Protocol for Fragment Hopping

Protocol
Part of the Methods in Molecular Biology book series (MIMB, volume 1289)

Abstract

Fragment hopping is a fragment-based approach to designing biologically active small molecules. The key of this approach is the determination of the minimal pharmacophoric elements in the three-dimensional space. Based on the derived minimal pharmacophoric elements, new fragments with different chemotypes can be generated and positioned to the active site of the target protein. Herein, we detail a protocol for performing fragment hopping. This approach can not only explore a wide chemical space to produce new ligands with novel scaffolds but also characterize and utilize the delicate differences in the active sites between isofunctional proteins to produce new ligands with high target selectivity/specificity.

Key words

Fragment-based drug discovery Fragment hopping Scaffold diversity Isofunctional proteins Protein–protein interactions Inhibitors Selectivity Peptidomimetics 

References

  1. 1.
    Murray CW, Rees DC (2009) The rise of fragment-based drug discovery. Nat Chem 1:187–192CrossRefPubMedGoogle Scholar
  2. 2.
    Davis BJ, Erlanson DA (2013) Learning from our mistakes: the ‘unknown knowns’ in fragment screening. Bioorg Med Chem Lett 23:2844–2852CrossRefPubMedGoogle Scholar
  3. 3.
    Shuker SB, Hajduk PJ, Meadows RP, Fesik SW (1996) Discovering high-affinity ligands for proteins: SAR by NMR. Science 274:1531–1534CrossRefPubMedGoogle Scholar
  4. 4.
    Hajduk PJ, Gerfin T, Boehlen JM, Häberli M, Marek D, Fesik SW (1999) High-throughput nuclear magnetic resonance-based screening. J Med Chem 42:2315–2317CrossRefPubMedGoogle Scholar
  5. 5.
    Peng JW, Lepre CA, Fejzo J, Abdul-Manan N, Moore JM (2002) Nuclear magnetic resonance-based approaches for lead generation in drug discovery. Methods Enzymol 338:202–230CrossRefGoogle Scholar
  6. 6.
    Mayer M, Meyer B (2001) Group epitope mapping by saturation transfer difference NMR to identify segments of a ligand in direct contact with a protein receptor. J Am Chem Soc 123:6108–6117CrossRefPubMedGoogle Scholar
  7. 7.
    Dalvit C, Fogliatto G, Stewart A, Veronesi M, Stockman B (2001) WaterLOGSY as a method for primary NMR screening: practical aspects and range of applicability. J Biomol NMR 21:349–359CrossRefPubMedGoogle Scholar
  8. 8.
    Mattos C, Ringe D (1996) Locating and characterizing binding sites on proteins. Nat Biotechnol 14:595–599CrossRefPubMedGoogle Scholar
  9. 9.
    Lesuisse D, Lange G, Deprez P, Bénard D, Schoot B, Delettre G, Marquette JP, Broto P, Jean-Baptiste V, Bichet P, Sarubbi E, Mandine E (2002) SAR and X-ray. A new approach combining fragment-based screening and rational drug design: application to the discovery of nanomolar inhibitors of Src SH2. J Med Chem 45:2379–2387CrossRefPubMedGoogle Scholar
  10. 10.
    Hartshorn MJ, Murray CW, Cleasby A, Frederickson M, Tickle IJ, Jhoti H (2005) Fragment-based lead discovery using X-ray crystallography. J Med Chem 48:403–413CrossRefPubMedGoogle Scholar
  11. 11.
    Ciulli A, Williams G, Smith AG, Blundell TL, Abell C (2006) Probing hot spots at protein–ligand binding sites: a fragment-based approach using biophysical methods. J Med Chem 49:4992–5000CrossRefPubMedGoogle Scholar
  12. 12.
    Erlanson DA, Braisted AC, Raphael DR, Randal M, Stroud RM, Gordon EM, Wells JA (2000) Site-directed ligand discovery. Proc Natl Acad Sci U S A 97:9367–9372CrossRefPubMedCentralPubMedGoogle Scholar
  13. 13.
    Seth PP, Miyaji A, Jefferson EA, Sannes-Lowery KA, Osgood SA, Propp SS, Ranken R, Massire C, Sampath R, Ecker DJ, Swayze EE, Griffey RH (2005) SAR by MS: discovery of a new class of RNA-binding small molecules for the hepatitis C virus: internal ribosome entry site IIA subdomain. J Med Chem 48:7099–7102CrossRefPubMedGoogle Scholar
  14. 14.
    Nordström H, Gossas T, Hämäläinen M, Källblad P, Nyström S, Wallberg H, Danielson UH (2008) Identification of MMP-12 inhibitors by using biosensor-based screening of a fragment library. J Med Chem 51:3449–3459CrossRefPubMedGoogle Scholar
  15. 15.
    de Kloe GE, Retra K, Geitmann M, Källblad P, Nahar T, van Elk R, Smit AB, van Muijlwijk-Koezen JE, Leurs R, Irth H, Danielson UH, de Esch IJP (2010) Surface plasmon resonance biosensor based fragment screening using acetylcholine binding protein identifies ligand efficiency hot spots (LE hot spots) by deconstruction of nicotinic acetylcholine receptor α7 ligands. J Med Chem 53:7192–7201CrossRefPubMedGoogle Scholar
  16. 16.
    Christopher JA, Brown J, Doré AS, Errey JC, Koglin M, Marshall FH, Myszka DG, Rich RL, Tate CG, Tehan B, Warne T, Congreve M (2013) Biophysical fragment screening of the β1-adrenergic receptor: identification of high affinity arylpiperazine leads using structure-based drug design. J Med Chem 56:3446–3455CrossRefPubMedCentralPubMedGoogle Scholar
  17. 17.
    Hesterkamp T, Barker J, Davenport A, Whittaker M (2007) Fragment based drug discovery using fluorescence correlation: spectroscopy techniques: challenges and solutions. Curr Top Med Chem 7:1582–1591CrossRefPubMedGoogle Scholar
  18. 18.
    Barker JJ, Barker O, Boggio R, Chauhan V, Cheng RK, Corden V, Courtney SM, Edwards N, Falque VM, Fusar F, Gardiner M, Hamelin EM, Hesterkamp T, Ichihara O, Jones RS, Mather O, Mercurio C, Minucci S, Montalbetti CA, Müller A, Patel D, Phillips BG, Varasi M, Whittaker M, Winkler D, Yarnold CJ (2009) Fragment-based identification of Hsp90 inhibitors. ChemMedChem 4:963–966CrossRefPubMedGoogle Scholar
  19. 19.
    Ganesan A (1998) Strategies for the dynamic integration of combinatorial synthesis and screening. Angew Chem Int Ed 37:2828–2831CrossRefGoogle Scholar
  20. 20.
    Lehn J-M, Eliseev AV (2001) Dynamic combinatorial chemistry. Science 291:2331–2332CrossRefPubMedGoogle Scholar
  21. 21.
    Ramström O, Lehn J–M (2002) Drug discovery by dynamic combinatorial libraries. Nat Rev Drug Discov 1:26–36CrossRefPubMedGoogle Scholar
  22. 22.
    Erlanson DA, Lam JW, Wiesmann C, Luong TN, Simmons RL, DeLano WL, Choong IC, Burdett MT, Flanagan WM, Lee D, Gordon EM, O'Brien T (2003) In situ assembly of enzyme inhibitors using extended tethering. Nat Biotechnol 21:308–314CrossRefPubMedGoogle Scholar
  23. 23.
    Choong IC, Lew W, Lee D, Pham P, Burdett MT, Lam JW, Wiesmann C, Luong TN, Fahr B, DeLano WL, McDowell RS, Allen DA, Erlanson DA, Gordon EM, O'Brien T (2002) Identification of potent and selective small-molecule inhibitors of caspase-3 through the use of extended tethering and structure-based drug design. J Med Chem 45:5005–5022CrossRefPubMedGoogle Scholar
  24. 24.
    Lewis WG, Green LG, Grynszpan F, Radić Z, Carlier PR, Taylor P, Finn MG, Sharpless KB (2002) Click chemistry in situ: acetylcholinesterase as a reaction vessel for the selective assembly of a femtomolar inhibitor from an array of building blocks. Angew Chem Int Ed 41:1053–1057CrossRefGoogle Scholar
  25. 25.
    Krasiński A, Radić Z, Manetsch R, Raushel J, Taylor P, Sharpless KB, Kolb HC (2005) In situ selection of lead compounds by click chemistry: target-guided optimization of acetylcholinesterase inhibitors. J Am Chem Soc 127:6686–6692CrossRefPubMedGoogle Scholar
  26. 26.
    Whiting M, Muldoon J, Lin YC, Silverman SM, Lindstrom W, Olson AJ, Kolb HC, Finn MG, Sharpless KB, Elder JH, Fokin VV (2006) Inhibitors of HIV-1 protease by using in situ click chemistry. Angew Chem Int Ed 45:1435–1439CrossRefGoogle Scholar
  27. 27.
    Babaoglu K, Shoichet BK (2006) Deconstructing fragment-based inhibitor discovery. Nat Chem Biol 2:720–723CrossRefPubMedGoogle Scholar
  28. 28.
    Barelier S, Pons J, Marcillat O, Lancelin J-M, Krimm I (2010) Fragment-based deconstruction of Bcl-xL inhibitors. J Med Chem 53:2577–2588CrossRefPubMedGoogle Scholar
  29. 29.
    Van Molle I, Thomann A, Buckley DL, So EC, Lang S, Crews CM, Ciulli A (2012) Dissecting fragment-based lead discovery at the von Hippel-Lindau protein:hypoxia inducible factor 1α protein-protein interface. Chem Biol 19:1300–1312CrossRefPubMedCentralPubMedGoogle Scholar
  30. 30.
    Krueger BA, Dietrich A, Baringhaus KH, Schneider G (2009) Scaffold-hopping potential of fragment-based de novo design: the chances and limits of variation. Comb Chem High Throughput Screen 12:383–396CrossRefPubMedGoogle Scholar
  31. 31.
    Sun H, Tawa G, Wallqvist A (2012) Classification of scaffold-hopping approaches. Drug Discov Today 17:310–324CrossRefPubMedCentralPubMedGoogle Scholar
  32. 32.
    Ji H, Stanton BZ, Igarashi J, Li H, Martásek P, Roman LJ, Poulos TL, Silverman RB (2008) Minimal pharmacophoric elements and fragment hopping, an approach directed at molecular diversity and isozyme selectivity. Design of selective neuronal nitric oxide synthase inhibitors. J Am Chem Soc 130:3900–3914CrossRefPubMedCentralPubMedGoogle Scholar
  33. 33.
    Ji H, Li H, Martásek P, Roman LJ, Poulos TL, Silverman RB (2009) Discovery of highly potent and selective inhibitors of neuronal nitric oxide synthase by fragment hopping. J Med Chem 52:779–797CrossRefPubMedCentralPubMedGoogle Scholar
  34. 34.
    Lin F-Y, Tseng YJ (2011) Structure-based fragment hopping for lead optimization using predocked fragment database. J Chem Inf Model 51:1703–1715CrossRefPubMedGoogle Scholar
  35. 35.
    Saluste G, Albarran MI, Alvarez RM, Rabal O, Ortega MA, Blanco C, Kurz G, Salgado A, Pevarello P, Bischoff JR, Pastor J, Oyarzabal J (2012) Fragment-hopping-based discovery of a novel chemical series of proto-oncogene PIM-1 kinase inhibitors. PLoS One 7:e45964CrossRefPubMedCentralPubMedGoogle Scholar
  36. 36.
    Yu B, Huang Z, Zhang M, Dillard DR, Ji H (2013) Rational design of small-molecule inhibitors for β-catenin/T-cell factor protein-protein interactions by bioisostere replacement. ACS Chem Biol 8:524–529CrossRefPubMedGoogle Scholar
  37. 37.
    De Luca L, Ferro S, Morreale F, Christ F, Debyser Z, Chimirri A, Gitto R (2013) Fragment hopping approach directed at design of HIV IN-LEDGF/p75 interaction inhibitors. J Enzyme Inhib Med Chem 28:1002–1009CrossRefPubMedGoogle Scholar
  38. 38.
    Goodford PJ (1985) A computational procedure for determining energetically favorable binding sites on biologically important macromolecules. J Med Chem 28:849–857CrossRefPubMedGoogle Scholar
  39. 39.
    von Itzstein M, Wu W-Y, Kok GB, Pegg MS, Dyason JC, Jin B, Van Phan T, Smythe ML, White HF, Oliver SW, Colman PM, Varghese JN, Ryan DM, Woods JM, Bethell RC, Hotham VJ, Cameron JM, Penn CR (1993) Rational design of potent sialidase-based inhibitors of influenza virus replication. Nature 363:418–423CrossRefGoogle Scholar
  40. 40.
    Halgren TA (2009) Identifying and characterizing binding sites and assessing druggability. J Chem Inf Model 49:377–389CrossRefPubMedGoogle Scholar
  41. 41.
    Miranker A, Karplus M (1991) Functionality maps of binding sites: a multiple copy simultaneous search method. Proteins 11:29–34CrossRefPubMedGoogle Scholar
  42. 42.
    Zoete V, Meuwly M, Karplus M (2005) Study of the insulin dimerization: binding free energy calculations and per-residue free energy decomposition. Proteins 61:79–93CrossRefPubMedGoogle Scholar
  43. 43.
    Momany FA, Rone R (1992) Validation of the general purpose QUANTA® 3.2/CHARMm® force field. J Comput Chem 13:888–900CrossRefGoogle Scholar
  44. 44.
    Kastenholz MA, Pastor M, Cruciani G, Haaksma EEJ, Fox T (2000) GRID/CPCA: a new computational tool to design selective ligands. J Med Chem 43:3033–3044CrossRefPubMedGoogle Scholar
  45. 45.
    Ji H, Li H, Flinspach M, Poulos TL, Silverman RB (2003) Computer modeling of selective regions in the active site of nitric oxide synthases: implication for the design of isoform-selective inhibitors. J Med Chem 46:5700–5711CrossRefPubMedGoogle Scholar
  46. 46.
    Brenke R, Kozakov D, Chuang G-Y, Beglov D, Hall D, Landon MR, Mattos C, Vajda S (2009) Fragment-based identification of druggable ‘hot spots’ of proteins using Fourier domain correlation techniques. Bioinformatics 25:621–627CrossRefPubMedCentralPubMedGoogle Scholar
  47. 47.
    Ngan CH, Bohnuud T, Mottarella SE, Beglov D, Villar EA, Hall DR, Kozakov D, Vajda S (2012) FTMAP: extended protein mapping with user-selected probe molecules. Nucleic Acids Res 40:W271–W275CrossRefPubMedCentralPubMedGoogle Scholar
  48. 48.
    Kortemme T, Kim DE, Baker D (2004) Computational alanine scanning of protein-protein interfaces. Sci STKE 2004(219):2Google Scholar
  49. 49.
    Koes D, Khoury K, Huang Y, Wang W, Bista M, Popowicz GM, Wolf S, Holak TA, Dömling A, Camacho CJ (2012) Enabling large-scale design, synthesis and validation of small molecule protein-protein antagonists. PLoS One 7:e32839CrossRefPubMedCentralPubMedGoogle Scholar
  50. 50.
    Koes DR, Camacho CJ (2012) Small-molecule inhibitor starting points learned from protein–protein interaction inhibitor structure. Bioinformatics 28:784–791CrossRefPubMedCentralPubMedGoogle Scholar
  51. 51.
    Koes DR, Camacho CJ (2012) PocketQuery: protein-protein interaction inhibitor starting points from protein-protein interaction structure. Nucleic Acids Res 40:W387–W392CrossRefPubMedCentralPubMedGoogle Scholar
  52. 52.
    Meireles LMC, Dömling AS, Camacho CJ (2010) ANCHOR: a web server and database for analysis of protein-protein interaction binding pockets for drug discovery. Nucleic Acids Res 38:W407–W411CrossRefPubMedCentralPubMedGoogle Scholar
  53. 53.
    Böhm H-J (1992) The computer program LUDI: a new method for the de novo design of enzyme inhibitors. J Comput Aided Mol Des 6:61–78CrossRefPubMedGoogle Scholar
  54. 54.
    Böhm H-J, Banner DW, Weber L (1999) Combinatorial docking and combinatorial chemistry: design of potent non-peptide thrombin inhibitors. J Comput Aided Mol Des 13:51–56CrossRefPubMedGoogle Scholar
  55. 55.
    Loving K, Salam NK, Sherman W (2009) Energetic analysis of fragment docking and application to structure-based pharmacophore hypothesis generation. J Comput Aided Mol Des 23:541–554CrossRefPubMedGoogle Scholar
  56. 56.
    Sándor M, Kiss R, Keseru GM (2010) Virtual fragment docking by Glide: a validation study on 190 protein-fragment complexes. J Chem Inf Model 50:1165–1172CrossRefPubMedGoogle Scholar
  57. 57.
    Morris GM, Huey R, Lindstrom W, Sanner MF, Belew RK, Goodsell DS, Olson AJ (2009) AutoDock4 and AutoDockTools4: automated docking with selective receptor flexibility. J Comput Chem 30:2785–2791CrossRefPubMedCentralPubMedGoogle Scholar
  58. 58.
    Gasteiger J, Marsili M (1980) Iterative partial equalization of orbital electronegativity – a rapid access to atomic charges. Tetrahedron 36:3219–3228CrossRefGoogle Scholar
  59. 59.
    Mooij WTM, Verdonk ML (2005) General and targeted statistical potentials for protein–ligand interactions. Proteins 61:272–287CrossRefPubMedGoogle Scholar
  60. 60.
    Korb O, Stützle T, Exner TE (2009) Empirical scoring functions for advanced protein–ligand docking with PLANTS. J Chem Inf Model 49:84–96CrossRefPubMedGoogle Scholar
  61. 61.
    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 46:3–26CrossRefPubMedGoogle Scholar
  62. 62.
    Veber DF, Johnson SR, Cheng H-Y, Smith BR, Ward KW, Kopple KD (2002) Molecular properties that influence the oral bioavailability of drug candidates. J Med Chem 45:2615–2623CrossRefPubMedGoogle Scholar
  63. 63.
    Norinder U, Haeberlein M (2002) Computational approaches to the prediction of the blood–brain distribution. Adv Drug Deliv Rev 54:291–313CrossRefPubMedGoogle Scholar
  64. 64.
    Clark DE (2003) In silico prediction of blood–brain barrier permeation. Drug Discov Today 8:927–933CrossRefPubMedGoogle Scholar
  65. 65.
    Morelli X, Bourgeas R, Roche P (2011) Chemical and structural lessons from recent successes in protein-protein interaction inhibition (2P2I). Curr Opin Chem Biol 15:475–481CrossRefPubMedGoogle Scholar
  66. 66.
    Cruciani G, Carosati E, De Boeck B, Ethirajulu K, Mackie C, Howe T, Vianello R (2005) MetaSite: understanding metabolism in human cytochromes from the perspective of the chemist. J Med Chem 48:6970–6979CrossRefPubMedGoogle Scholar
  67. 67.
    Wood WJL, Patterson AW, Tsuruoka H, Jain RK, Ellman JA (2005) Substrate activity screening: a fragment-based method for the rapid identification of nonpeptidic protease inhibitors. J Am Chem Soc 127:15521–15527CrossRefPubMedGoogle Scholar
  68. 68.
    Patterson AW, Wood WJL, Ellman JA (2007) Substrate activity screening (SAS): a general procedure for the preparation and screening of a fragment-based non-peptidic protease substrate library for inhibitor discovery. Nat Protoc 2:424–433CrossRefPubMedGoogle Scholar
  69. 69.
    Greene J, Kahn S, Savoj H, Sprague P, Teig S (1994) Chemical function queries for 3D database search. J Chem Inf Comput Sci 34:1297–1308CrossRefGoogle Scholar
  70. 70.
    Barnum D, Greene J, Smellie A, Sprague P (1996) Identification of common functional configurations among molecules. J Chem Inf Comput Sci 36:563–571CrossRefPubMedGoogle Scholar
  71. 71.
    Krovat EM, Langer T (2003) Non-peptide angiotensin II receptor antagonists: chemical feature based pharmacophore identification. J Med Chem 46:716–726CrossRefPubMedGoogle Scholar
  72. 72.
    Cottrell SJ, Gillet VJ, Taylor R, Wilton DJ (2004) Generation of multiple pharmacophore hypotheses using multiobjective optimisation techniques. J Comput Aided Mol Des 18:665–682CrossRefPubMedGoogle Scholar
  73. 73.
    Richmond NJ, Abrams CA, Wolohan PRN, Abrahamian E, Willett P, Clark RD (2006) GALAHAD: 1. Pharmacophore identification by hypermolecular alignment of ligands in 3D. J Comput Aided Mol Des 20:567–587CrossRefPubMedGoogle Scholar
  74. 74.
    Dixon SL, Smondyrev AM, Knoll EH, Rao SN, Shaw DE, Friesner RA (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 20:647–671CrossRefPubMedGoogle Scholar
  75. 75.
    Sastry GM, Dixon SL, Sherman W (2011) Rapid shape-based ligand alignment and virtual screening method based on atom/feature-pair similarities and volume overlap scoring. J Chem Inf Model 51:2455–2466CrossRefPubMedGoogle Scholar
  76. 76.
    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 45:160–169CrossRefPubMedGoogle Scholar
  77. 77.
    Cramer RD, Patterson DE, Bunce JD (1988) Comparative molecular field analysis (CoMFA). 1. Effect of shape on binding of steroids to carrier proteins. J Am Chem Soc 110:5959–5967CrossRefPubMedGoogle Scholar
  78. 78.
    Klebe G, Abraham U, Mietzner T (1994) Molecular similarity indices in a comparative analysis (CoMSIA) of drug molecules to correlate and predict their biological activity. J Med Chem 37:4130–4146CrossRefPubMedGoogle Scholar
  79. 79.
    Cruciani G, Watson KA (1994) Comparative molecular field analysis using GRID force-field and GOLPE variable selection methods in a study of inhibitors of glycogen phosphorylase b. J Med Chem 37:2589–2601CrossRefPubMedGoogle Scholar
  80. 80.
    Catana C (2009) Simple idea to generate fragment and pharmacophore descriptors and their implications in chemical informatics. J Chem Inf Model 49:543–548CrossRefPubMedGoogle Scholar
  81. 81.
    Cecchini M, Kolb P, Majeux N, Caflisch A (2004) Automated docking of highly flexible ligands by genetic algorithms: a critical assessment. J Comput Chem 25:412–422CrossRefPubMedGoogle Scholar
  82. 82.
    Huang D, Lüthi U, Kolb P, Cecchini M, Barberis A, Caflisch A (2006) In silico discovery of β-secretase inhibitors. J Am Chem Soc 128:5436–5443CrossRefPubMedGoogle Scholar
  83. 83.
    Coleman RG, Carchia M, Sterling T, Irwin JJ, Shoichet BK (2013) Ligand pose and orientational sampling in molecular docking. PLoS One 8:e75992CrossRefPubMedCentralPubMedGoogle Scholar
  84. 84.
    Verdonk ML, Giangreco I, Hall RJ, Korb O, Mortenson PN, Murray CW (2011) Docking performance of fragments and druglike compounds. J Med Chem 54:5422–5431CrossRefPubMedGoogle Scholar
  85. 85.
    Budin N, Majeux N, Caflisch A (2001) Fragment-based flexible ligand docking by evolutionary optimization. Biol Chem 382:1365–1372CrossRefPubMedGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  1. 1.Department of Chemistry, Center for Cell and Genome ScienceUniversity of UtahSalt Lake CityUSA

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