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Docking Methods for Structure-Based Library Design

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Book cover Chemical Library Design

Part of the book series: Methods in Molecular Biology ((MIMB,volume 685))

Abstract

The drug discovery process mainly relies on the experimental high-throughput screening of huge compound libraries in their pursuit of new active compounds. However, spiraling research and development costs and unimpressive success rates have driven the development of more rational, efficient, and cost-effective methods. With the increasing availability of protein structural information, advancement in computational algorithms, and faster computing resources, in silico docking-based methods are increasingly used to design smaller and focused compound libraries in order to reduce screening efforts and costs and at the same time identify active compounds with a better chance of progressing through the optimization stages. This chapter is a primer on the various docking-based methods developed for the purpose of structure-based library design. Our aim is to elucidate some basic terms related to the docking technique and explain the methodology behind several docking-based library design methods. This chapter also aims to guide the novice computational practitioner by laying out the general steps involved for such an exercise. Selected successful case studies conclude this chapter.

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References

  1. Mayr, L. M., Fuerst, P. (2008) The future of high-throughput screening. J Biomol Screen 13, 443–448.

    Article  PubMed  CAS  Google Scholar 

  2. Entzeroth, M. (2003) Emerging trends in high-throughput screening. Curr Opin Pharmacol 3, 522–529.

    Article  PubMed  CAS  Google Scholar 

  3. Schnecke, V., Bostrom, J. (2006) Computational chemistry-driven decision making in lead generation. Drug Discov Today 11, 43–50.

    Article  PubMed  CAS  Google Scholar 

  4. Boldt, G. E., Dickerson, T. J., Janda, K. D. (2006) Emerging chemical and biological approaches for the preparation of discovery libraries. Drug Discov Today 11, 143–148.

    Article  PubMed  CAS  Google Scholar 

  5. Bohacek, R. S., McMartin, C., Guida, W. C. (1996) The art and practice of structure-based drug design: a molecular modeling perspective. Med Res Rev 16, 3–50.

    Article  PubMed  CAS  Google Scholar 

  6. Walters, W. P., Stahl M. T., Murcko, M. A. (1998) Virtual screening – an overview. Drug Discov Today 3, 160–178.

    Article  CAS  Google Scholar 

  7. Phatak, S. S., Stephan, C. C., Cavasotto, C. N. (2009) High-throughput and in silico screenings in drug discovery. Expert Opin. Drug Discov 4, 947–959.

    Article  CAS  Google Scholar 

  8. Keseru, G. M., Makara, G. M. (2006) Hit discovery and hit-to-lead approaches. Drug Discov Today 11, 741–748.

    Article  PubMed  Google Scholar 

  9. Macarron, R. (2006) Critical review of the role of HTS in drug discovery. Drug Discov Today 11, 277–279.

    Article  PubMed  Google Scholar 

  10. Fox, S., Farr-Jones, S., Sopchak, L., Boggs, A., Nicely, H. W., Khoury, R., Biros, M. (2006) High-throughput screening: update on practices and success. J Biomol Screen 11, 864–869.

    Article  PubMed  Google Scholar 

  11. Keseru, G. M., Makara, G. M. (2009) The influence of lead discovery strategies on the properties of drug candidates. Nat Rev Drug Discov 8, 203–212.

    Article  PubMed  Google Scholar 

  12. Lipkin, M. J., Stevens, A. P., Livingstone, D. J., Harris, C. J. (2008) How large does a compound screening collection need to be? Comb Chem High Throughput Screening 11, 482–493.

    Article  CAS  Google Scholar 

  13. Nestler, H. P. (2005) Combinatorial chemistry and fragment screening – Two unlike siblings? Curr Drug Discov Tech 2, 1–12.

    Article  CAS  Google Scholar 

  14. Diller, D. J., Merz, K. M., Jr. (2001) High throughput docking for library design and library prioritization. Proteins 43, 113–124.

    Article  PubMed  CAS  Google Scholar 

  15. Decornez, H., Gulyas-Forro, A., Papp, A., Szabo, M., Sarmay, G., Hajdu, I., Cseh, S., Dorman, G., Kitchen, D. B. (2009) Design, selection, evaluation of a general kinase-focused library. ChemMedChem 4, 1273–1278.

    Article  PubMed  CAS  Google Scholar 

  16. Lipinski, C. A. (2000) Drug-like properties and the causes of poor solubility and poor permeability. J Pharmacol Toxicol Methods 44, 235–249.

    Article  PubMed  CAS  Google Scholar 

  17. Schnur, D. M. (2008) Recent trends in library design: ‘rational design’ revisited. Curr Opin Drug Discov Devel 11, 375–380.

    PubMed  CAS  Google Scholar 

  18. Villar, H. O., Koehler, R. T. (2000) Comments on the design of chemical libraries for screening. Mol Divers 5, 13–24.

    Article  PubMed  CAS  Google Scholar 

  19. Manjasetty, B. A., Turnbull, A. P., Panjikar, S., Bussow, K., Chance, M. R. (2008) Automated technologies and novel techniques to accelerate protein crystallography for structural genomics. Proteomics 8, 612–625.

    Article  PubMed  CAS  Google Scholar 

  20. Gileadi, O., Knapp, S., Lee, W. H., Marsden, B. D., Muller, S., Niesen, F. H., Kavanagh, K. L., Ball, L. J., von Delft, F., Doyle, D. A., Oppermann, U. C., Sundstrom, M. (2007) The scientific impact of the Structural Genomics Consortium: a protein family and ligand-centered approach to medically-relevant human proteins. J Struct Funct Genomics 8, 107–119.

    Article  PubMed  CAS  Google Scholar 

  21. Cavasotto, C. N., Phatak, S. S. (2009) Homology modeling in drug discovery: current trends and applications. Drug Discov Today 14, 676–683.

    Article  PubMed  CAS  Google Scholar 

  22. Cavasotto, C. N., Orry, A. J., Murgolo, N. J., Czarniecki, M. F., Kocsi, S. A., Hawes, B. E., O’Neill, K. A., Hine, H., Burton, M. S., Voigt, J. H., Abagyan, R. A., Bayne, M. L., Monsma, F. J., Jr. (2008) Discovery of novel chemotypes to a G-protein-coupled receptor through ligand-steered homology modeling and structure-based virtual screening. J Med Chem 51, 581–588.

    Article  PubMed  CAS  Google Scholar 

  23. Hong, T. J., Park, H., Kim, Y. J., Jeong, J. H., Hahn, J. S. (2009) Identification of new Hsp90 inhibitors by structure-based virtual screening. Bioorg Med Chem Lett 19, 4839–4842.

    Article  PubMed  CAS  Google Scholar 

  24. Brozic, P., Turk, S., Lanisnik Rizner, T., Gobec, S. (2009) Discovery of new inhibitors of aldo-keto reductase 1C1 by structure-based virtual screening. Mol Cell Endocrinol 301, 245–250.

    Article  PubMed  CAS  Google Scholar 

  25. Park, H., Bhattarai, B. R., Ham, S. W., Cho, H. (2009) Structure-based virtual screening approach to identify novel classes of PTP1B inhibitors. Eur J Med Chem 44, 3280–3284.

    Article  PubMed  CAS  Google Scholar 

  26. Heinke, R., Spannhoff, A., Meier, R., Trojer, P., Bauer, I., Jung, M., Sippl, W. (2009) Virtual screening and biological characterization of novel histone arginine methyltransferase PRMT1 inhibitors. ChemMedChem 4, 69–77.

    Article  PubMed  CAS  Google Scholar 

  27. Wang, Q., Wang, J., Cai, Z., Xu, W. (2008) Prediction of the binding modes between BB-83698 and peptide deformylase from Bacillus stearothermophilus by docking and molecular dynamics simulation. Biophys Chem 134, 178–184.

    Article  PubMed  CAS  Google Scholar 

  28. Padgett, L. W., Howlett, A. C., Shim, J. Y. (2008) Binding mode prediction of conformationally restricted anandamide analogs within the CB1 receptor. J Mol Signal 3, 5.

    Article  PubMed  Google Scholar 

  29. Zampieri, D., Mamolo, M. G., Vio, L., Banfi, E., Scialino, G., Fermeglia, M., Ferrone, M., Pricl, S. (2007) Synthesis, antifungal and antimycobacterial activities of new bis-imidazole derivatives, and prediction of their binding to P450(14DM) by molecular docking and MM/PBSA method. Bioorg Med Chem 15, 7444–7458.

    Article  PubMed  CAS  Google Scholar 

  30. Monti, M. C., Casapullo, A., Cavasotto, C. N., Napolitano, A., Riccio, R. (2007) Scalaradial, a dialdehyde-containing marine metabolite that causes an unexpected noncovalent PLA2 Inactivation. Chembiochem 8, 1585–1591.

    Article  PubMed  CAS  Google Scholar 

  31. Diaz P., Phatak, S. S., Xu, J., Fronczek, F. R., Astruc-Diaz, F., Thompson, C. M., Cavasotto, C. N., Naguib, M. (2009) 2,3-Dihydro-1-benzofuran derivatives as a series of potent selective cannabinoid receptor 2 agonists: design, synthesis, and binding mode prediction through ligand-steered modeling. ChemMedChem 4, 1615–1629.

    Google Scholar 

  32. Andricopulo, A. D., Salum, L. B., Abraham, D. J. (2009) Structure-based drug design strategies in medicinal chemistry. Curr Topics Med Chem 9, 777–790.

    Google Scholar 

  33. Cavasotto, C. N., Orry, A. J. (2007) Ligand docking and structure-based virtual screening in drug discovery. Curr Top Med Chem 7, 1006–1014.

    Article  PubMed  CAS  Google Scholar 

  34. Kitchen, D. B., Decornez, H., Furr, J. R., Bajorath, J. (2004) Docking and scoring in virtual screening for drug discovery: methods and applications. Nat Rev Drug Discov 3, 935–949.

    Article  PubMed  CAS  Google Scholar 

  35. Cavasotto, C. N., Ortiz, M. A., Abagyan, R. A., Piedrafita, F. J. (2006) In silico identification of novel EGFR inhibitors with antiproliferative activity against cancer cells. Bioorg Med Chem Lett 16, 1969–1974.

    Article  PubMed  CAS  Google Scholar 

  36. Klebe, G. (2006) Virtual ligand screening: strategies, perspectives and limitations. Drug Discov Today 11, 580–594.

    Article  PubMed  CAS  Google Scholar 

  37. Zoete, V., Grosdidier, A., Michielin, O. (2009) Docking, virtual high throughput screening and in silico fragment-based drug design. J Cell Mol Med 13, 238–248.

    Article  PubMed  CAS  Google Scholar 

  38. Marsden, R. L., Orengo, C. A. (2008) Target selection for structural genomics: an overview. Methods Mol Biol 426, 3–25.

    Article  PubMed  CAS  Google Scholar 

  39. Levitt, D. G., Banaszak, L. J. (1992) POCKET: a computer graphics method for identifying and displaying protein cavities and their surrounding amino acids. J Mol Graph 10, 229–234.

    Article  PubMed  CAS  Google Scholar 

  40. Hendlich, M., Rippmann, F., Barnickel, G. (1997) LIGSITE: automatic and efficient detection of potential small molecule-binding sites in proteins. J Mol Graph Model 15, 359–363, 389.

    Article  PubMed  CAS  Google Scholar 

  41. Laskowski, R. A. (1995) SURFNET: a program for visualizing molecular surfaces, cavities, intermolecular interactions. J Mol Graph 13, 323–330, 307–328.

    Article  Google Scholar 

  42. Balakin, K. V., Kozintsev, A. V., Kiselyov, A. S., Savchuk, N. P. (2006) Rational design approaches to chemical libraries for hit identification. Curr Drug Discov Technol 3, 49–65.

    Article  PubMed  CAS  Google Scholar 

  43. Orry, A. J., Abagyan, R. A., Cavasotto, C. N. (2006) Structure-based development of target-specific compound libraries. Drug Discov Today 11, 261–266.

    Article  PubMed  CAS  Google Scholar 

  44. Brown, E. N., Ramaswamy, S. (2007) Quality of protein crystal structures. Acta Crystallogr D Biol Crystallogr 63, 941–950.

    Article  PubMed  Google Scholar 

  45. Davis, A. M., St-Gallay, S. A., Kleywegt, G. J. (2008) Limitations and lessons in the use of X-ray structural information in drug design. Drug Discov Today 13, 831–841.

    Article  PubMed  CAS  Google Scholar 

  46. Cavasotto, C. N., Singh, N. (2008) Docking and high throughput docking: successes and the challenge of protein flexibility. Curr Comput Aided Drug Design 4, 221–234.

    Article  CAS  Google Scholar 

  47. Sousa, S. F., Fernandes, P. A., Ramos, M. J. (2006) Protein-ligand docking: current status and future challenges. Proteins 65, 15–26.

    Article  PubMed  CAS  Google Scholar 

  48. Li, Z., Lazaridis, T. (2007) Water at biomolecular binding interfaces. Phys Chem Chem Phys 9, 573–581.

    Article  PubMed  CAS  Google Scholar 

  49. Mancera, R. L. (2007) Molecular modeling of hydration in drug design. Curr Opin Drug Discov Devel 10, 275–280.

    PubMed  CAS  Google Scholar 

  50. Corbeil, C. R., Moitessier, N. (2009) Docking ligands into flexible and solvated macromolecules. 3. Impact of input ligand conformation, protein flexibility, and water molecules on the accuracy of docking programs. J Chem Inf Model 49, 997–1009.

    Article  PubMed  CAS  Google Scholar 

  51. Chen, J., Swamidass, S. J., Dou, Y., Bruand, J., Baldi, P. (2005) ChemDB: a public database of small molecules and related chemoinformatics resources. Bioinformatics 21, 4133–4139.

    Article  PubMed  CAS  Google Scholar 

  52. Irwin, J. J., Shoichet, B. K. (2005) ZINC–a free database of commercially available compounds for virtual screening. J Chem Inf Model 45, 177–182.

    Article  PubMed  CAS  Google Scholar 

  53. Williams, A. J. (2008) Public chemical compound databases. Curr Opin Drug Discov Develop 11, 393–404.

    CAS  Google Scholar 

  54. Drie, J. H. (2005) Pharmacophore-based virtual screening: a practical perspective, in (Alvarez, J., Shoichet, B., eds.) Virtual Screening in Drug Discovery. CRC Press, Boca Raton, FL, pp. 157–205.

    Google Scholar 

  55. Oprea, T. I., Bologa, C. G., Olah, M. M. (2005) Compound selection for virtual screening, in Virtual screening in Drug Discovery (Alvarez, J., Shoichet, B., eds.), CRC Press, Boca Raton, FL, pp. 89–106.

    Google Scholar 

  56. Lipinski, C. A., Lombardo, F., Dominy, B. W., Feeney, P. J. (1997) Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv Drug Del Rev 23, 3–25.

    Article  CAS  Google Scholar 

  57. Oprea, T. I. (2002) Current trends in lead discovery: are we looking for the appropriate properties? J Comput Aided Mol Des 16, 325–334.

    Article  PubMed  CAS  Google Scholar 

  58. Hubbard, R. E. (2008) Fragment approaches in structure-based drug discovery. J Synchrotron Radiat 15, 227–230.

    Article  PubMed  CAS  Google Scholar 

  59. Kroemer, R. T. (2007) Structure-based drug design: docking and scoring. Curr Protein Pept Sci 8, 312–328.

    Article  PubMed  CAS  Google Scholar 

  60. Barril, X., Hubbard, R. E., Morley, S. D. (2004) Virtual screening in structure-based drug discovery. Mini Rev Med Chem 4, 779–791.

    PubMed  CAS  Google Scholar 

  61. Teague, S. J. (2003) Implications of protein flexibility for drug discovery. Nat Rev Drug Discov 2, 527–541.

    Article  PubMed  CAS  Google Scholar 

  62. B-Rao, C., Subramanian, J., Sharma, S. D. (2009) Managing protein flexibility in docking and its applications. Drug Discov Today 14, 394–400.

    Article  PubMed  CAS  Google Scholar 

  63. Dias, R., de Azevedo, W. F., Jr. (2008) Molecular docking algorithms. Curr Drug Targets 9, 1040–1047.

    Article  PubMed  CAS  Google Scholar 

  64. Sperandio, O., Miteva, M. A., Delfaud, F., Villoutreix, B. O. (2006) Receptor-based computational screening of compound databases: the main docking-scoring engines. Curr Protein Pept Sci 7, 369–393.

    Article  PubMed  CAS  Google Scholar 

  65. Stahl, M., Rarey, M. (2001) Detailed analysis of scoring functions for virtual screening. J Med Chem 44, 1035–1042.

    Article  PubMed  CAS  Google Scholar 

  66. Perola, E., Walters, W. P., Charifson, P. S. (2005) An analysis of critical factors affecting docking and scoring, in (Alvarez, J., Shoichet, B., eds.) Virtual screening in drug discovery. CRC Press, Boca Raton, FL, pp. 47–85.

    Google Scholar 

  67. Warren, G. L., Andrews, C. W., Capelli, A. M., Clarke, B., LaLonde, J., Lambert, M. H., Lindvall, M., Nevins, N., Semus, S. F., Senger, S., Tedesco, G., Wall, I. D., Woolven, J. M., Peishoff, C. E., Head, M. S. (2006) A critical assessment of docking programs and scoring functions. J Med Chem 49, 5912–5931.

    Article  PubMed  CAS  Google Scholar 

  68. Waszkowycz, B. (2008) Towards improving compound selection in structure-based virtual screening. Drug Discov Today 13, 219–226.

    Article  PubMed  CAS  Google Scholar 

  69. Manallack, D. T., Pitt, W. R., Gancia, E., Montana, J. G., Livingstone, D. J., Ford, M. G., Whitley, D. C. (2002) Selecting screening candidates for kinase and G protein-coupled receptor targets using neural networks. J Chem Inf Comput Sci 42, 1256–1262.

    Article  PubMed  CAS  Google Scholar 

  70. Schneider, G. (2002) Trends in virtual combinatorial library design. Curr Med Chem 9, 2095–2101.

    PubMed  CAS  Google Scholar 

  71. Beavers, M. P., Chen, X. (2002) Structure-based combinatorial library design: methodologies and applications. J Mol Graph Model 20, 463–468.

    Article  PubMed  CAS  Google Scholar 

  72. Coupez, B., Lewis, R. A. (2006) Docking and scoring–theoretically easy, practically impossible? Curr Med Chem 13, 2995–3003.

    Article  PubMed  CAS  Google Scholar 

  73. Sun, Y., Ewing, T. J., Skillman, A. G., Kuntz, I. D. (1998) CombiDOCK: structure-based combinatorial docking and library design. J Comput Aided Mol Des 12, 597–604.

    Article  PubMed  CAS  Google Scholar 

  74. Kuntz, I. D., Blaney, J. M., Oatley, S. J., Langridge, R., Ferrin, T. E. (1982) A geometric approach to macromolecule-ligand interactions. J Mol Biol 161, 269–288.

    Article  PubMed  CAS  Google Scholar 

  75. Murray, C. W., Clark, D. E., Auton, T. R., Firth, M. A., Li, J., Sykes, R. A., Waszkowycz, B., Westhead, D. R., Young, S. C. (1997) PRO_SELECT: combining structure-based drug design and combinatorial chemistry for rapid lead discovery. 1. Technology. J Comput Aided Mol Des 11, 193–207.

    Article  PubMed  CAS  Google Scholar 

  76. Blaney, J. M., Dixon, J. S. (1993) A good ligand is hard to find: automated docking methods. Perspect Drug Discov Des 1, 301–319.

    Article  CAS  Google Scholar 

  77. Kuntz, I. D., Meng, E. C., Shoichet, B. (1994) Structure-based molecular design. Acc Chem Res 27, 117–123.

    Article  CAS  Google Scholar 

  78. Bohm, H. J. (1994) The development of a simple empirical scoring function to estimate the binding constant for a protein-ligand complex of known three-dimensional structure. J Comput Aided Mol Des 8, 243–256.

    Article  PubMed  CAS  Google Scholar 

  79. Clark, D. E., Frenkel, D., Levy, S. A., Li, J., Murray, C. W., Robson, B., Waszkowycz, B., Westhead, D. R. (1995) PRO-LIGAND: an approach to de novo molecular design. 1. Application to the design of organic molecules. J Comput Aided Mol Des 9, 13–32.

    Article  PubMed  CAS  Google Scholar 

  80. Makino, S., Ewing, T. J., Kuntz, I. D. (1999) DREAM++: flexible docking program for virtual combinatorial libraries. J Comput Aided Mol Des 13, 513–532.

    Article  PubMed  CAS  Google Scholar 

  81. Roe, D. C., Kuntz, I. D. (1995) BUILDER v.2: improving the chemistry of a de novo design strategy. J Comput Aided Mol Des 9, 269–282.

    Article  PubMed  CAS  Google Scholar 

  82. Van Gunsteren, W. F., Berendsen, H. J. C. (1977) Algorithms for macromolecular dynamics and constraint dynamics. Mol Phys 34, 1311–1327.

    Article  Google Scholar 

  83. Sprous, D. G., Lowis, D. R., Leonard, J. M., Heritage, T., Burkett, S. N., Baker, D. S., Clark, R. D. (2004) OptiDock: virtual HTS of combinatorial libraries by efficient sampling of binding modes in product space. J Comb Chem 6, 530–539.

    Article  PubMed  CAS  Google Scholar 

  84. Rarey, M., Lengauer, T. (2000) A recursive algorithm for efficient combinatorial library docking. Perspect Drug Discov Des 20, 63–81.

    Article  CAS  Google Scholar 

  85. Zhou, J. Z., Shi, S., Na, J., Peng, Z., Thacher, T. (2009) Combinatorial library-based design with Basis Products. J Comput Aided Mol Des DOI 10.1007/s10822-009-9297-9.

    Google Scholar 

  86. Grzybowski, B. A., Ishchenko, A. V., Kim, C. Y., Topalov, G., Chapman, R., Christianson, D. W., Whitesides, G. M., Shakhnovich, E. I. (2002) Combinatorial computational method gives new picomolar ligands for a known enzyme. Proc Natl Acad Sci USA 99, 1270–1273.

    Article  PubMed  CAS  Google Scholar 

  87. Zhou, J. Z. (2008) Structure-directed combinatorial library design. Curr Opin Chem Biol 12, 379–385.

    Article  PubMed  CAS  Google Scholar 

  88. Head, M. S., Ryan, M. D., Lee, D., Feng, Y., Janson, C. A., Concha, N. O., Keller, P. M., deWolf, W. E., Jr. (2001) Structure-based combinatorial library design: discovery of non-peptidic inhibitors of caspases 3 and 8. J Comput Aided Mol Des 15, 1105–1117.

    Article  PubMed  CAS  Google Scholar 

  89. Zhao, L., Huang, W., Liu, H., Wang, L., Zhong, W., Xiao, J., Hu, Y., Li, S. (2006) FK506-binding protein ligands: structure-based design, synthesis, and neurotrophic/neuroprotective properties of substituted 5,5-dimethyl-2-(4-thiazolidine)carboxylates. J Med Chem 49, 4059–4071.

    Article  PubMed  CAS  Google Scholar 

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Authors and Affiliations

  1. School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, USA

    Claudio N. Cavasotto & Sharangdhar S. Phatak

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  1. Claudio N. Cavasotto
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  2. Sharangdhar S. Phatak
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Correspondence to Claudio N. Cavasotto .

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  1. , Department of Pharmacology, University of California, La Jolla, 92093, California, USA

    Joe Zhongxiang Zhou

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Cavasotto, C.N., Phatak, S.S. (2011). Docking Methods for Structure-Based Library Design. In: Zhou, J. (eds) Chemical Library Design. Methods in Molecular Biology, vol 685. Humana Press. https://doi.org/10.1007/978-1-60761-931-4_8

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  • DOI: https://doi.org/10.1007/978-1-60761-931-4_8

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