,3524 selectivity and residue Glu297 was crucial for K antag- 15. Black. S. L: Jales. A.R.. Brandt. w onist potency and selectivity, whereas residue Lys227 Husbands. S. M.J. Med Chem. 2003. 46 of K opioid receptor was identified to contribute to 16. Black, S. L: Chauvignac, C; Grundt, P: Miller, C. N antagonists binding affinity rather than selectivity Wood, S: Traynor, J.R.; Lewis, J W; Husbands, S. M.J. due to its conservation among all three opioid recep- Med chen2003,46,5505. tors. At last a suggestion on how to modify the struc 17. Srivastava, S. K: Husbands, S. M. Aceto, M. D: Miller ture of indolomorphinan was C. N; Traynor, J.R., Lewis, J. W.J. Med. Chem. 2002 18. Grundt, P: Martinez- Bermejo, F. Lewis, J. W: Hus- bands. S. M.J. Med. Chem. 2003. 46. 3174 References and notes 19. Jones, R. M. Portoghese, P S. Eur J. Pharmacol. 2000, 1. Gilbert, P. E: Martin, W.R.J. Pharmacol. Exp. Ther. 20. SYBYL: Tripos Inc, 6.9 ed, 1699 Hanley Road, St Louis, 1976.19 MO63144. 2. Martin, W.R.: Eades, C. G. Thompson, J. A; Huppler, 21.Thecrystalstructureofnor-bniwasobtainedfromhttp:// E: Gilbert, P. E.J. Pharmacol. Exp. Ther. 1976, 197 umn.edu 22. Clark, M.: Cramer, R. D. I van Opdenbosch, N. J 3. Ackley, M. A: Hurley, R. w: Virnich, D. E; Hammond Compu.hem.1989,10,982 D.L.Pami2001,9l,377 23. Bairoch, A, Apweiler, R. Nucleic Acids Res. 2000, 28, 4. Fields. H. Nat. Reu. Neurosci. 2004. 5. 565 5. Vink, R: Portoghese, P. S: Faden, A. I. Am. J. Physiol. 4. Palczewski. K: Kumasaka. T: Hori. T: Behnke. C.A 991.261.R1527 Motoshima, H: Fox, B. A Le Trong. I: Teller. D. C. 6. Mague, S. D: Pliakas, A. M. Todtenkopf, M Okada, T; Stenkamp, R. E; Yamamoto, M. Miyano, M Tomasiewicz. H. C: Zha Stevens, W. C. Jr: Jor R. M Portoghese, P Carlezon. w. A. Jr. J 25. Hall, D. Pavitt, N. J. Comput. Chem. 1984, 5, 441 Pharmacol. Exp. Ther. 2003, 305, 323 6. Gasteiger, J: Marsili, M. Tetrahderon 1980, 36. 3219 7. Portoghese, P S: Lipkowski, A. W, Takemori, A. E Life Del re. G.J. Chem. Soc. 1958. 40 ci1987,40,1287 28. Halgren, T.A. J. Comput. Chem. 1996, 17, 490 8.Portoghese,P.s.;Nagase,H.;Takemori,A.e.j.Med.29.Halgren,T.A.j.compUt.Che.1996,17,553 Chem.1988,31,1344 30. Purcel, W, Singer, J. J. Chem. Eng. Data 1967, 12, E emori. A.E.: Portoghese. P. S.J. Med. Chem.1993,36,2412 31. Berthod. H. P A.J. hem. Phvs.1965,62,942. 10. Larson, D. L Jones, R. M., Hjorth, s.A., Schwartz, T. ller, D. C. Palczewski, K. arch W Portoghese, P S.J. Med. Chem. 2000, 43, 1573 Pharm. Ch c.2005,338,209 33. Evers, A: Klebe, G. Angew. Chem., Int. Ed. 2004. 43 Metzger, T.G.: Ferguson, D. M. Portoghese, P. S.J. Med chen2000,43,2759 34. Evers, E: Klabunde. T.J. Med. Chem. 2005, 48, 1088 12. Thomas, J. B Atkinson, R. N: Vinson, N. A: Catanz- 35. Metzger, T. G: Paterlini, M. G: Ferguson, D. M aro, J. L: Perretta, C. L: Fix, S. E: Mascarella, S. w Portoghese, P. S.J. Med. Chem. 2001, 44, 857
4. Conclusion 3D-QSAR models of indolomorphinan derivatives were obtained by CoMFA and CoMSIA methods. Both of them showed good predictive capabilities for antagonist binding upon j opioid receptors. Based on the results, CoMSIA contours were further mapped into the j opioid receptor–GNTI complex model and several key residues were identified. The results demonstrated that a second basic group was essential to j selectivity and residue Glu297 was crucial for j antagonist potency and selectivity, whereas residue Lys227 of j opioid receptor was identified to contribute to antagonists binding affinity rather than selectivity due to its conservation among all three opioid receptors. At last a suggestion on how to modify the structure of indolomorphinan was given. References and notes 1. Gilbert, P. E.; Martin, W. R. J. Pharmacol. Exp. Ther. 1976, 198, 66. 2. Martin, W. R.; Eades, C. G.; Thompson, J. A.; Huppler, R. E.; Gilbert, P. E. J. Pharmacol. Exp. Ther. 1976, 197, 517. 3. Ackley, M. A.; Hurley, R. W.; Virnich, D. E.; Hammond, D. L. Pain 2001, 91, 377. 4. Fields, H. Nat. Rev. Neurosci. 2004, 5, 565. 5. Vink, R.; Portoghese, P. S.; Faden, A. I. Am. J. Physiol. 1991, 261, R1527. 6. Mague, S. D.; Pliakas, A. M.; Todtenkopf, M. S.; Tomasiewicz, H. C.; Zhang, Y.; Stevens, W. C., Jr.; Jones, R. M.; Portoghese, P. S.; Carlezon, W. A., Jr. J. Pharmacol. Exp. Ther. 2003, 305, 323. 7. Portoghese, P. S.; Lipkowski, A. W.; Takemori, A. E. Life Sci. 1987, 40, 1287. 8. Portoghese, P. S.; Nagase, H.; Takemori, A. E. J. Med. Chem. 1988, 31, 1344. 9. Lin, C. E.; Takemori, A. E.; Portoghese, P. S. J. Med. Chem. 1993, 36, 2412. 10. Larson, D. L.; Jones, R. M.; Hjorth, S. A.; Schwartz, T. W.; Portoghese, P. S. J. Med. Chem. 2000, 43, 1573. 11. Stevens, W. C., Jr.; Jones, R. M.; Subramanian, G.; Metzger, T. G.; Ferguson, D. M.; Portoghese, P. S. J. Med. Chem. 2000, 43, 2759. 12. Thomas, J. B.; Atkinson, R. N.; Vinson, N. A.; Catanzaro, J. L.; Perretta, C. L.; Fix, S. E.; Mascarella, S. W.; Rothman, R. B.; Xu, H.; Dersch, C. M.; Cantrell, B. E.; Zimmerman, D. M.; Carroll, F. I. J. Med. Chem. 2003, 46, 3127. 13. Carroll, I.; Thomas, J. B.; Dykstra, L. A.; Granger, A. L.; Allen, R. M.; Howard, J. L.; Pollard, G. T.; Aceto, M. D.; Harris, L. S. Eur. J. Pharmacol. 2004, 501, 111. 14. Thomas, J. B.; Atkinson, R. N.; Namdev, N.; Rothman, R. B.; Gigstad, K. M.; Fix, S. E.; Mascarella, S. W.; Burgess, J. P.; Vinson, N. A.; Xu, H.; Dersch, C. M.; Cantrell, B. E.; Zimmerman, D. M.; Carroll, F. I. J. Med. Chem. 2002, 45, 3524. 15. Black, S. L.; Jales, A. R.; Brandt, W.; Lewis, J. W.; Husbands, S. M. J. Med. Chem. 2003, 46, 314. 16. Black, S. L.; Chauvignac, C.; Grundt, P.; Miller, C. N.; Wood, S.; Traynor, J. R.; Lewis, J. W.; Husbands, S. M. J. Med. Chem. 2003, 46, 5505. 17. Srivastava, S. K.; Husbands, S. M.; Aceto, M. D.; Miller, C. N.; Traynor, J. R.; Lewis, J. W. J. Med. Chem. 2002, 45, 537. 18. Grundt, P.; Martinez-Bermejo, F.; Lewis, J. W.; Husbands, S. M. J. Med. Chem. 2003, 46, 3174. 19. Jones, R. M.; Portoghese, P. S. Eur. J. Pharmacol. 2000, 396, 49. 20. SYBYL: Tripos Inc, 6.9 ed., 1699 Hanley Road, St. Louis, MO 63144. 21. The crystal structure of nor-BNI was obtained from http:// www.opioid.umn.edu. 22. Clark, M.; Cramer, R. D. I.; van Opdenbosch, N. J. Comput. Chem. 1989, 10, 982. 23. Bairoch, A.; Apweiler, R. Nucleic Acids Res. 2000, 28, 45. 24. Palczewski, K.; Kumasaka, T.; Hori, T.; Behnke, C. A.; Motoshima, H.; Fox, B. A.; Le Trong, I.; Teller, D. C.; Okada, T.; Stenkamp, R. E.; Yamamoto, M.; Miyano, M. Science 2000, 289, 739. 25. Hall, D.; Pavitt, N. J. Comput. Chem. 1984, 5, 441. 26. Gasteiger, J.; Marsili, M. Tetrahderon 1980, 36, 3219. 27. Del Re, G. J. Chem. Soc. 1958, 4031. 28. Halgren, T. A. J. Comput. Chem. 1996, 17, 490. 29. Halgren, T. A. J. Comput. Chem. 1996, 17, 553. 30. Purcel, W.; Singer, J. J. Chem. Eng. Data 1967, 12, 235. 31. Berthod, H.; Pullman, A. J. Chem. Phys. 1965, 62, 942. 32. Stenkamp, R. E.; Teller, D. C.; Palczewski, K. Arch. Pharm. Chem. Life Sci. 2005, 338, 209. 33. Evers, A.; Klebe, G. Angew. Chem., Int. Ed. 2004, 43, 248. 34. Evers, E.; Klabunde, T. J. Med. Chem. 2005, 48, 1088. 35. Metzger, T. G.; Paterlini, M. G.; Ferguson, D. M.; Portoghese, P. S. J. Med. Chem. 2001, 44, 857. 610 W. Li et al. / Bioorg. Med. Chem. 14 (2006) 601–610
