Advertisement

Application of Virtual Screening Approaches for the Identification of Small Molecule Inhibitors of the Methyllysine Reader Protein Spindlin1

  • Chiara Luise
  • Dina Robaa
Protocol
Part of the Methods in Molecular Biology book series (MIMB, volume 1824)

Abstract

Computer-based approaches represent a powerful tool which helps to identify and optimize lead structures in the process of drug discovery. Computer-aided drug design techniques (CADD) encompass a large variety of methods which are subdivided into structure-based (SBDD) and ligand-based drug design (LBDD) methods. Several approaches have been successfully used over the last three decades in different fields. Indeed also in the field of epigenetics, virtual screening (VS) studies and structure-based approaches have been applied to identify novel chemical modulators of epigenetic targets as well as to predict the binding mode of active ligands and to study the protein dynamics.

In this chapter, an iterative VS approach using both SBDD and LBDD methods, which was successful in identifying Spindlin1 inhibitors, will be described. All protocol steps, starting from structure-based pharmacophore modeling, protein and database preparation along with docking and similarity search, will be explained in details.

Key words

Computer-aided drug design Virtual screening Structure-based pharmacophore Docking Database preparation Similarity search Spindlin1 Methyllysine reader proteins Epigenetics 

References

  1. 1.
    Russo VEA, Martienssen RA, Riggs AD (1996) Epigenetic mechanisms of gene regulation. Cold Spring Harbor Laboratory Press, Plainview, NY, p 692Google Scholar
  2. 2.
    Egger G, Liang G, Aparicio A, Jones PA (2004) Epigenetics in human disease and prospects for epigenetic therapy. Nature 429(6990):457–463. https://doi.org/10.1038/nature02625 CrossRefPubMedGoogle Scholar
  3. 3.
    Handy DE, Castro R, Loscalzo J (2011) Epigenetic modifications: basic mechanisms and role in cardiovascular disease. Circulation 123(19):2145–2156. https://doi.org/10.1161/CIRCULATIONAHA.110.956839 CrossRefPubMedPubMedCentralGoogle Scholar
  4. 4.
    Dawson MA, Kouzarides T (2012) Cancer epigenetics: from mechanism to therapy. Cell 150(1):12–27. https://doi.org/10.1016/j.cell.2012.06.013 CrossRefPubMedGoogle Scholar
  5. 5.
    Sharma S, Kelly TK, Jones PA (2010) Epigenetics in cancer. Carcinogenesis 31(1):27–36. https://doi.org/10.1093/carcin/bgp220 CrossRefGoogle Scholar
  6. 6.
    Lardenoije R, Iatrou A, Kenis G et al (2015) The epigenetics of aging and neurodegeneration. Prog Neurobiol 131:21–64. https://doi.org/10.1016/j.pneurobio.2015.05.002 CrossRefPubMedGoogle Scholar
  7. 7.
    Fogel O, Richard-Miceli C, Tost J (2017) Epigenetic changes in chronic inflammatory diseases. Adv Protein Chem Struct Biol 106:139–189. https://doi.org/10.1016/bs.apcsb.2016.09.003 CrossRefPubMedGoogle Scholar
  8. 8.
    Zhang Z, Zhang R (2015) Epigenetics in autoimmune diseases: pathogenesis and prospects for therapy. Autoimmun Rev 14(10):854–863. https://doi.org/10.1016/j.autrev.2015.05.008 CrossRefPubMedGoogle Scholar
  9. 9.
    Nuhrenberg T, Gilsbach R, Preissl S et al (2014) Epigenetics in cardiac development, function, and disease. Cell Tissue Res 356(3):585–600. https://doi.org/10.1007/s00441-014-1887-8 CrossRefPubMedGoogle Scholar
  10. 10.
    Jones PA, Issa JP, Baylin S (2016) Targeting the cancer epigenome for therapy. Nat Rev Genet 17(10):630–641. https://doi.org/10.1038/nrg.2016.93 CrossRefGoogle Scholar
  11. 11.
    Luger K, Mader AW, Richmond RK et al (1997) Crystal structure of the nucleosome core particle at 2.8 A resolution. Nature 389(6648):251–260. https://doi.org/10.1038/38444 CrossRefGoogle Scholar
  12. 12.
    Kornberg RD, Lorch Y (1999) Twenty-five years of the nucleosome, fundamental particle of the eukaryote chromosome. Cell 98(3):285–294. https://doi.org/10.1016/S0092-8674(00)81958-3 CrossRefPubMedGoogle Scholar
  13. 13.
    Cosgrove MS, Boeke JD, Wolberger C (2004) Regulated nucleosome mobility and the histone code. Nat Struct Mol Biol 11(11):1037–1043. https://doi.org/10.1038/nsmb851 CrossRefPubMedGoogle Scholar
  14. 14.
    Rothbart SB, Strahl BD (2014) Interpreting the language of histone and DNA modifications. Biochim Biophys Acta 1839(8):627–643. https://doi.org/10.1016/j.bbagrm.2014.03.001 CrossRefPubMedPubMedCentralGoogle Scholar
  15. 15.
    Huang H, Sabari BR, Garcia BA et al (2014) SnapShot: histone modifications. Cell 159(2):458–458.e1. https://doi.org/10.1016/j.cell.2014.09.037 CrossRefPubMedPubMedCentralGoogle Scholar
  16. 16.
    Musselman CA, Lalonde ME, Cote J et al (2012) Perceiving the epigenetic landscape through histone readers. Nat Struct Mol Biol 19(12):1218–1227. https://doi.org/10.1038/nsmb.2436 CrossRefPubMedPubMedCentralGoogle Scholar
  17. 17.
    Taverna SD, Li H, Ruthenburg AJ et al (2007) How chromatin-binding modules interpret histone modifications: lessons from professional pocket pickers. Nat Struct Mol Biol 14(11):1025–1040. https://doi.org/10.1038/nsmb1338 CrossRefPubMedPubMedCentralGoogle Scholar
  18. 18.
    Patel DJ, Wang Z (2013) Readout of epigenetic modifications. Annu Rev Biochem 82:81–118. https://doi.org/10.1146/annurev-biochem-072711-165700 CrossRefPubMedPubMedCentralGoogle Scholar
  19. 19.
    Brand M et al (2015) Small molecule inhibitors of bromodomain-acetyl-lysine interactions. ACS Chem Biol 10(1):22–39. https://doi.org/10.1021/cb500996u CrossRefPubMedGoogle Scholar
  20. 20.
    James LI, Barsyte-Lovejoy D, Zhong N, Krichevsky L et al (2013) Discovery of a chemical probe for the L3MBTL3 methyllysine reader domain. Nat Chem Biol 9(3):184–191. https://doi.org/10.1038/nchembio.1157 CrossRefPubMedPubMedCentralGoogle Scholar
  21. 21.
    James LI, Korboukh VK, Krichevsky L et al (2013) Small-molecule ligands of methyl-lysine binding proteins: optimization of selectivity for L3MBTL3. J Med Chem 56(18):7358–7371. https://doi.org/10.1021/jm400919p CrossRefPubMedGoogle Scholar
  22. 22.
    Herold JM, Wigle TJ, Norris JL et al (2011) Small-molecule ligands of methyl-lysine binding proteins. J Med Chem 54(7):2504–2511. https://doi.org/10.1021/jm200045v CrossRefPubMedPubMedCentralGoogle Scholar
  23. 23.
    Perfetti MT, Baughman BM, Dickson BM et al (2015) Identification of a fragment-like small molecule ligand for the methyl-lysine binding protein, 53BP1. ACS Chem Biol 10(4):1072–1081. https://doi.org/10.1021/cb500956g CrossRefPubMedPubMedCentralGoogle Scholar
  24. 24.
    Bae N, Viviano M, Su X et al (2017) Developing Spindlin1 small-molecule inhibitors by using protein microarrays. Nat Chem Biol 13(7):750–756. https://doi.org/10.1038/nchembio.2377 CrossRefPubMedPubMedCentralGoogle Scholar
  25. 25.
    Robaa D, Wagner T, Luise C et al (2016) Identification and structure-activity relationship studies of small-molecule inhibitors of the methyllysine reader protein Spindlin1. ChemMedChem 11(20):2327–2338. https://doi.org/10.1002/cmdc.201600362 CrossRefPubMedGoogle Scholar
  26. 26.
    Wagner T, Greschik H, Burgahn T et al (2016) Identification of a small-molecule ligand of the epigenetic reader protein Spindlin1 via a versatile screening platform. Nucleic Acids Res 44(9):e88. https://doi.org/10.1093/nar/gkw089 CrossRefPubMedPubMedCentralGoogle Scholar
  27. 27.
    Ren C, Morohashi K, Plotnikov AN et al (2015) Small-molecule modulators of methyl-lysine binding for the CBX7 chromodomain. Chem Biol 22(2):161–168. https://doi.org/10.1016/j.chembiol.2014 CrossRefPubMedPubMedCentralGoogle Scholar
  28. 28.
    Ren C, Smith SG, Kyoko Y et al (2016) Structure-guided discovery of selective antagonists for the chromodomain of polycomb repressive protein CBX7. ACS Med Chem Lett 7(6):601–605. https://doi.org/10.1021/acsmedchemlett.6b00042 CrossRefPubMedPubMedCentralGoogle Scholar
  29. 29.
    Wagner T, Robaa D, Sippl W et al (2014) Mind the methyl: methyllysine binding proteins in epigenetic regulation. ChemMedChem 9(3):466–483. https://doi.org/10.1002/cmdc.201300422 CrossRefPubMedGoogle Scholar
  30. 30.
    Milosevich N, Hof F (2016) Chemical inhibitors of epigenetic methyllysine reader proteins. Biochemistry 55(11):1570–1583. https://doi.org/10.1021/acs.biochem.5b01073 CrossRefPubMedGoogle Scholar
  31. 31.
    Teske KA, Hadden MK (2017) Methyllysine binding domains: structural insight and small molecule probe development. Eur J Med Chem 136:14–35. https://doi.org/10.1016/j.ejmech.2017.04.047 CrossRefPubMedGoogle Scholar
  32. 32.
    Andreoli F, Del Rio A (2015) Computer-aided molecular design of compounds targeting histone modifying enzymes. Comput Struct Biotechnol J 13:358–365. https://doi.org/10.1016/j.csbj.2015.04.007 CrossRefPubMedPubMedCentralGoogle Scholar
  33. 33.
    Medina-Franco JL (2016) Epi-informatics: discovery and development of small molecule epigenetic drugs and probes. Elsevier, Amsterdam, p 424Google Scholar
  34. 34.
    Kannan S, Melesina J, Hauser AT et al (2014) Discovery of inhibitors of Schistosoma mansoni HDAC8 by combining homology modeling, virtual screening, and in vitro validation. J Chem Inf Model 54(10):3005–3019. https://doi.org/10.1021/ci5004653 CrossRefPubMedGoogle Scholar
  35. 35.
    Bowers EM, Yan G, Mukherjee C et al (2010) Virtual ligand screening of the p300/CBP histone acetyltransferase: identification of a selective small molecule inhibitor. Chem Biol 17(5):471–482. https://doi.org/10.1016/j.chembiol.2010.03.006 CrossRefPubMedPubMedCentralGoogle Scholar
  36. 36.
    Pulla VK, Alvala M, Sriram DS et al (2014) Structure-based drug design of small molecule SIRT1 modulators to treat cancer and metabolic disorders. J Mol Graph Model 52:46–56. https://doi.org/10.1016/j.jmgm.2014.06.005 CrossRefPubMedGoogle Scholar
  37. 37.
    Pulla VK, Sriram DS, Viswanadha S et al (2016) Energy-based pharmacophore and three-dimensional quantitative structure-activity relationship (3D-QSAR) modeling combined with virtual screening to identify novel small-molecule inhibitors of silent mating-type information regulation 2 homologue 1 (SIRT1). J Chem Inf Model 56(1):173–187. https://doi.org/10.1021/acs.jcim.5b00220 CrossRefPubMedGoogle Scholar
  38. 38.
    Schiedel M, Rumpf T, Karaman B et al (2016) Structure-based development of an affinity probe for Sirtuin 2. Angew Chem Int Ed Engl 55(6):2252–2256. https://doi.org/10.1002/anie.201509843 CrossRefPubMedGoogle Scholar
  39. 39.
    Uciechowska U, Schemies J, Neugebauer RC et al (2008) Thiobarbiturates as sirtuin inhibitors: virtual screening, free-energy calculations, and biological testing. ChemMedChem 3(12):1965–1976. https://doi.org/10.1002/cmdc.200800104 CrossRefPubMedGoogle Scholar
  40. 40.
    Parenti MD, Grozio A, Bauer I et al (2014) Discovery of novel and selective SIRT6 inhibitors. J Med Chem 57(11):4796–4804. https://doi.org/10.1021/jm500487d CrossRefPubMedGoogle Scholar
  41. 41.
    Heinke R, Spannhoff A, Meier R et al (2009) Virtual screening and biological characterization of novel histone arginine methyltransferase PRMT1 inhibitors. ChemMedChem 4(1):69–77. https://doi.org/10.1002/cmdc.200800301 CrossRefPubMedGoogle Scholar
  42. 42.
    Spannhoff A, Heinke R, Bauer I et al (2007) Target-based approach to inhibitors of histone arginine methyltransferases. J Med Chem 50(10):2319–2325. https://doi.org/10.1021/jm061250e CrossRefPubMedGoogle Scholar
  43. 43.
    Roatsch M, Robaa D, Pippel M et al (2016) Substituted 2-(2-aminopyrimidin-4-yl)pyridine-4-carboxylates as potent inhibitors of JumonjiC domain-containing histone demethylases. Future Med Chem 8(13):1553–1571. https://doi.org/10.4155/fmc.15.188 CrossRefPubMedGoogle Scholar
  44. 44.
    Kireev D, Wigle TJ, Norris-Drouin J et al (2010) Identification of non-peptide malignant brain tumor (MBT) repeat antagonists by virtual screening of commercially available compounds. J Med Chem 53(21):7625–7631. https://doi.org/10.1021/jm1007374 CrossRefPubMedPubMedCentralGoogle Scholar
  45. 45.
    Wang W, Chen Z, Mao Z et al (2011) Nucleolar protein Spindlin1 recognizes H3K4 methylation and stimulates the expression of rRNA genes. EMBO Rep 12(11):1160–1166. https://doi.org/10.1038/embor.2011.184 CrossRefPubMedPubMedCentralGoogle Scholar
  46. 46.
    Yang N, Wang W, Wang Y et al (2012) Distinct mode of methylated lysine-4 of histone H3 recognition by tandem tudor-like domains of Spindlin1. Proc Natl Acad Sci U S A 109(44):17954–17959. https://doi.org/10.1073/pnas.1208517109 CrossRefPubMedPubMedCentralGoogle Scholar
  47. 47.
    Su X, Zhu G, Ding X et al (2014) Molecular basis underlying histone H3 lysine-arginine methylation pattern readout by Spin/Ssty repeats of Spindlin1. Genes Dev 28(6):622–636. https://doi.org/10.1101/gad.233239.113 CrossRefPubMedPubMedCentralGoogle Scholar
  48. 48.
    Shanle EK, Shinsky SA, Bridgers JB et al (2017) Histone peptide microarray screen of chromo and Tudor domains defines new histone lysine methylation interactions. Epigenetics Chromatin 10:12. https://doi.org/10.1186/s13072-017-0117-5 CrossRefPubMedPubMedCentralGoogle Scholar
  49. 49.
    Jiang F, Zhao Q, Qin L et al (2006) Expression, purification, crystallization and preliminary X-ray analysis of human spindlin1, an ovarian cancer-related protein. Protein Pept Lett 13(2):203–205. https://doi.org/10.2174/092986606775101661 CrossRefPubMedGoogle Scholar
  50. 50.
    Wang JX, Zeng Q, Chen L et al (2012) SPINDLIN1 promotes cancer cell proliferation through activation of WNT/TCF-4 signaling. Mol Cancer Res 10(3):326–335. https://doi.org/10.1158/1541-7786.MCR-11-0440 CrossRefPubMedGoogle Scholar
  51. 51.
    Franz H, Greschik H, Willmann D et al (2015) The histone code reader SPIN1 controls RET signaling in liposarcoma. Oncotarget 6(7):4773–4789. https://doi.org/10.18632/oncotarget.3000 CrossRefPubMedPubMedCentralGoogle Scholar
  52. 52.
    Drago-Ferrante R, Pentimalli F, Carlisi D et al (2017) Suppressive role exerted by microRNA-29b-1-5p in triple negative breast cancer through SPIN1 regulation. Oncotarget 8(17):28939–28958. https://doi.org/10.18632/oncotarget.15960 CrossRefPubMedPubMedCentralGoogle Scholar
  53. 53.
    Chen X, Wang YW, Xing AY et al (2016) Suppression of SPIN1-mediated PI3K-Akt pathway by miR-489 increases chemosensitivity in breast cancer. J Pathol 239(4):459–472. https://doi.org/10.1002/path.4743 CrossRefPubMedGoogle Scholar
  54. 54.
    Yuan H, Zhang P, Qin L et al (2008) Overexpression of SPINDLIN1 induces cellular senescence, multinucleation and apoptosis. Gene 410(1):67–74. https://doi.org/10.1016/j.gene.2007.11.019 CrossRefPubMedGoogle Scholar
  55. 55.
    Zhang P, Cong B, Yuan H et al (2008) Overexpression of spindlin1 induces metaphase arrest and chromosomal instability. J Cell Physiol 217(2):400–408. https://doi.org/10.1002/jcp.21515 CrossRefPubMedGoogle Scholar
  56. 56.
    Zhao Q, Qin L, Jiang F et al (2007) Structure of human spindlin1. Tandem tudor-like domains for cell cycle regulation. J Biol Chem 282(1):647–656. https://doi.org/10.1074/jbc.M604029200 CrossRefPubMedGoogle Scholar
  57. 57.
    Shoichet BK (2004) Virtual screening of chemical libraries. Nature 432(7019):862–865. https://doi.org/10.1038/nature03197 CrossRefPubMedPubMedCentralGoogle Scholar
  58. 58.
    Wermuth CG, Ganellin CR, Lindberg P et al (1998) Glossary of terms used in Medicinal Chemistry (IUPAC Recommendations 1998). Pure Appl Chem 70(5):1129–1143. https://doi.org/10.1351/pac199870051129 CrossRefGoogle Scholar
  59. 59.
    Vuorinen A, Schuster D (2015) Methods for generating and applying pharmacophore models as virtual screening filters and for bioactivity profiling. Methods 71:113–134. https://doi.org/10.1016/j.ymeth.2014.10.013 CrossRefPubMedGoogle Scholar
  60. 60.
    Wolber G, Langer T (2005) LigandScout: 3-D pharmacophores derived from protein-bound Ligands and their use as virtual screening filters. J Chem Inf Model 45(1):160–169. https://doi.org/10.1021/ci049885e CrossRefGoogle Scholar
  61. 61.
    Molecular Operating Environment (MOE), 2013.08; Chemical Computing Group ULC, 1010 Sherbooke St. West, Suite #910, Montreal, QC, Canada, H3A 2R7 2017Google Scholar
  62. 62.
    Dixon SL, Smondyrev AM, Knoll EH et al (2006) PHASE: a new engine for pharmacophore perception, 3D QSAR model development, and 3D database screening: 1. Methodology and preliminary results. J Comput Aided Mol Des 20(10–11):647–671. https://doi.org/10.1007/s10822-006-9087-6 CrossRefPubMedGoogle Scholar
  63. 63.
    Dixon SL, Smondyrev AM, Rao SN (2006) PHASE: a novel approach to pharmacophore modeling and 3D database searching. Chem Biol Drug Des 67(5):370–372. https://doi.org/10.1111/j.1747-0285.2006.00384.x CrossRefGoogle Scholar
  64. 64.
    Godden JW, Furr JR, Xue L et al (2004) Molecular similarity analysis and virtual screening by mapping of consensus positions in binary-transformed chemical descriptor spaces with variable dimensionality. J Chem Inf Comput Sci 44(1):21–29. https://doi.org/10.1021/ci0302963 CrossRefPubMedGoogle Scholar
  65. 65.
    Barnum D, Greene J, Smellie A, Sprague P (1996) Identification of common functional configurations among molecules. J Chem Inf Comput Sci 36(3):563–571. https://doi.org/10.1021/ci950273r CrossRefPubMedGoogle Scholar
  66. 66.
    Koes DR, Camacho CJ (2011) Pharmer: efficient and exact pharmacophore search. J Chem Inf Model 51(6):1307–1314. https://doi.org/10.1021/ci200097m CrossRefPubMedPubMedCentralGoogle Scholar
  67. 67.
    Cheng T, Li Q, Zhou Z et al (2012) Structure-based virtual screening for drug discovery: a problem-centric review. AAPS J 14(1):133–141. https://doi.org/10.1208/s12248-012-9322-0 CrossRefPubMedPubMedCentralGoogle Scholar
  68. 68.
    Cole JC et al (2011) The basis for target-based virtual screening: protein structures, in Virtual Screening Wiley-VCH Verlag GmbH p 87–114Google Scholar
  69. 69.
    Berman HM, Westbrook J, Feng Z et al (2000) The protein data bank. Nucleic Acids Res 28(1):235–242CrossRefPubMedPubMedCentralGoogle Scholar
  70. 70.
  71. 71.
    Wilks ES (1995) Polymer Nomenclature and Structure - a Comparison of Systems Used by Chemical Abstracts Service, the International Union of Pure and Applied Chemistry, Mdl-Information-Systems-Inc, and Dupont. Abstracts of Papers of the American Chemical Society, 210:27-CinfGoogle Scholar
  72. 72.
    Sali A, Blundell TL (1993) Comparative protein modelling by satisfaction of spatial restraints. J Mol Biol 234(3):779–815. https://doi.org/10.1006/jmbi.1993.1626 CrossRefGoogle Scholar
  73. 73.
    Biasini M, Bienert S, Waterhouse A et al (2014) SWISS-MODEL: modelling protein tertiary and quaternary structure using evolutionary information. Nucleic Acids Res 42(Web Server issue):W252–W258. https://doi.org/10.1093/nar/gku340 CrossRefPubMedPubMedCentralGoogle Scholar
  74. 74.
    Verdonk ML, Berdini V, Hartshorn MJ et al (2004) Virtual screening using protein-ligand docking: avoiding artificial enrichment. J Chem Inf Comput Sci 44(3):793–806. https://doi.org/10.1021/ci034289q CrossRefGoogle Scholar
  75. 75.
    Chen YC (2015) Beware of docking! Trends Pharmacol Sci 36(2):78–95. https://doi.org/10.1016/j.tips.2014 CrossRefGoogle Scholar
  76. 76.
    Friesner RA, Banks JL, Murphy RB et al (2004) Glide: a new approach for rapid, accurate docking and scoring. 1. Method and assessment of docking accuracy. J Med Chem 47(7):1739–1749. https://doi.org/10.1021/jm0306430 CrossRefPubMedGoogle Scholar
  77. 77.
    Halgren TA, Murphy RB, Friesner RA et al (2004) Glide: a new approach for rapid, accurate docking and scoring. 2. Enrichment factors in database screening. J Med Chem 47(7):1750–1759. https://doi.org/10.1021/jm030644s CrossRefGoogle Scholar
  78. 78.
    Small-Molecule Drug Discovery Suite 2014–1 (2014) Glide, version 6.2, Schrödinger, LLC, New York, NYGoogle Scholar
  79. 79.
    Jones G, Willett P, Glen RC et al (1997) Development and validation of a genetic algorithm for flexible docking. J Mol Biol 267(3):727–748. https://doi.org/10.1006/jmbi.1996.0897 CrossRefGoogle Scholar
  80. 80.
    Irwin JJ, Shoichet BK, Mysinger MM et al (2009) Automated docking screens: a feasibility study. J Med Chem 52(18):5712–5720. https://doi.org/10.1021/jm9006966 CrossRefPubMedPubMedCentralGoogle Scholar
  81. 81.
    Trott O, Olson AJ (2010) AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J Comput Chem 31(2):455–461. https://doi.org/10.1002/jcc.21334 CrossRefPubMedPubMedCentralGoogle Scholar
  82. 82.
    Morris GM, Huey R, Lindstrom W et al (2009) AutoDock4 and AutoDockTools4: automated docking with selective receptor flexibility. J Comput Chem 30(16):2785–2791. https://doi.org/10.1002/jcc.21256 CrossRefPubMedPubMedCentralGoogle Scholar
  83. 83.
    Lill M (2013) Virtual screening in drug design. In: Kortagere S (ed) In silico models for drug discovery. Humana Press, Totowa, NJ, pp 1–12CrossRefGoogle Scholar
  84. 84.
    Eckert H, Bajorath J (2007) Molecular similarity analysis in virtual screening: foundations, limitations and novel approaches. Drug Discov Today 12(5–6):225–233. https://doi.org/10.1016/j.drudis.2007.01.011 CrossRefPubMedGoogle Scholar
  85. 85.
    Durant JL, Leland BA, Henry DR et al (2002) Reoptimization of MDL keys for use in drug discovery. J Chem Inf Comput Sci 42(6):1273–1280. https://doi.org/10.1021/ci010132r CrossRefPubMedGoogle Scholar
  86. 86.
    Irwin JJ, Sterling T, Mysinger MM et al (2012) ZINC: a free tool to discover chemistry for biology. J Chem Inf Model 52(7):1757–1768. https://doi.org/10.1021/ci3001277 CrossRefPubMedPubMedCentralGoogle Scholar
  87. 87.
    Sterling T, Irwin JJ (2015) ZINC 15-ligand discovery for everyone. J Chem Inf Model 55(11):2324–2337. https://doi.org/10.1021/acs.jcim.5b00559 CrossRefPubMedPubMedCentralGoogle Scholar
  88. 88.
  89. 89.
  90. 90.
    Groom CR, Bruno IJ, Lightfoot MP, Ward SC (2016) The Cambridge structural database. Acta Crystallogr B 72(2):171–179. https://doi.org/10.1107/S2052520616003954 CrossRefGoogle Scholar
  91. 91.
  92. 92.
  93. 93.
  94. 94.
    Lipinski CA, Lombardo F, Dominy BW, Feeney PJ (2001) Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv Drug Deliv Rev 46(1–3):3–26. https://doi.org/10.1016/S0169-409X(00)00129-0 CrossRefPubMedGoogle Scholar
  95. 95.
    Oprea TI (2000) Property distribution of drug-related chemical databases. J Comput Aided Mol Des 14(3):251–264. https://doi.org/10.1023/A:1008130001697 CrossRefPubMedGoogle Scholar
  96. 96.
    Baell J, Walters MA (2014) Chemistry: chemical con artists foil drug discovery. Nature 513(7519):481–483. https://doi.org/10.1038/513481a CrossRefPubMedGoogle Scholar
  97. 97.
    Baell JB, Holloway GA (2010) New substructure filters for removal of pan assay interference compounds (PAINS) from screening libraries and for their exclusion in bioassays. J Med Chem 53(7):2719–2740. https://doi.org/10.1021/jm901137j CrossRefGoogle Scholar
  98. 98.
    Dahlin JL, Inglese J, Walters MA (2015) Mitigating risk in academic preclinical drug discovery. Nat Rev Drug Discov 14(4):279–294. https://doi.org/10.1038/nrd4578 CrossRefPubMedPubMedCentralGoogle Scholar
  99. 99.
    Hawkins PCD, Skillman AG, Warren GL et al (2010) Conformer generation with OMEGA: algorithm and validation using high quality structures from the protein databank and Cambridge structural database. J Chem Inf Model 50(4):572–584. https://doi.org/10.1021/ci100031x CrossRefPubMedPubMedCentralGoogle Scholar
  100. 100.
    Meier R, Pippel M, Brandt F et al (2010) ParaDockS: a framework for molecular docking with population-based metaheuristics. J Chem Inf Model 50(5):879–889. https://doi.org/10.1021/ci900467x CrossRefPubMedGoogle Scholar
  101. 101.
    Corbeil CR, Williams CI, Labute P (2012) Variability in docking success rates due to dataset preparation. J Comput Aided Mol Des 26(6):775–786. https://doi.org/10.1007/s10822-012-9570-1 CrossRefPubMedPubMedCentralGoogle Scholar
  102. 102.
    Schrödinger Release 2014–2 (2014) Protein Preparation Wizard, Epik version 2.8, Impact version 6.3, Prime version 3.6; Schrödinger, LLC, New York, NYGoogle Scholar
  103. 103.
    Sastry GM, Adzhigirey M, Day T et al (2013) Protein and ligand preparation: parameters, protocols, and influence on virtual screening enrichments. J Comput Aided Mol Des 27(3):221–234. https://doi.org/10.1007/s10822-013-9644-8 CrossRefPubMedGoogle Scholar
  104. 104.
    Schrödinger Release 2014–1 (2014) Epik, version 2.8, Schrödinger, LLC, New York, NYGoogle Scholar
  105. 105.
    Greenwood JR, Calkins D, Sullivan AP, Shelley JC (2010) Towards the comprehensive, rapid, and accurate prediction of the favorable tautomeric states of drug-like molecules in aqueous solution. J Comput Aided Mol Des 24(6–7):591–604. https://doi.org/10.1007/s10822-010-9349-1 CrossRefPubMedGoogle Scholar
  106. 106.
    Shelley JC, Cholleti A, Frye LL et al (2007) Epik: a software program for pK a prediction and protonation state generation for drug-like molecules. J Comput Aided Mol Des 21(12):681–691. https://doi.org/10.1007/s10822-007-9133-z CrossRefPubMedGoogle Scholar
  107. 107.
    Banks JL, Beard HS, Cao Y et al (2005) Integrated modeling program, applied chemical theory (IMPACT). J Comput Chem 26(16):1752–1780. https://doi.org/10.1002/jcc.20292 CrossRefPubMedPubMedCentralGoogle Scholar
  108. 108.
    Cereto-Massague A, Ojeda MJ, Valls C et al (2015) Molecular fingerprint similarity search in virtual screening. Methods 71:58–63. https://doi.org/10.1016/j.ymeth.2014.08.005 CrossRefPubMedGoogle Scholar
  109. 109.
    Muegge I, Mukherjee P (2016) An overview of molecular fingerprint similarity search in virtual screening. Expert Opin Drug Discov 11(2):137–148. https://doi.org/10.1517/17460441.2016.1117070 CrossRefPubMedGoogle Scholar
  110. 110.
    Schrödinger Release 2014–1 (2014) Canvas, version 1.9, Schrödinger, LLC, New York, NYGoogle Scholar
  111. 111.
    Sherman W, Day T, Jacobson MP et al (2006) Novel procedure for modeling ligand/receptor induced fit effects. J Med Chem 49(2):534–553. https://doi.org/10.1021/jm050540c CrossRefGoogle Scholar
  112. 112.
    Osterberg F, Morris GM, Sanner MF et al (2002) Automated docking to multiple target structures: incorporation of protein mobility and structural water heterogeneity in AutoDock. Proteins 46(1):34–40. https://doi.org/10.1002/prot.10028 CrossRefPubMedGoogle Scholar
  113. 113.
    Genheden S, Ryde U (2015) The MM/PBSA and MM/GBSA methods to estimate ligand-binding affinities. Exp Opin Drug Discov 10(5):449–461. https://doi.org/10.1517/17460441.2015.1032936 CrossRefGoogle Scholar
  114. 114.
    Case DA, Cheatham TE III, Darden TA, Duke RE, Giese TJ, Gohlke H et al (2017) AMBER 2017. University of California, San Francisco, CAGoogle Scholar
  115. 115.
    Schrödinger Release 2017–1 (2017) Prime, Schrödinger, LLC, New York, NYGoogle Scholar
  116. 116.
    Spitzer GM, Heiss M, Mangold M et al (2010) One concept, three implementations of 3D pharmacophore-based virtual screening: distinct coverage of chemical search space. J Chem Inf Model 50(7):1241–1247. https://doi.org/10.1021/ci100136b CrossRefPubMedGoogle Scholar
  117. 117.
    Mysinger MM, Carchia M, Irwin JJ et al (2012) Directory of useful decoys, enhanced (DUD-E): better ligands and decoys for better benchmarking. J Med Chem 55(14):6582–6594. https://doi.org/10.1021/jm300687e CrossRefPubMedPubMedCentralGoogle Scholar
  118. 118.
    Cereto-Massague A, Guasch L, Valls C et al (2012) DecoyFinder: an easy-to-use python GUI application for building target-specific decoy sets. Bioinformatics 28(12):1661–1662. https://doi.org/10.1093/bioinformatics/bts249 CrossRefPubMedGoogle Scholar
  119. 119.
    Graves AP, Brenk R, Shoichet BK (2005) Decoys for docking. J Med Chem 48(11):3714–3728. https://doi.org/10.1021/jm0491187 CrossRefPubMedPubMedCentralGoogle Scholar
  120. 120.
    Wallach I, Lilien R (2011) Virtual decoy sets for molecular docking benchmarks. J Chem Inf Model 51(2):196–202. https://doi.org/10.1021/ci100374f CrossRefPubMedGoogle Scholar
  121. 121.
    Truchon JF, Bayly CI (2007) Evaluating virtual screening methods: good and bad metrics for the “early recognition” problem. J Chem Inf Model 47(2):488–508. https://doi.org/10.1021/ci600426e CrossRefGoogle Scholar
  122. 122.
    Clark RD, Webster-Clark DJ (2008) Managing bias in ROC curves. J Comput Aided Mol Des 22(3–4):141–146. https://doi.org/10.1007/s10822-008-9181-z CrossRefPubMedGoogle Scholar
  123. 123.
    Lagorce D, Oliveira N, Miteva MA, Villoutreix BO (2017) Pan-assay interference compounds (PAINS) that may not be too painful for chemical biology projects. Drug Discov Today 22(8):1131–1133. https://doi.org/10.1016/j.drudis.2017.05.017 CrossRefPubMedGoogle Scholar
  124. 124.
    Capuzzi SJ, Muratov EN, Tropsha A (2017) Phantom PAINS: problems with the utility of alerts for pan-assay INterference CompoundS. J Chem Inf Model 57(3):417–427. https://doi.org/10.1021/acs.jcim.6b00465 CrossRefPubMedPubMedCentralGoogle Scholar
  125. 125.
    Senger MR et al (2016) Filtering promiscuous compounds in early drug discovery: is it a good idea? Drug Discov Today 21(6):868–872. https://doi.org/10.1016/j.drudis.2016.02.004 CrossRefPubMedGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Pharmaceutical ChemistryMartin-Luther University of Halle-WittenbergHalle/SaaleGermany

Personalised recommendations