复旦大学：《药物设计学》课程教学资源（教学研究）基于靶点结构的药物设计_Modeling the Interaction between Glycogen Synthase Kinase 3β（GSK-3β）and Its Non-ATP Competitive Inhibitors

山国劇蔹论文在线 http://www.paper.edu.cn Modeling the Interaction between Glycogen Synthase Kinase 3R(GsK-3B)and Its Non-ATP Competitive Inhibitors Department of Medicinal Chemistry, School of Pharmacy, Fudan University, Shanghai 201203, China The Genomics Research Center; Academia Sinica, 128 Academia Road, Section 2, Nankang, Taipei Department of Medicinal Chemistry, School of pharmacy, Shandong University, Jinan, Shandong 250012,Chia E-mail:cyl10@fudan.edu.cn(YChu),mli@sdu.edu.cn(M.Li) Abstract: Glycogen synthase kinase-3(GSK-3) plays an important role in a diverse number of regulatory pathways. GSK-3 inhibitors, particularly the non-ATP-competitive inhibitors, have been evaluated as promising drug candidates for a lot of unmet pathologies, such as Alzheimer's disease and diabetes. In this paper, a molecular docking study with the published GsK-3B crystal structure and receptor-based pharmacophore modeling of four highly active non-ATP-competitive GSK-3 inhibitors were performed by dOCK 5.4 and Catalyst 4.11, respectively. The results could provide an exquisite understanding on their mechanism of interaction within the non-ATP-binding pocket of GSK-3B, meanwhile the finding of the common properties shared by these pharmacological inhibitors of GsK-3B could be helpful to urther chemical optimization of such potent drug candidates Keywords: GSK-3B, non-ATP-competitive inhibitor, molecular dock, pharmacophore model Introduction In recent years, Glycogen Synthase Kinase-3(GSK-3) has been the focus of extensive medicinal hemistry efforts including for insulin resistance, Alzheimers disease(AD), stroke and bipolar disorders. Many GSK-3B inhibitors have been reported and reviewed in the literatures", such as maleimides, indirubins, paullones and hymenimaldisine derivatives. These compounds can decrease GSK-3 a thus to restore insulin responses including glucose uptake and glycogen synthesis in insulin signaling pathway, as well as decrease neurodegenerative markers and behaviora deficits in AD pathogenesis by reducing the production of AB, therefore reducing AB-induced neuronal cell death. Thus GsK-3 inhibitors clearly have clinical potential for the treatment of diabetes and AD Unfortunately, most of available inhibitors are bound to the ATP-binding pocket of GSK-3 Considering the aTP binding pocket is highly conserved in protein kinase, non-specific protein kinase inhibition by ATP site-directed inhibitors might have widespread effects. As a matter of fact, such GSK-3 inhibitors always showed many others kinases cross-activities though high selectivity of few were reported. This is the case of the great majority of GSK-3 inhibitors discovered until now diminishing their drug development possibilities I Financiall supported from Specialized Research Fund for the Doctoral Program of Higher Education(SRFDP No. 20070246089) Chinese Ministry of Education

1 Financiall supported from Specialized Research Fund for the Doctoral Program of Higher Education (SRFDP, No. 20070246089), Chinese Ministry of Education. - 1 - Modeling the Interaction between Glycogen Synthase Kinase 3β (GSK-3β) and Its Non-ATP Competitive Inhibitors Yong Chu 1 , Keng-Chang Tsai 2 , Deyong Ye 1 , Minyong Li 3,* 1 Department of Medicinal Chemistry, School of Pharmacy, Fudan University, Shanghai 201203, China 2 The Genomics Research Center, Academia Sinica, 128 Academia Road, Section 2, Nankang, Taipei 115 3 Department of Medicinal Chemistry, School of Pharmacy, Shandong University, Jinan, Shandong 250012, China E-mail: cy110@fudan.edu.cn (Y. Chu), * mli@sdu.edu.cn (M. Li) Abstract: Glycogen synthase kinase-3 (GSK-3) plays an important role in a diverse number of regulatory pathways. GSK-3 inhibitors, particularly the non-ATP-competitive inhibitors, have been evaluated as promising drug candidates for a lot of unmet pathologies, such as Alzheimer’s disease and diabetes. In this paper, a molecular docking study with the published GSK-3β crystal structure and receptor-based pharmacophore modeling of four highly active non-ATP-competitive GSK-3 inhibitors were performed by DOCK 5.4 and Catalyst 4.11, respectively. The results could provide an exquisite understanding on their mechanism of interaction within the non-ATP-binding pocket of GSK-3β, meanwhile the finding of the common properties shared by these pharmacological inhibitors of GSK-3β could be helpful to further chemical optimization of such potent drug candidates. Keywords: GSK-3β, non-ATP-competitive inhibitor, molecular dock, pharmacophore model Introduction In recent years, Glycogen Synthase Kinase-3 (GSK-3) has been the focus of extensive medicinal chemistry efforts including for insulin resistance 1-2, Alzheimer’s disease (AD)3-5, stroke and bipolar disorders6 . Many GSK-3β inhibitors have been reported and reviewed in the literatures7-8, such as maleimides, indirubins, paullones and hymenimaldisine derivatives. These compounds can decrease GSK-3 activity, thus to restore insulin responses including glucose uptake and glycogen synthesis in insulin signaling pathway9-11, as well as decrease neurodegenerative markers and behaviora deficits in AD pathogenesis12 by reducing the production of Aβ13, therefore reducing Aβ-induced neuronal cell death14. Thus GSK-3 inhibitors clearly have clinical potential for the treatment of diabetes and AD. Unfortunately, most of available inhibitors are bound to the ATP-binding pocket of GSK-3. Considering the ATP binding pocket is highly conserved in protein kinase, non-specific protein kinase inhibition by ATP site-directed inhibitors might have widespread effects. As a matter of fact, such GSK-3 inhibitors always showed many others kinases cross-activities though high selectivity of few were reported. This is the case of the great majority of GSK-3 inhibitors discovered until now, which is diminishing their drug development possibilities. 中国科技论文在线 http://www.paper.edu.cn

山国武论义在统 http://www edu.cn However, non-ATP-competitive GSK-3 selective inhibitors represent a more efficient pathway for providing real promising drugs for therapeutic intervention. Thiadiazolidinones (TDZDs)and halomethylarylketones(HMKs)were reported as first two families of non-ATP competitive GSK-3 inhibitors and both of them really do not show inhibition on others several kinases as PKA, PKC, CK-2 nd CDK1cyclin B. The privileged scaffold of TDZDs for the selective inhibition of GSK-3 has been revealed based on an extensive SAR study, and two binding modes were then put forward by mapping studies. This information in turn guided an optimization toward the inhibitory activity of TDZD Herein four highly active TDZD and HMK inhibitors 1-4 as depicted in Figure 1 were selected based on structure diversity and a docking study was performed with the published GsK-3B crystal structure(PDB code: 1Q3D)to provide an exquisite understanding of their mechanism of interaction within the non-ATP-binding pocket. The finding of the common properties shared by these pharmacological inhibitors of GSK-3B would be helpful to further optimize these potential drug candidates C O 2 Figure 1. The chemical structures of TDzD and hmK inhibitors 1-4 Materials and methods Molecular docking and structural optimization structures for these four inhibitors were refined using the pm3 method in the mopac 7 and assigned with AMl-BCC partial charges by the QuACPAC program. All partial charges on the atoms of the homology model were derived from AMBEr parameters Docking of the ligands into GSK-3B was performed by using DOCK 5.4. The active site included residues Arg 92 Arg96, Arg180, Lys205 and Tyr216 as recommended by literatures. 5, I7 After docking, MD simulations were carried out by using the CHARMM c33bl program- and a GBSW implicit solvation model- following similar procedures we reported elsewhere. The protein atoms were parameterized by CHARMM-GUl using the CHARMM22 force field.The surface tension coefficient(representing the non-polar solvation energy) was set to 0.03 kcal/mol A),which was consistent with literature precedents in the calculation of non-polar contributions in soluble proteins. 26 All bond lengths involving hydrogen atoms were fixed using the SHAKE algorithm 30.No cutoff for the non-bonded and GB energy calculations was used. In the simulation, temperature was at 300 K. Minimizations were carried out using 1500 steps of steepest descent, followed by Adopted Basis Newton-Raphson(ABNR) minimization until the root mean square gradient was less than 0.001 kcal/mol A. The whole system was then equilibrated for 50 ps, followed by another 10 ns of canonical ensemble (NVT)-MD simulation run. Finally, the ligand-receptor complexes were analyzed by

- 2 - However, non-ATP-competitive GSK-3 selective inhibitors represent a more efficient pathway for providing real promising drugs for therapeutic intervention. Thiadiazolidinones (TDZDs) and halomethylarylketones (HMKs) were reported as first two families of non-ATP competitive GSK-3 inhibitors and both of them really do not show inhibition on others several kinases as PKA, PKC, CK-2 and CDK1/cyclin B15-16. The privileged scaffold of TDZDs for the selective inhibition of GSK-3 has been revealed based on an extensive SAR study, and two binding modes were then put forward by mapping studies17-18. This information in turn guided an optimization toward the inhibitory activity of TDZDs. Herein four highly active TDZD and HMK inhibitors 1-4 as depicted in Figure 1 were selected based on structure diversity and a docking study was performed with the published GSK-3β crystal structure (PDB code: 1Q3D)19 to provide an exquisite understanding of their mechanism of interaction within the non-ATP-binding pocket. The finding of the common properties shared by these pharmacological inhibitors of GSK-3β would be helpful to further optimize these potential drug candidates. S N N O CH3 N S H S N O O N S N O O CH3 S Cl O Br Br 1 2 3 4 Figure 1. The chemical structures of TDZD and HMK inhibitors 1-4 Materials and Methods Molecular docking and structural optimization The 3D structures for these four inhibitors were refined using the PM3 method in the MOPAC 7 program 20 and assigned with AM1-BCC partial charges 21-23 by the QuACPAC program. All partial charges on the atoms of the homology model were derived from AMBER 8 parameters. Docking of the ligands into GSK-3β was performed by using DOCK 5.4 24. The active site included residues Arg92, Arg96, Arg180, Lys205 and Tyr216 as recommended by literatures.15, 17 After docking, MD simulations were carried out by using the CHARMM c33b1 program25 and a GBSW implicit solvation model26 following similar procedures we reported elsewhere.27 The protein atoms were parameterized by CHARMM-GUI28 using the CHARMM22 force field 29. The surface tension coefficient (representing the non-polar solvation energy) was set to 0.03 kcal/ (mol·Å2 ), which was consistent with literature precedents in the calculation of non-polar contributions in soluble proteins. 26 All bond lengths involving hydrogen atoms were fixed using the SHAKE algorithm 30. No cutoff for the non-bonded and GB energy calculations was used. In the simulation, temperature was at 300 K. Minimizations were carried out using 1500 steps of steepest descent, followed by Adopted Basis Newton-Raphson (ABNR) minimization until the root mean square gradient was less than 0.001 kcal/mol Å. The whole system was then equilibrated for 50 ps, followed by another 10 ns of canonical ensemble (NVT)-MD simulation run. Finally, the ligand-receptor complexes were analyzed by 中国科技论文在线 http://www.paper.edu.cn

山国到技论文在线 http://www.paper.edu.cn HBPLUS 3.06, LIGPLOT 4.223and Pymol 0.99 33 Docking-based pharmacophore modeling Based on the molecular docking and molecular dynamics results, an attempt to identify the hypothetical receptor-based pharmacophore model was made by using Hiphop algorithm implemented in Catalyst 4. 11 package. Such practice is the same as what we have used in the past for other modeling works. 4 +-33 In particular, HipHop algorithm finds common-feature pharmacophore models among a set of highly active compounds thus it carries out a 'qualitative model without the use of activity data, which represents the essential 3D arrangement of functional groups common to a set of molecules for interacting with a specific biological target In this hypothesis generation phase, the docking conformations of these four inhibitors were directly used as input coordinates without any structural minimization and conformational search. A default uncertainty factor of 3 for each compound was defined, and four chemical features, including hydrogen-bond acceptor(A), hydrogen-bond donor(D), aromatic ring(R)and hydrophobic(H) group, were selected to form the pharmacophore hypothesis using HipHop. A Principal number of 2 and MaxOmit Feat number of 0 was defined for the good mapping of all features of these compounds on a Hardware and sofhvare Molecular docking(doCk 5.4), binding analysis(HBPLUs 3.06 and ligplot 4.22)and visualization (PyMol 0.99) were carried out on a Linux workstation. MM calculations and MD simulations (CHARMM c33b1)were performed on URSA, a 160-processor computer based on the Power5+ processor and IBMs P series architecture. The pharmacophore modeling(Catalyst 4. 11)was executed on a SGI Origin 3800 workstation equipped with 48 x400 MHz MIPS R12000 processors Results and discussion The proposed binding conformation and schematic diagram of compound I are depicted in Figure 2 The symmetrical acyl groups herein are proposed to have multiple hydrogen bonding with Arg 96, Arg 180 and Lys 205, in the meanwhile the molecular scaffold of this compound is engaged in the hydrophobic interaction with Val 214 and Tyr 216. Moreover, the phenyl ring should have a T-stacking with Tyr 216

- 3 - HBPLUS 3.06 31, LIGPLOT 4.22 32 and Pymol 0.99 33. Docking-based pharmacophore modeling Based on the molecular docking and molecular dynamics results, an attempt to identify the hypothetical receptor-based pharmacophore model was made by using Hiphop algorithm implemented in Catalyst 4.11 package. Such practice is the same as what we have used in the past for other modeling works.34-35 In particular, HipHop algorithm finds common-feature pharmacophore models among a set of highly active compounds thus it carries out a ‘qualitative model’ without the use of activity data, which represents the essential 3D arrangement of functional groups common to a set of molecules for interacting with a specific biological target 36. In this hypothesis generation phase, the docking conformations of these four inhibitors were directly used as input coordinates without any structural minimization and conformational search. A default uncertainty factor of 3 for each compound was defined, and four chemical features, including hydrogen-bond acceptor (A), hydrogen-bond donor (D), aromatic ring (R) and hydrophobic (H) group, were selected to form the pharmacophore hypothesis using HipHop. A Principal number of 2 and MaxOmitFeat number of 0 was defined for the good mapping of all features of these compounds on a hypothesis model 37. Hardware and software Molecular docking (DOCK 5.4), binding analysis (HBPLUS 3.06 and Ligplot 4.22) and visualization (PyMol 0.99) were carried out on a Linux workstation. MM calculations and MD simulations (CHARMM c33b1) were performed on URSA, a 160-processor computer based on the Power5+ processor and IBM’s P series architecture. The pharmacophore modeling (Catalyst 4.11) was executed on a SGI Origin 3800 workstation equipped with 48×400 MHz MIPS R12000 processors. Results and Discussion The proposed binding conformation and schematic diagram of compound 1 are depicted in Figure 2. The symmetrical acyl groups herein are proposed to have multiple hydrogen bonding with Arg 96, Arg 180 and Lys 205, in the meanwhile the molecular scaffold of this compound is engaged in the hydrophobic interaction with Val 214 and Tyr 216. Moreover, the phenyl ring should have a π-stacking with Tyr 216. 中国科技论文在线 http://www.paper.edu.cn

山国到技论文在线 http://www.paper.edu.cn A B Figure 2. The simulated conformation(A)and interaction diagram(B)of compound I and GSK-3B For compound 2, it has similar binding pattern with compound 1 (Figure 3). The two ketone groups in compound 2 play roles of hydrogen bond acceptors for contacting with Arg 96, Arg 180 and Lys 205 The whole structure of this molecule is trapped into a hydrophobic pocket lined by gly 202, Ser 203 nd Tyr 216. It should be noted that the phenyl ring herein also contributes to a T-stacking with Tyr 216 A B Figure 3. The simulated conformation(A)and interaction diagram(B)of compound 2 and GsK-3B In the case of compound 3, the simulated results suggest that the ketone group forms three hydrogen bonds with Arg 180 and Lys 205, while the imine group engages in hydrogen binding with Arg 96 in GSK-3B(Figure 4). The computational results also propose that this compound inserts into a hydrophobic network clustered by Ser 203 and Tyr 216. In addition, the phenyl ring seems to build up a T-stacking interaction with Tyr 216

- 4 - Figure 2. The simulated conformation (A) and interaction diagram (B) of compound 1 and GSK-3β For compound 2, it has similar binding pattern with compound 1 (Figure 3). The two ketone groups in compound 2 play roles of hydrogen bond acceptors for contacting with Arg 96, Arg 180 and Lys 205. The whole structure of this molecule is trapped into a hydrophobic pocket lined by Gly 202, Ser 203 and Tyr 216. It should be noted that the phenyl ring herein also contributes to a π-stacking with Tyr 216. Figure 3. The simulated conformation (A) and interaction diagram (B) of compound 2 and GSK-3β In the case of compound 3, the simulated results suggest that the ketone group forms three hydrogen bonds with Arg 180 and Lys 205, while the imine group engages in hydrogen binding with Arg 96 in GSK-3β (Figure 4). The computational results also propose that this compound inserts into a hydrophobic network clustered by Ser 203 and Tyr 216. In addition, the phenyl ring seems to build up a π-stacking interaction with Tyr 216. 中国科技论文在线 http://www.paper.edu.cn

山国到技论文在线 http://www.paper.edu.cn A B Figure 4. The simulated conformation(A)and interaction diagram(B)of compound 3 and GSK-3B Figure 5 shows the proposed main interactions between GSK-3B and compound 4. In brief, this molecule is proposed to be captured by Val 214 and Tyr 216 through hydrophobic and T-stacking interaction. The ketone and chloride group forms a network of hydrogen bonds with Arg 96, Arg 180 and lys 205 A B Figure 5. The simulated conformation(A)and interaction diagram(B)of compound 4 and GsK-3B HipHop calculation produces ten hypotheses, and Hypol is the best significant pharmacophore hypothesis in this study, characterized by the highest ranking score(Table 1). All the ten hypotheses have the same features of one aromatic ring(r) and two hydrogen bond acceptors(A). In Hypol, it significantly that the docking conformations of these four compounds could be overlapped into three harmacophore points, in which the distances between two features are 3.96, 4.28 and 5.04 A( Figure 6), respectively Table 1. Results of the common feature hypothesis run* Hypothesis No Ranking score Direct hit Partial Hit RAA l08900 lIlI 0000 234 RAA 08.799 l111 0000 RAA 108.593 l111 RAA 108.388 l111

- 5 - Figure 4. The simulated conformation (A) and interaction diagram (B) of compound 3 and GSK-3β Figure 5 shows the proposed main interactions between GSK-3β and compound 4. In brief, this molecule is proposed to be captured by Val 214 and Tyr 216 through hydrophobic and π-stacking interaction. The ketone and chloride group forms a network of hydrogen bonds with Arg 96, Arg 180 and Lys 205. Figure 5. The simulated conformation (A) and interaction diagram (B) of compound 4 and GSK-3β HipHop calculation produces ten hypotheses, and Hypo1 is the best significant pharmacophore hypothesis in this study, characterized by the highest ranking score (Table 1). All the ten hypotheses have the same features of one aromatic ring (R) and two hydrogen bond acceptors (A). In Hypo1, it is significantly that the docking conformations of these four compounds could be overlapped into three pharmacophore points, in which the distances between two features are 3.96, 4.28 and 5.04 Å (Figure 6), respectively. Table 1. Results of the common feature hypothesis run* Hypothesis No. Composition Ranking Score Direct Hit Partial Hit 1 RAA 108.900 1111 0000 2 RAA 108.799 1111 0000 3 RAA 108.593 1111 0000 4 RAA 108.388 1111 0000 中国科技论文在线 http://www.paper.edu.cn

山国到技论文在线 http://www.paper.edu.cn RAA 108365 1lI 0000 RAA 08.256 RAA 0000 RAA 08.084 l111 RAA l08018 Direct Hit, all the features of the hypothesis are mapped. Direct Hit=l means yes and Direct Hit=0 is no; Partial Hit, partial mapping of the hypothesis. Partial Hit=l means yes and Partial Hit=0 means no A B 28 Figure 6. The representation of pharmacophore model Hypol alone(A)and mapped with all four compounds(B). Distances between pharmacophore features are reported in angstroms. The aromatic and hydrogen bond acceptor pharmacophore feature is coded as gold and green spheres, respectively For all four compounds, the overlapping of docking conformations and pharmacophore features is shown in Figure 7. It is obviously that all conformations of four compounds have perfect match with three pharmacophore features. Combined with molecular docking and structural optimization results the two hydrogen bond acceptors in ligand should have hydrogen bonding with Arg 96, Arg 180 and Lys 205 around non-ATP competitive active site of GsK-3B, while the one aromatic feature seems to form T-stacking interaction with Tyr 216

- 6 - 5 RAA 108.365 1111 0000 6 RAA 108.256 1111 0000 7 RAA 108.152 1111 0000 8 RAA 108.126 1111 0000 9 RAA 108.084 1111 0000 10 RAA 108.018 1111 0000 * Direct Hit, all the features of the hypothesis are mapped. Direct Hit=1 means yes and Direct Hit=0 is no; Partial Hit, partial mapping of the hypothesis. Partial Hit=1 means yes and Partial Hit=0 means no. Figure 6. The representation of pharmacophore model Hypo1 alone (A) and mapped with all four compounds (B). Distances between pharmacophore features are reported in angstroms. The aromatic and hydrogen bond acceptor pharmacophore feature is coded as gold and green spheres, respectively. For all four compounds, the overlapping of docking conformations and pharmacophore features is shown in Figure 7. It is obviously that all conformations of four compounds have perfect match with three pharmacophore features. Combined with molecular docking and structural optimization results, the two hydrogen bond acceptors in ligand should have hydrogen bonding with Arg 96, Arg 180 and Lys 205 around non-ATP competitive active site of GSK-3β, while the one aromatic feature seems to form π-stacking interaction with Tyr 216. 中国科技论文在线 http://www.paper.edu.cn

山国到技论文在线 http://www.paper.edu.cn A B C Figure 7. Pharmacophore Hypol mapped with compounds 1(A),2(B),3(C)and 4 (D) The good overlapping of pharmacophore features and simulated interaction patterns can provide strong confidence on both the pharmacophore modeling and docking results. Therefore, this structure-based pharmacophore model will provide more detailed information and accuracy in its description of non-ATP competitive inhibitors against GSK-3B. Such an application of pharmacophore models can there fore be useful in the detection of new leads The current study proposed the ligand-receptor interaction of four highly active non-ATP-competitive GSK-3B inhibitors(TDZDs and HMKs) In brief, these compounds were docked into the non-ATP site of the published GsK-3B crystal structure by DOCK 5.4. Furthermore, docking-based pharmacophore model was established by using Hiphop algorithm implemented in the Catalyst 4.11 package. All of these active compounds have the highly similar features, including one aromatic ring(r)and two hydrogen bond acceptors(A). The modeling results also exhibited that two hydrogen bond acceptors form hydrogen bonding with Arg 96, Arg 180 and lys 205, and the aromatic feature form T-stacking interaction with Tyr 216 in the non-ATP-binding pocket of GSK-3B. Based on the established

- 7 - Figure 7. Pharmacophore Hypo1 mapped with compounds 1 (A), 2 (B), 3 (C) and 4 (D) The good overlapping of pharmacophore features and simulated interaction patterns can provide strong confidence on both the pharmacophore modeling and docking results. Therefore, this structure-based pharmacophore model will provide more detailed information and accuracy in its description of non-ATP competitive inhibitors against GSK-3β. Such an application of pharmacophore models can therefore be useful in the detection of new leads. Conclusion The current study proposed the ligand-receptor interaction of four highly active non-ATP-competitive GSK-3β inhibitors (TDZDs and HMKs). In brief, these compounds were docked into the non-ATP site of the published GSK-3β crystal structure by DOCK 5.4. Furthermore, docking-based pharmacophore model was established by using Hiphop algorithm implemented in the Catalyst 4.11 package. All of these active compounds have the highly similar features, including one aromatic ring (R) and two hydrogen bond acceptors (A). The modeling results also exhibited that two hydrogen bond acceptors form hydrogen bonding with Arg 96, Arg 180 and Lys 205, and the aromatic feature form π-stacking interaction with Tyr 216 in the non-ATP-binding pocket of GSK-3β. Based on the established 中国科技论文在线 http://www.paper.edu.cn

山国到技论文在线 http://www.paper.edu.cn three-point pharmacophore model, the distances of R-Al, R-A2, and ArA2 are 3.96, 5.04, and 4.28 A respectively. The resulting structural information provide an exquisite understanding of their mechanism of interaction within non-ATP binding site of GSK-3B, and the finding of the common properties shared by such pharmacological inhibitors could be helpful for further optimization of these potent drug candidates Acknowledgement le gratefully acknowledge the financial support from Specialized Research Fund for the doctoral Program of Higher Education (SRFDP, No. 20070246089), Chinese Ministry of Education, Youth Science Foundation of Fudan University and Youth Fund of School of Pharmacy, Fudan University References 1. Lee, J Kim, M.S. The role of GsK3 in glucose homeostasis and the development of insulin resistance Diabetes Res. Clin Pract. 2007, 77 Suppl 1, $49-57 2. Frojdo, S; Vidal, H ; Pirola, L. Alterations of insulin signaling in type 2 diabetes: a review of the current evidence from humans. Biochim. Biophys. Acta. 2009, 1792, 83-92 3. Hanger, D. P; Hughes, K, Woodgett, J. R; Brion, J. P, Anderton, B. H. Glycogen synthase kinase-3 induces Alzheimers disease-like phosphorylation of tau generation of paired helical filament epitopes and neuronal localisation of the kinase. Neurosci. Lett. 1992, 147, 58-62. 4. Hernandez, F, Avila, J. The role of glycogen synthase kinase 3 in the early stages of Alzheimers' disease FEBS Lett. 2008 5. Martin, L, Magnaudeix, A Esclaire, F. Yardin, C. Terro, F. Inhibition of glycogen synthase kinase-3beta downregulates total tau proteins in cultured neurons and its reversal by the blockade of protein phosphatase-2A Brain res.2009,1252,66-75 6. Gould, T D; Zarate, C. A, Manji, H. K. Glycogen synthase kinase-3: a target for novel bipolar disorder treatments. Clin Psychiatry 2004, 65, 10-21 7. Alonso, M ; Martinez, A. GSK-3 inhibitors: discoveries and developments. Curr. Med. Chem. 2004, 11 755-63 8. Martinez, A. Preclinical efficacy on GSK-3 inhibitors: towards a future generation of powerful drugs. Med. Res.Rev.2008,28,773-96 9. Cohen, P. Goedert, M. GSK3 inhibitors: development and therapeutic potential. Nature Reviews Drug Discover2004,3,479-487 10. Wagman, A S, Johnson, K. W, Bussiere, D. E. Discovery and development of GsK3 inhibitors for the treatment of type 2 diabetes. Current Pharmaceutical Design 2004, 10, 1105-1137 rame S ; Zheleva, D. Targeting glycogen synthase kinase-3 in insulin signalling. Expert Opinion on Therapeutic Targets 2006, 10, 429-444 12. Hu, S, Begum, A. N, Jones, M.R.: Oh, M. S: Beech, w.K.: Begum, A. N, Jones, M.R. Beech, B. H Yang, F, Chen, P; Ubeda, O. J. Kim, P. C. Davies, P. Ma, Q; Cole, G. M; Frautschy, S. A. GSK3 inhibitors show benefits in an Alzheimers disease(AD) model of neurodegeneration but adverse effects in control animals Neurobiology of Disease 2009, 33, 193-206 13. Sun, X, Sato, S, Murayama, O: Murayama, M, Park, J. M ; Yamaguchi, H. Takashima, A. Lithium inhibits amy loid secretion in COs7 cells transfected with amyloid precursor protein C100. Neurosci. Left. 2002 321,61-64 14. Koh, S. H; Noh, M.Y. Kim, S.H. Amyloid-beta-induced neurotoxicity is reduced by inhibition of glycogen synthase kinase-3. Brain Research 2008, 1188, 254-262 15. Martinez, A, Alonso, M, Castro, A, Perez, C. Moreno, F J. First Non-ATP Competitive Glycogen Synthase Kinase 3B(GSK-3B)Inhibitors: Thiadiazolidinones (TDzD) as Potential Drugs for the Treatment of Alzheimers Disease. J. Med. Chem. 2002, 45. 1292-1299. 16. Conde, S; Perez, D. I, Martinez, A; Perez, C. Moreno, F J. Thienyl and Pher New Inhibitors of Glycogen Synthase Kinase(GSK-3 alpha) from a Library of Ce ol alhopuard seaming toes. Chem.2003,46,4631-4633 17. Martinez, A Alonso, M. Castro, A, Dorronsoro, I, Gelpi, J. L; Luque, F J, Perez, C, Moreno, F.J. SAR and 3D-QSAR studies on thiadiazolidinone derivatives: exploration of structural requirements for glycogen synthase kinase 3 inhibitors. J. Med. Chem. 2005, 48, 7103-12 18. Castro, A, Encinas, A, Gil, C; Brase, S, Porcal, W, Perez, C ; Moreno, F. J, Martinez, A. Non-ATP competitive glycogen synthase kinase 3[beta](GSK-3 [beta)inhibitors: Study of structural requirements for thiadiazolidinone derivatives. Bioorganic Medicinal Chemistry 2008, 16, 495-510 19. Bertrand, J. A, Thieffine, S, Vulpetti, A; Cristiani, C. Valsasina, B ; Knapp, S, Kalisz, H. M., Flocco, M Structural characterization of the gsK-3 beta active site using selective and non-selective ATP-mi J.Mol.Biol.2003,333,393-407. 0. Stewart, JJ MOPAC: a semiempirical molecular orbital program. J Comput Aided Mol Des 1990, 4, 1-105 1. Jakalian, A, Bush, B. L, Jack, D. B, Bayly, C. I. Fast, Efficient Generation of High-Quality Atomi

- 8 - three-point pharmacophore model, the distances of R–A1, R–A2, and A1–A2 are 3.96, 5.04, and 4.28 A˚, respectively. The resulting structural information provide an exquisite understanding of their mechanism of interaction within non-ATP binding site of GSK-3β, and the finding of the common properties shared by such pharmacological inhibitors could be helpful for further optimization of these potent drug candidates. Acknowledgement We gratefully acknowledge the financial support from Specialized Research Fund for the Doctoral Program of Higher Education (SRFDP, No. 20070246089), Chinese Ministry of Education, Youth Science Foundation of Fudan University and Youth Fund of School of Pharmacy, Fudan University. References: 1. Lee, J.; Kim, M. S. The role of GSK3 in glucose homeostasis and the development of insulin resistance. Diabetes Res. Clin. Pract. 2007, 77 Suppl 1, S49-57. 2. Frojdo, S.; Vidal, H.; Pirola, L. Alterations of insulin signaling in type 2 diabetes: a review of the current evidence from humans. Biochim. Biophys. Acta. 2009, 1792, 83-92. 3. Hanger, D. P.; Hughes, K.; Woodgett, J. R.; Brion, J. P.; Anderton, B. H. Glycogen synthase kinase-3 induces Alzheimer's disease-like phosphorylation of tau: generation of paired helical filament epitopes and neuronal localisation of the kinase. Neurosci. Lett. 1992, 147, 58-62. 4. Hernandez, F.; Avila, J. The role of glycogen synthase kinase 3 in the early stages of Alzheimers' disease. FEBS Lett. 2008, 582, 3848-54. 5. Martin, L.; Magnaudeix, A.; Esclaire, F.; Yardin, C.; Terro, F. Inhibition of glycogen synthase kinase-3beta downregulates total tau proteins in cultured neurons and its reversal by the blockade of protein phosphatase-2A. Brain Res. 2009, 1252, 66-75. 6. Gould, T. D.; Zarate, C. A.; Manji, H. K. Glycogen synthase kinase-3: a target for novel bipolar disorder treatments. J Clin Psychiatry 2004, 65, 10-21. 7. Alonso, M.; Martinez, A. GSK-3 inhibitors: discoveries and developments. Curr. Med. Chem. 2004, 11, 755-63. 8. Martinez, A. Preclinical efficacy on GSK-3 inhibitors: towards a future generation of powerful drugs. Med. Res. Rev. 2008, 28, 773-96. 9. Cohen, P.; Goedert, M. GSK3 inhibitors: development and therapeutic potential. Nature Reviews Drug Discovery 2004, 3, 479-487. 10. Wagman, A. S.; Johnson, K. W.; Bussiere, D. E. Discovery and development of GSK3 inhibitors for the treatment of type 2 diabetes. Current Pharmaceutical Design 2004, 10, 1105-1137. 11. Frame, S.; Zheleva, D. Targeting glycogen synthase kinase-3 in insulin signalling. Expert Opinion on Therapeutic Targets 2006, 10, 429-444. 12. Hu, S.; Begum, A. N.; Jones, M. R.; Oh, M. S.; Beech, W. K.; Begum, A. N.; Jones, M. R.; Beech, B. H.; Yang, F.; Chen, P.; Ubeda, O. J.; Kim, P. C.; Davies, P.; Ma, Q.; Cole, G. M.; Frautschy, S. A. GSK3 inhibitors show benefits in an Alzheimer's disease (AD) model of neurodegeneration but adverse effects in control animals. Neurobiology of Disease 2009, 33, 193-206. 13. Sun, X.; Sato, S.; Murayama, O.; Murayama, M.; Park, J. M.; Yamaguchi, H.; Takashima, A. Lithium inhibits amyloid secretion in COS7 cells transfected with amyloid precursor protein C100. Neurosci.Lett. 2002, 321, 61-64 14. Koh, S. H.; Noh, M. Y.; Kim, S. H. Amyloid-beta-induced neurotoxicity is reduced by inhibition of glycogen synthase kinase-3. Brain Research 2008, 1188, 254-262. 15. Martinez, A.; Alonso, M.; Castro, A.; Perez, C.; Moreno, F. J. First Non-ATP Competitive Glycogen Synthase Kinase 3β(GSK-3β) Inhibitors: Thiadiazolidinones (TDZD) as Potential Drugs for the Treatment of Alzheimer's Disease. J. Med. Chem. 2002, 45, 1292-1299. 16. Conde, S.; Perez, D. I.; Martinez, A.; Perez, C.; Moreno, F. J. Thienyl and Phenyl alhpa-Halomethyl Ketones: New Inhibitors of Glycogen Synthase Kinase (GSK-3 alpha) from a Library of Compound Searching. J. Med. Chem. 2003, 46, 4631-4633. 17. Martinez, A.; Alonso, M.; Castro, A.; Dorronsoro, I.; Gelpi, J. L.; Luque, F. J.; Perez, C.; Moreno, F. J. SAR and 3D-QSAR studies on thiadiazolidinone derivatives: exploration of structural requirements for glycogen synthase kinase 3 inhibitors. J. Med. Chem. 2005, 48, 7103-12. 18. Castro, A.; Encinas, A.; Gil, C.; Bräse, S.; Porcal, W.; Pérez, C.; Moreno, F. J.; Martínez, A. Non-ATP competitive glycogen synthase kinase 3[beta] (GSK-3[beta]) inhibitors: Study of structural requirements for thiadiazolidinone derivatives. Bioorganic & Medicinal Chemistry 2008, 16, 495-510. 19. Bertrand, J. A.; Thieffine, S.; Vulpetti, A.; Cristiani, C.; Valsasina, B.; Knapp, S.; Kalisz, H. M.; Flocco, M. Structural characterization of the GSK-3beta active site using selective and non-selective ATP-mimetic inhibitors. J. Mol. Biol. 2003, 333, 393-407. 20. Stewart, J. J. MOPAC: a semiempirical molecular orbital program. J Comput Aided Mol Des 1990, 4, 1-105. 21. Jakalian, A.; Bush, B. L.; Jack, D. B.; Bayly, C. I. Fast, Efficient Generation of High-Quality Atomic 中国科技论文在线 http://www.paper.edu.cn

山国到技论文在线 http://www.paper.edu.cn Charges. AMI-BCC Model: I Method. J Comput. Chem. 2000, 21, 132-146 22. Jakalian, A, Jack, D. B, Bayly, C. I. Fast, Efficient Generation of High-Quality Atomic Charges AMI-BCC Model: II. Parameterization and Validation. Comput Chem. 2002, 23, 1623-41 23. Tsai, K.-C, Wang, S.-H, Hsiao, N -W,Li, M ; Wang, B. The Effect of Different Electrostatic Potentials on Docking Accuracy: A Case Study Using DOCKs.4. Bioorg. Med. Chem. Left. 2008, 18, 3509-3512 24. Moustakas, D. T. Lang, P. T, Pegg, S, Pettersen, E, Kuntz, I. D; Brooijmans, N, Rizzo, R. C Development and validation of a modular, extensible docking program: DOCK 5.J Comput Aided Mol Des 2006. 20,601-19 25. Brooks, B. R, Brooks, C. L, 3rd; Mackerell, A. D, Jr; Nilsson, L: Petrella, R J: Roux, B: Won,Y J: Hodoscek, M.: Im, w: Kuczera, K, Lazaridis, T, Ma, J: Ovchinnikov, V. Paci, E, Pastor, R. W: Post, C. B Pu, J Z, Schaefer, M: Tidor, B. Venable, R. M, Woodcock, H. L; Wu, X, Yang, W: York, D CHARMM: the biomolecular simulation program. J. Comput. Chem. 2009, 30, 1545-614 6. Im, w, Lee, M.S.; Brooks, C. L,, 3rd. Generalized born model with a simple smoothing function. J. Compt Che Li, M. Y: Fang, H, Du, L. P; Xia, L, Wang, B. H. Computational studies of the binding site of Chem.2003.24.1691702 C1A-adrenoceptor antagonists J. Mol. Model. 2008, 14, 957-966 28. Jo, S, Kim, T; lyer, V G. Im, W. CHARMM-GUI a web-based graphical user interface for CHARMM.J. Compul.Chem.2008,29,1859-65 29. MacKerell, A D; Bashford, D. Bellott, M; Dunbrack, R L Evanseck, J D; Field, M.J. ; Fischer, S; Gao, J, Guo, H,, Ha, S, Joseph-McCarthy, D. Kuchnir, L, Kuczera, K, Lau, F. T.K., Mattos, C, Michnick, S, Ngo, T, Nguyen, D. T; Prodhom, B, Reiher, W. E; Roux, B. Schlenkrich, M, Smith, J. C. Stote, R ; Straub, J Watanabe, M, Wiorkiewicz-Kuczera, J, Yin, D; Karplus, M. All-Atom Empirical Potential for Molecular Modeling and Dynamics Studies of Proteins. J. Phys. Chem. B 1998, 102, 3586-3616 30. Ryckaert, J -P Ciccotti, G ; Berendsen, H. J. C. Numerical integration of the cartesian equations of motion of a system with constraints: molecular dynamics of n-alkanes. J. CompuT. Phys. 1977, 23, 327-341 61. McDonald, I. K, Thornton, J. M. Satisfying hydrogen bonding potential in proteins. J Mol Bio! 1994, 238, 777-93 2. Wallace, A C; Laskowski, R. A, Thornton, J M. LIGPLOT: a program to generate schematic diagrams of otein-ligand interactions Protein Eng 1995,8, 127-34 3. DeLano, W.L. The PyMOL Molecular Graphics System, 0.99; DeLano Scientific, San Carlos, CA, USA 2006. 34. Yang, Q, Du, L P, Wang, X J, Li, M. Y, You, Q. D. Modeling the binding modes of Kv1.5 potassium channel and blockers. J. Mol. Graph. Model. 2008, 27, 178-187 35. Yang, Q, Du, L. P, Wang, X.J. ,Li, M. Y,, You, Q. D. Pharmacophore Mapping for Kv1.5 Potassium Channel Blockers OSAR Comb. Sci. 2009, 28, 59-71 36. Clement, O. O; Mehl, A. T. HipHop: pha phores based on multiple common-feature alignments In Pharmacophore Perception, Development Use In Drug Design, International University Line: 2000; Vol 2, pp 37. Barnum, D; Greene, J, Smellie, A; Sprague, P. Identification of common functional configurations among molecules. J. Chem. Inf Comput. Sci. 1996, 36, 563-71 糖原合成酶激酶-3β(GSK-3B)的非ATP竞争抑制剂药效团模型研究 楚勇,蔡耿彰2,叶德泳',李敏勇3 复大学药学院药化教研室,中国上海,201203 2中央研究院基因体研究中心,台北市南港区研究院路二段128号,115 3山东大学药学院药物化学研究室,山东省济南市,250012 E-mail:cy110@fudan.edu.cn(YChu),mli@sdu.edu.cn(M.Li) 摘要 糖原合成酶激酶-3(GSK-3)在多个信号传导通路中起着重要的调节作用,其抑制剂可用于治疗糖尿病和阿 尔茨海默氏症等多种疾病,尤其是非ATP竞争型抑制剂选择性更高,毒副作用更小。本文选择了4个高活 性的非ATP竞争的GSK-3B抑制剂,运用D0CK5.4对GSK-3B的晶体结构进行分子对接,在此基础上,采 用 Catalyst4.ll建立了基于配体的药效团模型。该结果加深了这些化合物对GSK-3β非ATP结合口袋作 用机理的进一步认识,可用于活性分子的进一步设计和优化 关键词 糖原合成酶激酶一3β(GSK-3β),非ATP竞争抑制剂,分子对接,药效团模型

- 9 - Charges. AM1-BCC Model: I. Method. J. Comput. Chem. 2000, 21, 132-146. 22. Jakalian, A.; Jack, D. B.; Bayly, C. I. Fast, Efficient Generation of High-Quality Atomic Charges. AM1-BCC Model: II. Parameterization and Validation. J. Comput. Chem. 2002, 23, 1623-41. 23. Tsai, K.-C.; Wang, S.-H.; Hsiao, N.-W.; Li, M.; Wang, B. The Effect of Different Electrostatic Potentials on Docking Accuracy: A Case Study Using DOCK5.4. Bioorg. Med. Chem. Lett. 2008, 18, 3509-3512. 24. Moustakas, D. T.; Lang, P. T.; Pegg, S.; Pettersen, E.; Kuntz, I. D.; Brooijmans, N.; Rizzo, R. C. Development and validation of a modular, extensible docking program: DOCK 5. J Comput Aided Mol Des 2006, 20, 601-19. 25. Brooks, B. R.; Brooks, C. L., 3rd; Mackerell, A. D., Jr.; Nilsson, L.; Petrella, R. J.; Roux, B.; Won, Y.; Archontis, G.; Bartels, C.; Boresch, S.; Caflisch, A.; Caves, L.; Cui, Q.; Dinner, A. R.; Feig, M.; Fischer, S.; Gao, J.; Hodoscek, M.; Im, W.; Kuczera, K.; Lazaridis, T.; Ma, J.; Ovchinnikov, V.; Paci, E.; Pastor, R. W.; Post, C. B.; Pu, J. Z.; Schaefer, M.; Tidor, B.; Venable, R. M.; Woodcock, H. L.; Wu, X.; Yang, W.; York, D. M.; Karplus, M. CHARMM: the biomolecular simulation program. J. Comput. Chem. 2009, 30, 1545-614. 26. Im, W.; Lee, M. S.; Brooks, C. L., 3rd. Generalized born model with a simple smoothing function. J. Comput. Chem. 2003, 24, 1691-702. 27. Li, M. Y.; Fang, H.; Du, L. P.; Xia, L.; Wang, B. H. Computational studies of the binding site of α1A-adrenoceptor antagonists. J. Mol. Model. 2008, 14, 957-966. 28. Jo, S.; Kim, T.; Iyer, V. G.; Im, W. CHARMM-GUI: a web-based graphical user interface for CHARMM. J. Comput. Chem. 2008, 29, 1859-65. 29. MacKerell, A. D.; Bashford, D.; Bellott, M.; Dunbrack, R. L.; Evanseck, J. D.; Field, M. J.; Fischer, S.; Gao, J.; Guo, H.; Ha, S.; Joseph-McCarthy, D.; Kuchnir, L.; Kuczera, K.; Lau, F. T. K.; Mattos, C.; Michnick, S.; Ngo, T.; Nguyen, D. T.; Prodhom, B.; Reiher, W. E.; Roux, B.; Schlenkrich, M.; Smith, J. C.; Stote, R.; Straub, J.; Watanabe, M.; Wiorkiewicz-Kuczera, J.; Yin, D.; Karplus, M. All-Atom Empirical Potential for Molecular Modeling and Dynamics Studies of Proteins. J. Phys. Chem. B 1998, 102, 3586-3616. 30. Ryckaert, J.-P.; Ciccotti, G.; Berendsen, H. J. C. Numerical integration of the cartesian equations of motion of a system with constraints: molecular dynamics of n-alkanes. J. Comput. Phys. 1977, 23, 327-341. 31. McDonald, I. K.; Thornton, J. M. Satisfying hydrogen bonding potential in proteins. J Mol Biol 1994, 238, 777-93. 32. Wallace, A. C.; Laskowski, R. A.; Thornton, J. M. LIGPLOT: a program to generate schematic diagrams of protein-ligand interactions. Protein Eng 1995, 8, 127-34. 33. DeLano, W. L. The PyMOL Molecular Graphics System, 0.99; DeLano Scientific, San Carlos, CA, USA: 2006. 34. Yang, Q.; Du, L. P.; Wang, X. J.; Li, M. Y.; You, Q. D. Modeling the binding modes of Kv1.5 potassium channel and blockers. J. Mol. Graph. Model. 2008, 27, 178-187. 35. Yang, Q.; Du, L. P.; Wang, X. J.; Li, M. Y.; You, Q. D. Pharmacophore Mapping for Kv1.5 Potassium Channel Blockers. QSAR Comb. Sci. 2009, 28, 59-71. 36. Clement, O. O.; Mehl, A. T. HipHop: pharmacophores based on multiple common-feature alignments. In Pharmacophore Perception, Development & Use In Drug Design, International University Line: 2000; Vol. 2, pp 69-84. 37. Barnum, D.; Greene, J.; Smellie, A.; Sprague, P. Identification of common functional configurations among molecules. J. Chem. Inf. Comput. Sci. 1996, 36, 563-71. 糖原合成酶激酶-3β（GSK-3β）的非 ATP 竞争抑制剂药效团模型研究 楚勇 1 ，蔡耿彰 2 ，叶德泳 1 ，李敏勇 3,* 1 复旦大学药学院药化教研室，中国上海，201203 2 中央研究院基因体研究中心，台北市南港区研究院路二段128号，115 3山东大学药学院药物化学研究室，山东省济南市，250012 E-mail: cy110@fudan.edu.cn (Y. Chu), mli@sdu.edu.cn (M. Li) * 摘要： 糖原合成酶激酶－3(GSK-3) 在多个信号传导通路中起着重要的调节作用，其抑制剂可用于治疗糖尿病和阿 尔茨海默氏症等多种疾病，尤其是非 ATP 竞争型抑制剂选择性更高，毒副作用更小。本文选择了 4 个高活 性的非 ATP 竞争的 GSK-3β 抑制剂，运用 DOCK 5.4 对 GSK-3β 的晶体结构进行分子对接，在此基础上，采 用 Catalyst 4.11 建立了基于配体的药效团模型。该结果加深了这些化合物对 GSK-3β 非 ATP 结合口袋作 用机理的进一步认识，可用于活性分子的进一步设计和优化。 关键词 糖原合成酶激酶－3β(GSK-3β)，非ATP竞争抑制剂，分子对接，药效团模型 中国科技论文在线 http://www.paper.edu.cn