Dokument

Summary:
In the field of computer-aided drug design, docking is a computational tool, often used to evaluate the sterical and chemical complementarity between two molecules. This technique can be used to estimate the binding or non-binding of a small molecule to a protein binding site. The classical application of docking is to find those molecules within a large set of molecules that bind a certain target protein and modulate its biological activity. This setup can be considered as established for a single target protein. In contrast to this, the docking to multiple target structures offers new possible applications. It can be used, for example, to assess the binding profile of a ligand against a number of proteins. In this work, the applicability of docking is assessed in such a scenario where multiple target structures are used. The corresponding proteins mostly belong to the family of G protein-coupled receptors. This protein family is very large and numerous GPCRs have been identified as potential drug targets, explaining the their relevance in pharmaceutical research. The protein structures used herein have different relationships and thus represent different application scenarios. The first case study uses two structures belonging to different proteins. These proteins are CXCR3 and CXCR4, a pair of chemokine GPCRs. In this chapter, new ligands are identified that bind to these proteins and modulate their biological activity. More importantly, for each of these newly identified ligands it could be predicted using docking, whether this ligand binds only to one of the two target proteins or to both. This study proves the applicability of docking to identify ligands with tailored selectivity. In addition, these ligands show excellent binding affinities to their respective target or targets. In the following two studies, the docking to different structures of the same target protein is investigated. The first application aims at identifying ligands selective for either one of two isoforms of the zebrafish CXC receptor 4. Subsequently, multiple conformations of the chemokine receptor CCR5 are used to show that different starting structures can identify different ligands. Next to the plain identification of chemically new ligands, experimental hurdles to prove the biological activity of these molecules in a functional assay is discussed. These difficulties are based on the fact that docking evaluates the structural complementarity between molecules and protein structures rather than predicting the effect of these molecules on the proteins. In addition, GPCRs form a challenging set of target proteins, since their ligands can induce a variety of different effects. Finally, the general applicability of multi-target docking to a very large number of structures is investigated. For this evaluation, kinases are used as protein family since many more structures have been experimentally determined for these proteins compared to GPCRs as membrane proteins. First, using published experimental data, a dataset is created consisting of several hundred kinase structures and a set of small-molecule kinase inhibitors. This dataset is characterised by the availability of experimental binding data for each single kinase-inhibitor combination. These experimental data were subsequently compared to the docking results of each ligand into each single kinase structure. The results indicate that a reliable selectivity prediction for a ligand is highly demanding in such a large-scale setup and beyond current possibilities. However, it can be shown that the prediction accuracy of docking can be improved by normalising the docking scores over multiple ligands and proteins. Based on these findings, the idea of "protein decoys" is developed, which might in the future allow more accurate predictions of selectivity profiles using docking.

Bibliographie / References

1. Dror, RO, Green, HF, Valant, C, Borhani, DW, et al. Structural basis for modulation of a G protein-coupled receptor by allosteric drugs. Nature, 503(7475):295–299 (2013).
2. Arnold, K, Bordoli, L, Kopp, J, and Schwede, T. The SWISS-MODEL workspace: a web-based environment for protein structure homology mod- elling. Bioinformatics, 22(2):195–201 (2006).
3. Wood, ER, Truesdale, AT, McDonald, OB, Yuan, D, et al. A unique struc- ture for epidermal growth factor receptor bound to GW572016 (Lapatinib): Relationships among protein conformation, inhibitor off-rate, and receptor activity in tumor cells. Cancer Res, 64(18):6652–6659 (2004).
4. Gerard, C and Rollins, BJ. Chemokines and disease. Nat Immunol, 2(2):108–115 (2001).
5. Irwin, JJ and Shoichet, BK. ZINC–a free database of commercially avail- able compounds for virtual screening. J Chem Inf Model, 45(1):177–182 (2005).
6. Jacobson, MP, Kaminski, GA, Friesner, RA, and Rapp, CS. Force Field Validation Using Protein Side Chain Prediction. J Phys Chem B, 106(44):11673–11680 (2002).
7. Huang, N, Shoichet, BK, and Irwin, JJ. Benchmarking Sets for Molecular Docking. J Med Chem, 49(23):6789–6801 (2006).
8. Noble, MEM, Endicott, Ja, and Johnson, LN. Protein Kinase Inhibitors: Insights into Drug Design from Structure. Science, 303(5665):1800–1805 (2004).
9. Konagurthu, AS, Whisstock, JC, Stuckey, PJ, and Lesk, AM. MUSTANG: A Multiple Structural Alignment Algorithm. Proteins, 64:559 –574 (2006).
10. Endres, MJ, Clapham, PR, Marsh, M, Ahuja, M, et al. CD4-Independent Infection by HIV-2 Is Mediated by Fusin/CXCR4. Cell, 87(4):745–756 (1996).
11. Rodríguez, D, Brea, J, Loza, MI, and Carlsson, J. Structure-based discov- ery of selective serotonin 5-HT 1B receptor ligands. Structure, 22(8):1140– 1151 (2014).
12. Abad-Zapatero, C and Metz, JT. Ligand efficiency indices as guideposts for drug discovery. Drug Discov Today, 10(7):464–469 (2005).
13. Wallach, I, Jaitly, N, Nguyen, K, Schapira, M, et al. Normalizing molecular docking rankings using virtually generated decoys. J Chem Inf Model, 51(8):1817–1830 (2011).
14. Bamborough, P, Brown, MJ, Christopher, JA, Chung, CW, et al. Selectiv- ity of kinase inhibitor fragments. J Med Chem, 54(14):5131–5143 (2011).
15. Vroling, B, Sanders, M, Baakman, C, Borrmann, A, et al. GPCRDB: information system for G protein-coupled receptors. Nucleic Acids Res, 39(Database issue):D309–D319 (2011).
16. Bradford, Y, Conlin, T, Dunn, N, Fashena, D, et al. ZFIN: enhancements and updates to the Zebrafish Model Organism Database. Nucleic Acids Res, 39(Database issue):D822–D829 (2011).
17. Siekmann, AF, Standley, C, Fogarty, KE, Wolfe, SA, et al. Chemokine signaling guides regional patterning of the first embryonic artery. Genes Dev, 23(19):2272–2277 (2009).
18. Swinney, DC, Beavis, P, Chuang, KT, Zheng, Y, et al. A study of the molecular mechanism of binding kinetics and long residence times of hu- man CCR5 receptor small molecule allosteric ligands. Br J Pharmacol, 171(14):3364–3375 (2014).
19. Kondru, R, Zhang, J, Ji, C, Mirzadegan, T, et al. Molecular interactions of CCR5 with major classes of small-molecule anti-HIV CCR5 antagonists.
20. FDA U.S. Food And Drug Administration. 64, 70, 86
21. Bussmann, J, Wolfe, SA, and Siekmann, AF. Arterial-venous network formation during brain vascularization involves hemodynamic regulation of chemokine signaling. Development, 138(9):1717–1726 (2011).
22. Boldajipour, B, Doitsidou, M, Tarbashevich, K, Laguri, C, et al. Cxcl12 evolution–subfunctionalization of a ligand through altered interaction with the chemokine receptor. Development, 138(14):2909–2914 (2011).
23. Yin, J, Mobarec, JC, Kolb, P, and Rosenbaum, DM. Crystal structure of the human OX2 orexin receptor bound to the insomnia drug suvorexant. Nature, 519(7542):247–250 (2014).
24. Doitsidou, M, Reichman-Fried, M, Stebler, J, Köprunner, M, et al. Guid- ance of Primordial Germ Cell Migration by the Chemokine SDF-1. Cell, 111(5):647–659 (2002).
25. Krieger, E, Koraimann, G, and Vriend, G. Increasing the precision of com- parative models with YASARA NOVA–a self-parameterizing force field. Proteins, 47(3):393–402 (2002).
26. Pease, JE and Horuk, R. Chemokine receptor antagonists: part 2. Expert Opin Ther Pat, 19(2):199–221 (2009).
27. Princen, K, Hatse, S, Vermeire, K, Aquaro, S, et al. Inhibition of human immunodeficiency virus replication by a dual CCR5/CXCR4 antagonist. J Virol, 78(23):12996–13006 (2004).
28. Brooks, BR, Bruccoleri, RE, Olafson, BD, States, DJ, et al. CHARMM: A program for macromolecular energy, minimization, and dynamics calcu- lations. J Comput Chem, 4:187–217 (1983).
29. Acronyms ECFP extended-connectivity fingerprint. 47, 58
30. Acronyms Lys lysine. 60 NMR nuclear magnetic resonance. 43
31. Bernat, V, Admas, TH, Brox, R, Heinemann, FW, et al. Boronic acids as probes for investigation of allosteric modulation of the chemokine receptor CXCR3. ACS Chem Biol, 9(11):2664–2677 (2014).
32. Hall, SE, Mao, A, Nicolaidou, V, Finelli, M, et al. Elucidation of binding sites of dual antagonists in the human chemokine receptors CCR2 and CCR5. Mol Pharmacol, 75:1325–1336 (2009).
33. FBS fetal bovine serum. 44
34. GABA γ-Aminobutyric acid. 58 GDP guanosine diphosphate. 7, 45
35. Gln glutamine. 59, 60, 66–69
36. GPCR G protein-coupled receptor. 2
37. HBSS Hank's Balanced Salt Solution. 92
38. HEK human embryonic kidney. 44, 45
39. HEPES 4-(2-hydroxyethyl)-1-piperazineethanesulfonic acid. 45, 92
40. HIV human immunodeficiency virus. 17, 64, 86
41. IC 50 half maximal inhibitory concentration. 20, 39 ICL intracellular loop. 20
42. Ile isoleucine. 59, 60, 90
43. LDA lateral dorsal aorta. 50, 51, 57
44. Chen, YZ and Zhi, DG. Ligand -Protein inverse docking and its potential use in the computer search of protein targets of a small molecule. Proteins Struct Funct Genet, 43(2):217–226 (2001).
45. Dolinsky, TJ, Nielsen, JE, McCammon, JA, and Baker, NA. PDB2PQR: an automated pipeline for the setup of Poisson-Boltzmann electrostatics calculations. Nucleic Acids Res, 32(Web Server issue):W665–W667 (2004).
46. Peters, JU. Polypharmacology -Foe or friend? J Med Chem, 56(22):8955– 8971 (2013).
47. Hubbard, SR and Till, JH. Protein Tyrosine Kinase Structure and Func- tion. Annu Rev Biochem, 69:373–398 (2000).
48. Landrum, G. RDKit: Open-source chemoinformatics.
49. RMSD root-mean-square distance. 66, 69, 71
50. Olivera, A and Spiegel, S. Sphingosine kinase: A mediator of vital cellular functions. Prostaglandins Other Lipid Mediat, 64(1-4):123–134 (2001).
51. Maeda, K, Das, D, Ogata-Aoki, H, Nakata, H, et al. Structural and molecular interactions of CCR5 inhibitors with CCR5. J Biol Chem, 281(18):12688–12698 (2006).
52. White, JF, Noinaj, N, Shibata, Y, Love, J, et al. Structure of the agonist- bound neurotensin receptor. Nature, 490(7421):508–513 (2012).
53. Tachibana, K, Hirota, S, Iizasa, H, Yoshida, H, et al. The chemokine receptor CXCR4 is essential for vascularization of the gastrointestinal tract. Nature, 393(6685):591–594 (1998).
54. Manning, G, Whyte, DB, Martinez, R, Hunter, T, et al. The protein kinase complement of the human genome. Science, 298(5600):1912–1934 (2002).
55. Dorr, P, Westby, M, Dobbs, S, Griffin, P, et al. Maraviroc (UK-427,857), a potent, orally bioavailable, and selective small-molecule inhibitor of chemokine receptor CCR5 with broad-spectrum anti-human immunode- ficiency virus type 1 activity. Antimicrob Agents Chemother, 49(11):4721– 4732 (2005).
56. Yarden, Y and Sliwkowski, MX. Untangling the ErbB signalling network. Nat Rev Mol Cell Biol, 2(2):127–137 (2001).
57. Kruse, AC, Weiss, DR, Rossi, M, Hu, J, et al. Muscarinic receptors as model targets and antitargets for structure-based ligand discovery. Mol Pharmacol, 84(4):528–40 (2013).
58. Edgar, RC. MUSCLE: multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res, 32(5):1792–1797 (2004).
59. Gaulton, A, Bellis, LJ, Bento, AP, Chambers, J, et al. ChEMBL: a large-scale bioactivity database for drug discovery. Nucleic Acids Res, 40(D1):D1100–D1107 (2012).
60. The Uniprot Consortium. Activities at the Universal Protein Resource (UniProt). Nucleic Acids Res, 42(Database issue):D191–D198 (2014).
61. Finn, RD, Bateman, A, Clements, J, Coggill, P, et al. Pfam: The protein families database. Nucleic Acids Res, 42(D1):222–230 (2014).
62. Ku, H. Notes on the use of propagation of error formulas. J Res Natl Bur Stand Sect C Eng Instrum, 70C(4):263 (1966).
63. Shoichet, BK, Leach, AR, and Kuntz, ID. Ligand solvation in molecular docking. Proteins, 34(1):4–16 (1999).
64. Evers, A and Klebe, G. Ligand-supported homology modeling of G protein- coupled receptor sites: models sufficient for successful virtual screening. Angew Chem Int Ed Engl, 43(2):248–251 (2004).
65. Bernat, V, Heinrich, MR, Baumeister, P, Buschauer, A, et al. Synthesis and application of the first radioligand targeting the allosteric binding pocket of chemokine receptor CXCR3. ChemMedChem, 7(8):1481–1489 (2012).
66. Murakami, T, Kawada, K, Iwamoto, M, Akagami, M, et al. The role of CXCR3 and CXCR4 in colorectal cancer metastasis. Int J Cancer, 132(2):276–287 (2013).
67. Trott, O and Olson, AJ. AutoDock Vina: Improving the speed and ac- curacy of docking with a new scoring function, efficient optimization, and multithreading. J Comput Chem, 31(2):455–461 (2010).
68. Melo, F, Sánchez, R, and Šali, A. Statistical potentials for fold assessment. Protein Sci, 11(2):430–448 (2002).
69. Lovell, SC, Davis, IW, Arendall, WB, De Bakker, PIW, et al. Structure validation by Cα geometry: φ,ψ and Cβ deviation. Proteins Struct Funct Genet, 50(3):437–450 (2003).
70. Li, H, Robertson, AD, and Jensen, JH. Very fast empirical prediction and rationalization of protein pKa values. Proteins, 61(4):704–721 (2005).
71. Jacobs, MD, Caron, PR, and Hare, BJ. Classifying protein kinase struc- tures guides use of ligand-selectivity profiles to predict inactive conforma- tions: Structure of lck/imatinib complex. Proteins Struct Funct Genet, 70(4):1451–1460 (2008).
72. Shen, MY and Šali, A. Statistical potential for assessment and prediction of protein structures. Protein Sci, 15(11):2507–2524 (2006).
73. Kohler, RE, Comerford, I, Townley, S, Haylock-Jacobs, S, et al. Antago- nism of the chemokine receptors CXCR3 and CXCR4 reduces the pathology of experimental autoimmune encephalomyelitis. Brain Pathol, 18(4):504– 516 (2008).
74. Shoichet, BK and Kuntz, ID. Matching chemistry and shape in molecular docking. Protein Eng, 6(7):723–732 (1993).
75. Christopoulos, A and Kenakin, T. G protein-coupled receptor allosterism and complexing. Pharmacol Rev, 54(2):323–374 (2002).
76. Wan, PTC, Garnett, MJ, Roe, SM, Lee, S, et al. Mechanism of activation of the RAF-ERK signaling pathway by oncogenic mutations of B-RAF.
77. Jacobsson, M and Karlén, A. Ligand Bias of Scoring Functions in Structure-Based Virtual Screening. J Chem Inf Model, 46(3):1334–1343 (2006).
78. Irwin, JJ, Sterling, T, Mysinger, MM, Bolstad, ES, et al. ZINC: a free tool to discover chemistry for biology. J Chem Inf Model, 52(7):1757–1768 (2012).
79. Casey, FP, Pihan, E, and Shields, DC. Discovery of Small Molecule In- hibitors of Protein–Protein Interactions Using Combined Ligand and Tar- get Score Normalization. J Chem Inf Model, 49(12):2708–2717 (2009).
80. Olsson, MHM, Søndergaard, CR, Rostkowski, M, and Jensen, JH. PROPKA3: Consistent Treatment of Internal and Surface Residues in Em- pirical pKa Predictions. J Chem Theory Comput, 7(2):525–537 (2011).
81. Coan, KED and Shoichet, BK. Stoichiometry and physical chemistry of promiscuous aggregate-based inhibitors. J Am Chem Soc, 130(29):9606– 9612 (2008).
82. Vigers, GPA and Rizzi, JP. Multiple Active Site Corrections for Docking and Virtual Screening. J Med Chem, 47(1):80–89 (2004).
83. Köster, H, Craan, T, Brass, S, Herhaus, C, et al. A small nonrule of 3 com- patible fragment library provides high hit rate of endothiapepsin crystal structures with various fragment chemotypes. J Med Chem, 54(22):7784– 7796 (2011).
84. Sassano, MF, Doak, AK, Roth, BL, and Shoichet, BK. Colloidal aggre- gation causes inhibition of G protein-coupled receptors. J Med Chem, 56(6):2406–2414 (2013).
85. Bissantz, C, Kuhn, B, and Stahl, M. A medicinal chemist's guide to molec- ular interactions. J Med Chem, 53:5061–5084 (2010).
86. Junker, A, Schepmann, D, Yamaguchi, J, Itami, K, et al. Diverse modi- fications of the 4-methylphenyl moiety of TAK-779 by late-stage Suzuki- Miyaura cross-coupling. Org Biomol Chem, 12:177–186 (2014).
87. Blanpain, C, Migeotte, I, Lee, B, Vakili, J, et al. CCR5 binds multiple CC- chemokines: MCP-3 acts as a natural antagonist. Blood, 94(6):1899–1905 (1999).
88. Sabroe, I, Peck, MJ, Van Keulen, BJ, Jorritsma, A, et al. A small molecule antagonist of chemokine receptors CCR1 and CCR3. Potent inhibition of eosinophil function and CCR3-mediated HIV-1 entry. J Biol Chem, 275(34):25985–25992 (2000).
89. Nagasawa, T, Hirota, S, Tachibana, K, Takakura, N, et al. Defects of B- cell lymphopoiesis and bone-marrow myelopoiesis in mice lacking the CXC chemokine PBSF/SDF-1. Nature, 382(6592):635–638 (1996).
90. Thompson, AA, Liu, W, Chun, E, Katritch, V, et al. Structure of the noci- ceptin/orphanin FQ receptor in complex with a peptide mimetic. Nature, 485(7398):395–399 (2012).
91. Keiser, MJ, Roth, BL, Armbruster, BN, Ernsberger, P, et al. Relating protein pharmacology by ligand chemistry. Nat Biotechnol, 25(2):197–206 (2007).
92. Karaman, MW, Herrgard, S, Treiber, DK, Gallant, P, et al. A quantita- tive analysis of kinase inhibitor selectivity. Nat Biotechnol, 26(1):127–132 (2008).
93. Liu, Y and Gray, NS. Rational design of inhibitors that bind to inactive kinase conformations. Nat Chem Biol, 2(7):358–364 (2006).
94. Balkwill, F. Cancer and the chemokine network. Nat Rev Cancer, 4(7):540– 550 (2004).
95. Chothia, C and Lesk, AM. The relation between the divergence of sequence and structure in proteins. EMBO J, 5(4):823–826 (1986).
96. Rajagopal, S, Rajagopal, K, and Lefkowitz, RJ. Teaching old receptors new tricks: biasing seven-transmembrane receptors. Nat Rev Drug Discov, 9(5):373–386 (2010).
97. Cheung, J, Ginter, C, Cassidy, M, Franklin, MC, et al. Structural insights into mis-regulation of protein kinase A in human tumors. Proc Natl Acad Sci, 112(5):1374–1379 (2015).
98. Baba, M, Nishimura, O, Kanzaki, N, Okamoto, M, et al. A small-molecule, nonpeptide CCR5 antagonist with highly potent and selective anti-HIV-1 activity. Proc Natl Acad Sci U S A, 96(May):5698–5703 (1999).
99. Meng, EC, Shoichet, BK, and Kuntz, ID. Automated docking with grid- based energy evaluation. J Comp Chem, 13:505–524 (1992).
100. Sadowski, J, Rudolph, C, and Gasteiger, J. The generation of 3D models of host-guest complexes. Anal Chim Acta, 265(2):233–241 (1992).
101. Gasteiger, J, Rudolph, C, and Sadowski, J. Automatic generation of 3D- atomic coordinates for organic molecules. Tetrahedron Comput Methodol, 3(6):537–547 (1990).
102. Jacobson, MP, Friesner, RA, Xiang, Z, and Honig, B. On the Role of the Crystal Environment in Determining Protein Side-chain Conformations. J Mol Biol, 320(3):597–608 (2002).
103. Schmitt, S, Kuhn, D, and Klebe, G. A new method to detect related function among proteins independent of sequence and fold homology. J Mol Biol, 323(2):387–406 (2002).
104. Sali, A and Blundell, TL. Comparative Protein Modelling by Satisfaction of Spatial Restraints. J Mol Biol, 234:779–815 (1993).
105. Seibert, C, Ying, W, Gavrilov, S, Tsamis, F, et al. Interaction of small molecule inhibitors of HIV-1 entry with CCR5. Virology, 349:41 – 54 (2006).
106. Nygaard, R, Zou, Y, Dror, RO, Mildorf, TJ, et al. The dynamic process of β2-adrenergic receptor activation. Cell, 152(3):532–542 (2013).
107. Kenakin, T. Principles: Receptor Theory in Pharmacology. Trends Phar- macol Sci, 25(4):186–192 (2004).
108. Rosenkilde, MM, Benned-Jensen, T, Frimurer, TM, and Schwartz, TW. The minor binding pocket: a major player in 7TM receptor activation. Trends Pharmacol Sci, 31(12):567–574 (2010).

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