ISSN 16076729, Doklady Biochemistry and Biophysics, 2014, Vol. 454, pp. 13–16. © Pleiades Publishing, Ltd., 2014. Original Russian Text © E.V. Radchenko, D.S. Karlov, V.A. Palyulin, N.S. Zefirov, 2014, published in Doklady Akademii Nauk, 2014, Vol. 454, No. 3, pp. 351–354.
BIOCHEMISTRY, BIOPHYSICS AND MOLECULAR BIOLOGY
Molecular Modeling of the Transmembrane Domain of mGluR2 Metabotropic Glutamate Receptor and the Binding Site of Its Positive Allosteric Modulators E. V. Radchenkoa, b, D. S. Karlova, b, V. A. Palyulina, b, and Academician N. S. Zefirova, b Received August 19, 2013
DOI: 10.1134/S1607672914010050
brane domain, based on only one template structure, were described in the literature (for example, see [4]), and data on the detailed structure of the binding sites of their modulators are missing in the literature. The goals of this work were to build a model of the 3D structure of the transmembrane domain of mGluR2, model the binding site of its positive allos teric modulators and the corresponding pharmacoph ore, perform their mutual validation, and develop approaches to targeted design of and search for new active compounds. To ensure maximum reliability of the 3D model of the 7TM domain of mGluR2 (sequence code UniProtQ14416, residues 562–842), during its construc tion by homology modeling using the MODELLER 9.8 software [5], the alignment of several template pro teins (PDB codes 1U19, 1F88, 3QAK, 3EML, 2Y00, 3ODU, 2RH1, 3PBl) was performed by using the ClustalX software [6] and BLOSUM62 homology matrices, taking into account the predictions of the secondary structure and transmembrane topology made by PSIPRED and MEMSAT programs [7]. After the molecular mechanics optimization of the model, its stereochemical quality was assessed by using Ramachandran plots. On the basis of published data [8], a representative set of positive allosteric modulators of the mGluR2 receptor, including derivatives of cyanopyridone, ace tophenone, benzimidazole, indanone, and pyrimidyl methylaniline was prepared. To determine the PAM binding site, docking of the structure of the highly active compound 1 into the receptor model was per formed using the AutoDock 4.2 software [9], taking into account the available data on the location of this site between the 3rd, 4th, and 5th transmembrane helices near the amino acid residues Ser688, Gly689, Ala733, and Asn735 [3]. For the preliminary structure of the complex, molecular dynamics simulation in the phospholipid bilayer with an aqueous environment (125 molecules of dipalmitoylphosphatidylcholine and ~6000 molecules of TIP3P model water; the total
Glutamic acid is a key excitatory neurotransmitter in the mammalian central nervous system, which can act by two main mechanisms associated with two groups of membrane receptors. Fast synaptic trans mission is mediated by the glutamatedependent cat ion channels (ionotropic glutamate receptors), whereas the metabotropic glutamate receptors (mGluR), belonging to the class of G proteincoupled receptors (GPCRs), are responsible for a “slow” neu romodulation through the regulation of intracellular metabolic processes [1]. These receptors contain a large extracellular Nterminal VFT domain, where the endogenous agonist, glutamate, is bound. Glutamate induced conformational changes are transmitted through the cysteinerich domain (CRD) and then through the transmembrane 7TM domain, formed by seven αhelices, to the intracellular Cterminal domain, thereby activating the second messenger sig naling pathways. The transmembrane domain contains the binding sites for small molecules that can enhance (positive allosteric modulators, PAM) or weaken (negative allosteric modulators, NAM) the receptor function. The allosteric modulators are active even against the monomeric form of the complete receptor or an iso lated 7TM domain, whereas the agonist causes response only as a result of interaction with the dimeric form [2]. Positive modulators of metabotropic glutamate receptor subtypes mGluR2 and mGluR3 may become a basis for creating effective and selective drugs for the treatment of serious diseases of the cen tral nervous system, such as schizophrenia, anxiety disorders, drug addiction, neurodegenerative diseases, etc. [3]. However, despite the great importance of this receptor, only a few attempts to model its transmem a
Department of Chemistry, Moscow State University, Moscow, 119991 Russia b Moscow Institute of Physics and Technology, Dolgoprudnyi, Moscow oblast, 141700 Russia 13
14
RADCHENKO et al.
number of atoms in the system ~40 000) was per formed using the GROMACS software [10]. After the initial relaxation with the imposition of certain con straints (~1.5 ns), simulation without constraints was performed for 10 ns. In the first 3 ns, the structure underwent certain rearrangements (mostly in the parts
distant from the binding site), after which it was stabi lized. The resulting model of the transmembrane domain of the receptor in the complex with the mod ulator was used for further analysis. The general view of the model and the location of the modulator in the binding site are shown in Fig. 1.
N
N F
O
F
N
1
N
N
N
N
N F
Cl 3
N
F
F
Then, the suitability of the PAM binding site struc ture obtained as a result of molecular dynamics simu lation for the search for active compounds by virtual screening was analyzed. For this purpose, a library of lowenergy conformations of 111 active ligands was created using the Omega 2.5.1.4 software [11] taking into account their protonation at pH 7.4 [12] and par
O
F 2
N N
N O S O F 4
N
F O F
O
tial atomic charges determined by the MMFF94 method. The set of decoys (conditionally inactive compounds) [13] included 5000 compounds from the ZINC database [14], which were close to the active compounds by their physicochemical characteristics but strongly differed from them in structure. Further formation of the library of 3D structures was per formed using the same approach as for the active com pounds. On average, the conformational ensemble for each molecule of active compounds and decoys included several hundred conformations. The model of the binding site of positive allosteric modulators (cavity volume, 1044 Å3) was built on the basis of the structure of the receptor–ligand complex obtained by molecular dynamics simulation using the Make Receptor 3.0.1 software [11]. In addition, we considered model variants with the additional require ments for the presence of a hydrophobic substituent in the ligand, a hydrogen bond between Ser727 and the hydrogen bond acceptor in the ligand, or both these conditions. The general view of the binding site model with the requirement for the presence of a hydropho bic substituent is shown in Fig. 2.
Fig. 1. General structure of the 7TM domain of the mGluR2 receptor and the location of the molecule of its positive allosteric modulator 1 (spacefilled model) in the binding site. The extracellular and intracellular portions are oriented upward and downward, respectively.
To validate the binding site models, we performed the docking of libraries of active compounds and decoys with the use of different scoring functions by using the FRED 2.2.5 software [11]. The quality of the models was estimated by the area under the ROC curve (AUROC), which characterizes the overall qual ity of the recognition of active and inactive com pounds, and the factors of enrichment of the library with active compounds (EF) depending on the relative size of the sample, calculated using the VSstat software developed by us. The optimal recognition quality was ensured by the Shapegauss scoring function and binding site model with the requirement for a hydrophobic sub stituent (AUROC = 0.83, EF0.5% = 24.5, EF2% = 11.1). As
DOKLADY BIOCHEMISTRY AND BIOPHYSICS
Vol. 454
2014
MOLECULAR MODELING OF THE TRANSMEMBRANE DOMAIN P 0.008
15 EF 25 20
0.006
15 0.004
His723
10 0.002
0 −800
Ser727 Gly689
Ser688
Ser731
5
−600 −400 Shapegauss
0 −200
Fig. 3. Distribution density of values of scoring functions (P) for the active compounds (thin solid line) and decoys (dashed line) and the dependence of the enrichment factor EF on the threshold value of the scoring function (solid line) for the Shapegauss function (for docking into the binding site model with the requirement for the presence of a hydrophobic substituent).
Fig. 2. General view of the model of the binding site of pos itive modulators of mGluR2 receptor with the requirement for the presence of a hydrophobic substituent. The contour of the allowed region (binding cavity) is shown with light gray mesh; the contour of the obligatory region is shown with dark gray mesh; the region of the requirement for a hydrophobic substituent is indicated with a gray ball. The ligand 1 molecule and some of the key amino acid residues in the binding site (stick model) are shown.
can be seen in Fig. 3, the plots of the distribution den sity of active compounds and decoys, this function has a wide region where only active compounds are present (the threshold value of the scoring function equal to –600 can be regarded optimal for their reli able detection). The pharmacophore model of the binding site (vROCS 3.1.2 [11]) was obtained by the automated analysis of the structures of compounds 2–4 aligned on the basis of docking results, with subsequent man ual modification to simplify the model and expand its range of applicability. In addition to the molecular shape characteristics, it also includes the centers of cyclic hydrophobic systems and hydrogen bond accep tors (Fig. 4). On the basis of the results of model valida tion using the sets of active compounds and decoys and stability tests, the optimal scoring function is FitColorTversky, which is based on the correspondence of properties of pharmacophore groups (AUROC = 0.61, EF0.5% = 24.5, EF2% = 10.1). For the purposes of virtual screening of potentially active compounds, the applicability of the models to new classes of compounds is of great importance. To verify it, a set of 168 benzotriazole derivatives—posi DOKLADY BIOCHEMISTRY AND BIOPHYSICS
Vol. 454
Fig. 4. Pharmacophore model of positive modulators of mGluR2 receptor. The structures of the reference com pounds and the shape of a sterically favorable area are shown. The black balls mark the positions of the centers of cyclic systems, and the gray mesh balls show the positions of the hydrogen bond acceptors.
tive allosteric modulators of mGluR2 receptor that were not used in the construction of models—was pre pared on the basis of published data [15]. The activity of compounds (EC50) varied in the range from 2 to 14 000 nM. In further analysis, we considered the log arithmic values pEC50 = log (1/EC50). The library of 3D structures was formed using the above procedure. The average number of conformations in the ensemble per one molecule was ~40. According to the docking results, the threshold value Shapegauss = –600, indeed, made it possible to identify highly active compound 5 and 6; however, for the given set, this condition is too restrictive. The 2014
16
RADCHENKO et al.
transition to the qualitative scale of activity with a threshold value pEC50 > 7.5 makes it possible to cal culate the summary parameters of virtual screening quality: AUROC = 0.74, the enrichment factors after the extraction of 2 and 10% of the set EF = 2.4 and 2.3, respectively. When screening was performed
Br O
N O S
N
O
N
using the pharmacophore model, these parameters were somewhat worse. Nevertheless, the considered models provide satisfactory virtual screening results even for such small test sets of structurally different compounds, which allows their use in search for new classes of ligands.
H
O
N N
N
Br
O
N N
O
H
N
N
N
N
F Cl
5
ACKNOWLEDGMENTS We are grateful to the OpenEye and ChemAxon companies for kindly providing licenses for the molec ular modeling software. This study was supported by the Ministry of Education and Science of the Russian Federation (state contract no. 14.514.11.4069). REFERENCES 1. Gregory, K.J. and Conn, P.J., in Encyclopedia of Bio logical Chemistry, New York: Academic, 2013, pp. 395– 398. 2. El Moustaine, D., Granier, S., Doumazane, E., et al., Proc. Nat. Acad. Sci. U.S.A., 2012, vol. 109, no. 40, pp. 16342–16347. 3. Gregory, K.J., Noetzel, M.J., and Niswender, C.M., Progr. Mol. Biol. Trans. Sci., 2013, vol. 115, pp. 61–121.
4. Bruno, A., Costantino, G., de Fabritiis, G., et al., PLoS ONE, 2012, vol. 7, no. 8, e42023. 5. S ali, A. and Blundell, T.L., J. Mol. Biol., 1993, vol. 234, no. 3, pp. 779–815. 6. Larkin, M.A., Blackshields, G., Brown, N.P., et al., Bioinformatics, 2007, vol. 23, no. 21, pp. 2947–2948. 7. Buchan, D.W., Ward, S.M., Lobley, A.E., et al., Nucleic Acids Res., 2010, vol. 38, pp. W563–W568. 8. Fraley, M.E., Expert Opin. Ther. Pat., 2009, vol. 19, no. 9, pp. 1259–1275. 9. Morris, G.M., Huey, R., Lindstrom, W., et al., J. Com put. Chem., 2009, vol. 30, no. 16, pp. 2785–2791. 10. Hess, B., Kutzner, C., van der Spoel, D., and Lindahl, E., J. Chem. Theory Comput., 2008, vol. 4, no. 3, pp. 435– 447. 11. OpenEye Scientific Software, 2009–2013. www.eyesopen. com 12. ChemAxon, 2013. www.chemaxon.com 13. Huang, N., Shoichet, B., and Irwin, J., J. Med. Chem., 2006, vol. 49, no. 23, pp. 6789–6801. 14. Irwin, J.J., Sterling, T., Mysinger, M.M., et al., J. Chem. Inf. Model., 2012, vol. 52, no. 7, pp. 1757– 1768. 15. Trabanco, A.A. and Cid, J.M., Expert Opin. Ther. Pat., 2013, vol. 23, no. 5, pp. 629–647. ˆ
Thus, we constructed the models of the transmem brane domain of the mGluR2 receptor and the bind ing site of its positive allosteric modulators and opti mized them by molecular dynamics simulation. These models can be used for a more thorough analysis of the structure and mechanism of functioning of the recep tor as well as for searching and targeted design of new promising ligands.
6
Translated by M. Batrukova
DOKLADY BIOCHEMISTRY AND BIOPHYSICS
Vol. 454
2014
BIOCHEMISTRY, BIOPHYSICS AND MOLECULAR BIOLOGY
Molecular Modeling of the Transmembrane Domain of mGluR2 Metabotropic Glutamate Receptor and the Binding Site of Its Positive Allosteric Modulators E. V. Radchenkoa, b, D. S. Karlova, b, V. A. Palyulina, b, and Academician N. S. Zefirova, b Received August 19, 2013
DOI: 10.1134/S1607672914010050
brane domain, based on only one template structure, were described in the literature (for example, see [4]), and data on the detailed structure of the binding sites of their modulators are missing in the literature. The goals of this work were to build a model of the 3D structure of the transmembrane domain of mGluR2, model the binding site of its positive allos teric modulators and the corresponding pharmacoph ore, perform their mutual validation, and develop approaches to targeted design of and search for new active compounds. To ensure maximum reliability of the 3D model of the 7TM domain of mGluR2 (sequence code UniProtQ14416, residues 562–842), during its construc tion by homology modeling using the MODELLER 9.8 software [5], the alignment of several template pro teins (PDB codes 1U19, 1F88, 3QAK, 3EML, 2Y00, 3ODU, 2RH1, 3PBl) was performed by using the ClustalX software [6] and BLOSUM62 homology matrices, taking into account the predictions of the secondary structure and transmembrane topology made by PSIPRED and MEMSAT programs [7]. After the molecular mechanics optimization of the model, its stereochemical quality was assessed by using Ramachandran plots. On the basis of published data [8], a representative set of positive allosteric modulators of the mGluR2 receptor, including derivatives of cyanopyridone, ace tophenone, benzimidazole, indanone, and pyrimidyl methylaniline was prepared. To determine the PAM binding site, docking of the structure of the highly active compound 1 into the receptor model was per formed using the AutoDock 4.2 software [9], taking into account the available data on the location of this site between the 3rd, 4th, and 5th transmembrane helices near the amino acid residues Ser688, Gly689, Ala733, and Asn735 [3]. For the preliminary structure of the complex, molecular dynamics simulation in the phospholipid bilayer with an aqueous environment (125 molecules of dipalmitoylphosphatidylcholine and ~6000 molecules of TIP3P model water; the total
Glutamic acid is a key excitatory neurotransmitter in the mammalian central nervous system, which can act by two main mechanisms associated with two groups of membrane receptors. Fast synaptic trans mission is mediated by the glutamatedependent cat ion channels (ionotropic glutamate receptors), whereas the metabotropic glutamate receptors (mGluR), belonging to the class of G proteincoupled receptors (GPCRs), are responsible for a “slow” neu romodulation through the regulation of intracellular metabolic processes [1]. These receptors contain a large extracellular Nterminal VFT domain, where the endogenous agonist, glutamate, is bound. Glutamate induced conformational changes are transmitted through the cysteinerich domain (CRD) and then through the transmembrane 7TM domain, formed by seven αhelices, to the intracellular Cterminal domain, thereby activating the second messenger sig naling pathways. The transmembrane domain contains the binding sites for small molecules that can enhance (positive allosteric modulators, PAM) or weaken (negative allosteric modulators, NAM) the receptor function. The allosteric modulators are active even against the monomeric form of the complete receptor or an iso lated 7TM domain, whereas the agonist causes response only as a result of interaction with the dimeric form [2]. Positive modulators of metabotropic glutamate receptor subtypes mGluR2 and mGluR3 may become a basis for creating effective and selective drugs for the treatment of serious diseases of the cen tral nervous system, such as schizophrenia, anxiety disorders, drug addiction, neurodegenerative diseases, etc. [3]. However, despite the great importance of this receptor, only a few attempts to model its transmem a
Department of Chemistry, Moscow State University, Moscow, 119991 Russia b Moscow Institute of Physics and Technology, Dolgoprudnyi, Moscow oblast, 141700 Russia 13
14
RADCHENKO et al.
number of atoms in the system ~40 000) was per formed using the GROMACS software [10]. After the initial relaxation with the imposition of certain con straints (~1.5 ns), simulation without constraints was performed for 10 ns. In the first 3 ns, the structure underwent certain rearrangements (mostly in the parts
distant from the binding site), after which it was stabi lized. The resulting model of the transmembrane domain of the receptor in the complex with the mod ulator was used for further analysis. The general view of the model and the location of the modulator in the binding site are shown in Fig. 1.
N
N F
O
F
N
1
N
N
N
N
N F
Cl 3
N
F
F
Then, the suitability of the PAM binding site struc ture obtained as a result of molecular dynamics simu lation for the search for active compounds by virtual screening was analyzed. For this purpose, a library of lowenergy conformations of 111 active ligands was created using the Omega 2.5.1.4 software [11] taking into account their protonation at pH 7.4 [12] and par
O
F 2
N N
N O S O F 4
N
F O F
O
tial atomic charges determined by the MMFF94 method. The set of decoys (conditionally inactive compounds) [13] included 5000 compounds from the ZINC database [14], which were close to the active compounds by their physicochemical characteristics but strongly differed from them in structure. Further formation of the library of 3D structures was per formed using the same approach as for the active com pounds. On average, the conformational ensemble for each molecule of active compounds and decoys included several hundred conformations. The model of the binding site of positive allosteric modulators (cavity volume, 1044 Å3) was built on the basis of the structure of the receptor–ligand complex obtained by molecular dynamics simulation using the Make Receptor 3.0.1 software [11]. In addition, we considered model variants with the additional require ments for the presence of a hydrophobic substituent in the ligand, a hydrogen bond between Ser727 and the hydrogen bond acceptor in the ligand, or both these conditions. The general view of the binding site model with the requirement for the presence of a hydropho bic substituent is shown in Fig. 2.
Fig. 1. General structure of the 7TM domain of the mGluR2 receptor and the location of the molecule of its positive allosteric modulator 1 (spacefilled model) in the binding site. The extracellular and intracellular portions are oriented upward and downward, respectively.
To validate the binding site models, we performed the docking of libraries of active compounds and decoys with the use of different scoring functions by using the FRED 2.2.5 software [11]. The quality of the models was estimated by the area under the ROC curve (AUROC), which characterizes the overall qual ity of the recognition of active and inactive com pounds, and the factors of enrichment of the library with active compounds (EF) depending on the relative size of the sample, calculated using the VSstat software developed by us. The optimal recognition quality was ensured by the Shapegauss scoring function and binding site model with the requirement for a hydrophobic sub stituent (AUROC = 0.83, EF0.5% = 24.5, EF2% = 11.1). As
DOKLADY BIOCHEMISTRY AND BIOPHYSICS
Vol. 454
2014
MOLECULAR MODELING OF THE TRANSMEMBRANE DOMAIN P 0.008
15 EF 25 20
0.006
15 0.004
His723
10 0.002
0 −800
Ser727 Gly689
Ser688
Ser731
5
−600 −400 Shapegauss
0 −200
Fig. 3. Distribution density of values of scoring functions (P) for the active compounds (thin solid line) and decoys (dashed line) and the dependence of the enrichment factor EF on the threshold value of the scoring function (solid line) for the Shapegauss function (for docking into the binding site model with the requirement for the presence of a hydrophobic substituent).
Fig. 2. General view of the model of the binding site of pos itive modulators of mGluR2 receptor with the requirement for the presence of a hydrophobic substituent. The contour of the allowed region (binding cavity) is shown with light gray mesh; the contour of the obligatory region is shown with dark gray mesh; the region of the requirement for a hydrophobic substituent is indicated with a gray ball. The ligand 1 molecule and some of the key amino acid residues in the binding site (stick model) are shown.
can be seen in Fig. 3, the plots of the distribution den sity of active compounds and decoys, this function has a wide region where only active compounds are present (the threshold value of the scoring function equal to –600 can be regarded optimal for their reli able detection). The pharmacophore model of the binding site (vROCS 3.1.2 [11]) was obtained by the automated analysis of the structures of compounds 2–4 aligned on the basis of docking results, with subsequent man ual modification to simplify the model and expand its range of applicability. In addition to the molecular shape characteristics, it also includes the centers of cyclic hydrophobic systems and hydrogen bond accep tors (Fig. 4). On the basis of the results of model valida tion using the sets of active compounds and decoys and stability tests, the optimal scoring function is FitColorTversky, which is based on the correspondence of properties of pharmacophore groups (AUROC = 0.61, EF0.5% = 24.5, EF2% = 10.1). For the purposes of virtual screening of potentially active compounds, the applicability of the models to new classes of compounds is of great importance. To verify it, a set of 168 benzotriazole derivatives—posi DOKLADY BIOCHEMISTRY AND BIOPHYSICS
Vol. 454
Fig. 4. Pharmacophore model of positive modulators of mGluR2 receptor. The structures of the reference com pounds and the shape of a sterically favorable area are shown. The black balls mark the positions of the centers of cyclic systems, and the gray mesh balls show the positions of the hydrogen bond acceptors.
tive allosteric modulators of mGluR2 receptor that were not used in the construction of models—was pre pared on the basis of published data [15]. The activity of compounds (EC50) varied in the range from 2 to 14 000 nM. In further analysis, we considered the log arithmic values pEC50 = log (1/EC50). The library of 3D structures was formed using the above procedure. The average number of conformations in the ensemble per one molecule was ~40. According to the docking results, the threshold value Shapegauss = –600, indeed, made it possible to identify highly active compound 5 and 6; however, for the given set, this condition is too restrictive. The 2014
16
RADCHENKO et al.
transition to the qualitative scale of activity with a threshold value pEC50 > 7.5 makes it possible to cal culate the summary parameters of virtual screening quality: AUROC = 0.74, the enrichment factors after the extraction of 2 and 10% of the set EF = 2.4 and 2.3, respectively. When screening was performed
Br O
N O S
N
O
N
using the pharmacophore model, these parameters were somewhat worse. Nevertheless, the considered models provide satisfactory virtual screening results even for such small test sets of structurally different compounds, which allows their use in search for new classes of ligands.
H
O
N N
N
Br
O
N N
O
H
N
N
N
N
F Cl
5
ACKNOWLEDGMENTS We are grateful to the OpenEye and ChemAxon companies for kindly providing licenses for the molec ular modeling software. This study was supported by the Ministry of Education and Science of the Russian Federation (state contract no. 14.514.11.4069). REFERENCES 1. Gregory, K.J. and Conn, P.J., in Encyclopedia of Bio logical Chemistry, New York: Academic, 2013, pp. 395– 398. 2. El Moustaine, D., Granier, S., Doumazane, E., et al., Proc. Nat. Acad. Sci. U.S.A., 2012, vol. 109, no. 40, pp. 16342–16347. 3. Gregory, K.J., Noetzel, M.J., and Niswender, C.M., Progr. Mol. Biol. Trans. Sci., 2013, vol. 115, pp. 61–121.
4. Bruno, A., Costantino, G., de Fabritiis, G., et al., PLoS ONE, 2012, vol. 7, no. 8, e42023. 5. S ali, A. and Blundell, T.L., J. Mol. Biol., 1993, vol. 234, no. 3, pp. 779–815. 6. Larkin, M.A., Blackshields, G., Brown, N.P., et al., Bioinformatics, 2007, vol. 23, no. 21, pp. 2947–2948. 7. Buchan, D.W., Ward, S.M., Lobley, A.E., et al., Nucleic Acids Res., 2010, vol. 38, pp. W563–W568. 8. Fraley, M.E., Expert Opin. Ther. Pat., 2009, vol. 19, no. 9, pp. 1259–1275. 9. Morris, G.M., Huey, R., Lindstrom, W., et al., J. Com put. Chem., 2009, vol. 30, no. 16, pp. 2785–2791. 10. Hess, B., Kutzner, C., van der Spoel, D., and Lindahl, E., J. Chem. Theory Comput., 2008, vol. 4, no. 3, pp. 435– 447. 11. OpenEye Scientific Software, 2009–2013. www.eyesopen. com 12. ChemAxon, 2013. www.chemaxon.com 13. Huang, N., Shoichet, B., and Irwin, J., J. Med. Chem., 2006, vol. 49, no. 23, pp. 6789–6801. 14. Irwin, J.J., Sterling, T., Mysinger, M.M., et al., J. Chem. Inf. Model., 2012, vol. 52, no. 7, pp. 1757– 1768. 15. Trabanco, A.A. and Cid, J.M., Expert Opin. Ther. Pat., 2013, vol. 23, no. 5, pp. 629–647. ˆ
Thus, we constructed the models of the transmem brane domain of the mGluR2 receptor and the bind ing site of its positive allosteric modulators and opti mized them by molecular dynamics simulation. These models can be used for a more thorough analysis of the structure and mechanism of functioning of the recep tor as well as for searching and targeted design of new promising ligands.
6
Translated by M. Batrukova
DOKLADY BIOCHEMISTRY AND BIOPHYSICS
Vol. 454
2014
