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Bioinformatics Platform Development

From Gene to Lead Compound
  • Alexis S. Ivanov
  • Alexander V. Veselovsky
  • Alexander V. Dubanov
  • Vladlen S. Skvortsov
Part of the Methods in Molecular Biology book series (MIMB, volume 316)

Abstract

In the past 10 yr, the field of bioinformatics has been characterized by the mapping of many genomes. These efforts have stimulated explosive development of novel bioinformatics and experimental approaches to predict the functions and metabolic role of the new proteins. The main application of the work is to search, validate, and prioritize new targets for designing a new generation of drugs. Modern computer and experimental methods for discovery of new lead compounds have also expanded and integrated into the process referred to as rational drug design. They are directed to accelerate and optimize the drug discovery process using experimental and virtual (computer-aided drug discovery) methods. Recently, these methods and approaches have merged into a “from gene to lead” platform that includes the processes from new target discovery through obtaining highly effective lead compounds. This chapter describes the strategies as employed by the “From Gene to Lead” platform, including the major computer and experimental approaches and their interrelationship. The latter part of the chapter contains some examples of the steps required for implementing this platform.

Key Words

Rational drug design bioinformatics lead compound computer-aided drug discovery target discovery database mining target validation structure-based drug design ligand-based drug design de novo design 

References

  1. 1.
    Lohse, M. J. (1998) The future of pharmacology. Trends Pharmacol. Sci. 19, 198–200.PubMedCrossRefGoogle Scholar
  2. 2.
    Borchardt, J. K. (2001) New drug development costs now average $802 million. Alchemist 6. (http://www.chemweb.com/alchem/articles/1005928853806.html). Accessed on 12/6/2004.
  3. 3.
    National Center for Biotechnology Information, http://www.ncbi.nlm.nih.gov.
  4. 4.
    Kulikova, T., Aldebert, P., Althorpe, N., et al. (2004) The EMBL Nucleotide Sequence Database. Nucleic Acids Res. 32(database issue), D27–D30. (http://www.ebi.ac.uk/embl).PubMedCrossRefGoogle Scholar
  5. 5.
    DNA Data Bank of Japan, http://www.ddbj.nig.ac.jp.
  6. 6.
    Boeckmann, B., Bairoch, A., Apweiler, R., et al. (2003) The SWISS-PROT protein knowledgebase and its supplement TrEMBL in 2003. Nucleic Acids Res. 31, 365–370 (http://www.expasy.org/sprot).PubMedCrossRefGoogle Scholar
  7. 7.
    Wu, C. H., Huang, H., Yeh, L.-S. L., and Barker, W. C. (2003) Protein family classification and functional annotation. Comput. Biol. Chem. 27, 37–47 (http://pir.georgetown.edu)PubMedCrossRefGoogle Scholar
  8. 8.
    Tatusov, R. L., Fedorova, N. D., Jackson, J. D., et al. (2003) The COG database: an updated version includes eukaryotes. BMC Bioinfor. 4, 41.CrossRefGoogle Scholar
  9. 9.
    Kanehisa, M., Goto, S., Kawashima, S., Okuno, Y., and Hattori, M. (2004) The KEGG resource for deciphering the genome. Nucleic Acids Res. 32(database issue), D277–D280.PubMedCrossRefGoogle Scholar
  10. 10.
    Pandit, S. B., Bhadra, R., Gowri, V. S., Balaji, S., Anand, B., and Srinivasan, N. (2004) SUPFAM: a database of sequence superfamilies of protein domains. BMC Bioinf. 5, 28–32. (http://www.sanger.ac.uk/Software/Pfam/).CrossRefGoogle Scholar
  11. 11.
    Haft, D. H., Selengut, J. D., and White, O. (2003) The TIGRFAMs database of protein families. Nucleic Acids Res. 31, 371–373 (http://www.tigr.org/TIGRFAMs/).PubMedCrossRefGoogle Scholar
  12. 12.
    Peterson, J. D., Umayam, L. A., Dickinson, T., Hickey, E. K., and White, O. (2001) The comprehensive microbial resource. Nucleic Acids Res. 29, 123–125 (http://www.tigr.org/CMR).PubMedCrossRefGoogle Scholar
  13. 13.
    Uchiyama, I. (2003) MBGD: microbial genome database for comparative analysis. Nucleic Acids Res. 31, 58–62 (http://mbgd.genome.ad.jp).PubMedCrossRefGoogle Scholar
  14. 14.
    Xenarios, I., Salwinski, L., Duan, X. J., Higney, P., Kim, S. M., and Eisenberg, D. (2002) DIP, the Database of Interacting Proteins: a research tool for studying cellular networks of protein interactions. Nucleic Acids Res. 30, 303–305 (http://dip.doe-mbi.ucla.edu).PubMedCrossRefGoogle Scholar
  15. 15.
    Bader, G. D., Betel, D., and Hogue, C. W. (2003) BIND: the Biomolecular Interaction Network Database. Nucleic Acids Res. 31, 248–250 (http://bind.ca).PubMedCrossRefGoogle Scholar
  16. 16.
    Ng, S. K., Zhang, Z., and Tan, S. H. (2003) Integrative approach for computationally inferring protein domain interactions. Bioinformatics 19, 923–929(http://interdom.lit.org.sg).PubMedCrossRefGoogle Scholar
  17. 17.
    Suhre, K. and Claverie, J.-M. (2004) FusionDB: a database for in-depth analysis of prokaryotic gene fusion events. Nucleic Acids Res. 32(database issue), D273–D276 (http://igs-server.cnrs-mrs.fr/FusionDB/).PubMedCrossRefGoogle Scholar
  18. 18.
    NCGR, National Center for Genome Resources, http://www.ncgr.org/pathdb/.
  19. 19.
    Berman, H. M., Westbrook, J., Feng, Z., et al. (2000) The Protein Data Bank. Nucleic Acids Res. 28, 235–242 (http://www.rcsb.org/pdb).PubMedCrossRefGoogle Scholar
  20. 20.
    Noguchi, T. and Akiyama, Y. (2003) PDB-REPRDB: a database of representative protein chains from the Protein Data Bank (PDB) in 2003. Nucleic Acids Res. 31, 492, 493 (http://mbs.crbc.jp/pdbreprdb-cgi/reprdb_menu.pl).PubMedCrossRefGoogle Scholar
  21. 21.
    Frishman, D., Mokrejs, M., Kosykh, D., et al. (2003) The PEDANT genome database. Nucleic Acids Res. 31, 207–211 (http://pedant.gsf.de).PubMedCrossRefGoogle Scholar
  22. 22.
    Galperin, M. Y. (2004) The Molecular Biology Database Collection: 2004 update. Nucleic Acids Res. 32(database issue), D3–D22.PubMedCrossRefGoogle Scholar
  23. 23.
    Freiberg, C. (2001) Novel computation methods in anti-microbial target identification. Drug Discov. Today 6, S72–S80.CrossRefGoogle Scholar
  24. 24.
    Allen, F. H. (2002) The Cambridge Structural Database: a quarter of a million crystal structures and rising. Acta Crystallogr. B58, 380–388 (http://www.ccdc.cam.ac.uk).Google Scholar
  25. 25.
    National Cancer Institute: Pure Chemicals Repository, http://www.dtp.nci.nih.gov/branches/dscb/repo_open.html.
  26. 26.
    MDL Drug Data Report, MDL Information Systems, http://www.mdl.com.
  27. 27.
    Comprehensive Medicinal Chemistry, MDL Information Systems, http://www.mdl.com.
  28. 28.
    ASINEX Ltd., http://www.asinex.com.
  29. 29.
    ChemBridge Corporation, http://www.chembridge.com.
  30. 30.
  31. 31.
    SYBYL 6.7.1, Tripos Inc., http://www.tripos.com.
  32. 32.
    Spaltmann, F., Blunck, M., and Ziegelbauer, K. (1999) Computer-aided target selectionprioritizing targets for antifungal drug discovery. Drug Discov. Today 4, 17–26.PubMedCrossRefGoogle Scholar
  33. 33.
    Dubanov, A. V., Ivanov, A. S., and Archakov, A. I. (2001) Computer searching of new targets for antimicrobial drugs based on comparative analysis of genomes. Vopr. Med. Khim. 47, 353–367 (in Russian).PubMedGoogle Scholar
  34. 34.
  35. 35.
    The Perl Directory, http://www.perl.org.
  36. 36.
  37. 37.
    Mangalam, H. (2002) The Bio* toolkits—a brief overview. Brief Bioinform. 3, 296–302.PubMedCrossRefGoogle Scholar
  38. 38.
  39. 39.
  40. 40.
  41. 41.
  42. 42.
  43. 43.
    Case, D. A., Darden, T. A., Cheatham, T. E. III, et al. (2004) AMBER 8, University of California, San Francisco (http://amber.scripps.edu).Google Scholar
  44. 44.
    Berendsen, H. J. C., van der Spoel, D., and van Drunen, R. (1995) GROMACS: A messagepassing parallel molecular dynamics implementation. Comp. Phys. Commun. 91, 43–56 (http://www.gromacs.org).CrossRefGoogle Scholar
  45. 45.
    Allsop, A. E. (1998) New antibiotic discovery, novel screens, novel targets and impact of microbial genomics. Curr. Opin. Microbiol. 1, 530–534.PubMedCrossRefGoogle Scholar
  46. 46.
    Veselovsky, A. V., Ivanov, Y. D., Ivanov, A. S., Archakov, A. I., Lewi, P., and Janssen, P. (2002) Protein-protein interactions: mechanisms and modification by drugs. J. Mol. Recognit. 15, 405–422.PubMedCrossRefGoogle Scholar
  47. 47.
    Archakov, A. I., Govorun, V. M., Dubanov, A. V., et al. (2003) Protein-protein interactions as a target for drugs in proteomics. Proteomics 3, 380–391.PubMedCrossRefGoogle Scholar
  48. 48.
    Rost, B., Liu, J., Wrzeszczynski, K. O., and Ofran, Y. (2003) Automatic prediction of protein fuction. Cell. Mol. Life Sci. 60, 2637–2650.PubMedCrossRefGoogle Scholar
  49. 49.
    Eisenberg, D., Marcotte, E. M., Xenarios, I., and Yeates, T. O. (2000) Protein function in the post-genomic era. Nature 2000 405, 823–826.CrossRefGoogle Scholar
  50. 50.
    Butte A. J. and Kohane I. S. (2000) Mutual information relevance networks: functional genomic clustering using pairwise entropy measurements. Pac. Symp. Biocomput. 5, 415–426.Google Scholar
  51. 51.
    Yanai, I. and DeLisi, C. (2002) The society of genes: networks of functional links between genes from comparative genomics. Genome Biol. 3, research0064/12 (http://genomebiology.com/content/pdg/gb-2002-3-11-research0064.pdf).
  52. 52.
    Jansen, R., Lan, N., Qian, J., and Gerstein, M. (2002) Integration of genomic datasets to predict protein complexes in yeast. J. Struct. Funct. Genomics 2, 71–81.PubMedCrossRefGoogle Scholar
  53. 53.
    Jansen, R., Yu, H., Greenbaum, D., et al. (2003) A Bayesian networks approach for predicting protein-protein interactions from genomic data. Science 302, 449–453.PubMedCrossRefGoogle Scholar
  54. 54.
    Marcotte, E. M., Xenarios, I., van Der Bliek, A. M., and Eisenberg D. (2000) Localizing proteins in the cell from their phylogenetic profiles. Proc. Natl. Acad. Sci. USA 97, 12,115–12,120.PubMedCrossRefGoogle Scholar
  55. 55.
    Thanassi, J. A., Hartman-Neumann, S. L., Dougherty, T. J., Dougherty, B. A., and Pucci, M. J. (2002) Identification of 113 conserved essential genes using a high-throughput gene disruption system in Streptococcus pneumoniae. Nucleic Acids Res. 30, 3152–3162.PubMedCrossRefGoogle Scholar
  56. 56.
    Boguslavsky, J. (2002) Target validation: finding a needle in a haystack. Drug Discov. Dev. 5, 41–48.Google Scholar
  57. 57.
    Lau, A. T., He, Q. Y., and Chiu, J. F. (2003) Proteomic technology and its biomedical application. Acta Biochim. Biophys. Sinica 35, 965–975.Google Scholar
  58. 58.
    Walgren, J. L. and Thompson, D. C. (2004) Application of proteomic technologies in the drug development process. Toxicol. Lett. 149, 377–385.PubMedCrossRefGoogle Scholar
  59. 59.
    Cooper, R. A. and Carucci, D. J. (2004) Proteomic approaches to studying drug targets and resistance in Plasmodium. Curr. Drug Targets Infect. Disord. 4, 41–51.PubMedCrossRefGoogle Scholar
  60. 60.
    Flory, M. R. and Aebersold, R. (2003) Proteomic approaches for the identification of cell cycle-related drug targets. Prog. Cell. Cycle Res. 5, 167–171.PubMedGoogle Scholar
  61. 61.
    Lopez, M. F. (1998) Proteomic databases: roadmaps for drug discovery. Am. Clin. Lab. 17, 16–18.PubMedGoogle Scholar
  62. 62.
    Jones, S. and Thornton, J. M. (1995) Protein-protein interactions: a review of protein dimer structures. Prog. Biophys. Mol. Biol. 63, 31–65.PubMedCrossRefGoogle Scholar
  63. 63.
    Wilkinson, K. D. (2004) Quantitative analysis of protein-protein interactions. Methods Mol. Biol. 261, 15–32.PubMedGoogle Scholar
  64. 64.
    Nedelkov, D. and Nelson, R. W. (2003) Delineating protein-protein interactions via biomolecular interaction analysis-mass spectrometry. J. Mol. Recognit. 16, 9–14.PubMedCrossRefGoogle Scholar
  65. 65.
    Strosberg, A. D. (2002) Protein interaction mapping for target validation: the need for an integrated combinatory process involving complementary approaches. Curr. Opin. Mol. Ther. 4, 594–600.PubMedGoogle Scholar
  66. 66.
    Pillutla, R. C., Goldstein, N. I., Blume, A. J., and Fisher, P. B. (2002) Target validation and drug discovery using genomic and protein-protein interaction technologies. Expert Opin. Ther. Targets 6, 517–531.PubMedCrossRefGoogle Scholar
  67. 67.
    Butcher, S. P. (2003) Target discovery and validation in the post-genomic era. Neurochem. Res. 28, 367–371.PubMedCrossRefGoogle Scholar
  68. 68.
    Williams, M. (2003) Target validation. Curr. Opin. Pharmacol. 3, 571–577.PubMedCrossRefGoogle Scholar
  69. 69.
    Cowman, A. F. and Crabb, B. S. (2003) Functional genomics: identifying drug targets for parasitic diseases. Trends Parasitol. 19, 538–543.PubMedCrossRefGoogle Scholar
  70. 70.
    Sheppard, D. (1994) Dominant negative mutants: tools for the study of protein function in vitro and in vivo. Am. J. Respir. Cell. Mol. Biol. 11, 1–6.PubMedGoogle Scholar
  71. 71.
    Homanics, G. E., Quinlan, J. J., Mihalek, R., and Firestone, L. L. (1998) Genetic dissection of the molecular target(s) of anesthetics with the gene knockout approach in mice. Toxicol. Lett. 100–101, 301–307.PubMedCrossRefGoogle Scholar
  72. 72.
    Luscombe, N. M., Austin, S. E., Berman, H. M., and Thornton, J. M. (2000) An overview of the structures of protein-DNA complexes. Genome Biol. 1, reviews 001.1–001.10 (http://genomebiology.com/content/pdf/gb-2000-1-1-reviews001.pdf).
  73. 73.
    Kim, C. A. and Berg, J. M. (1996) A 2.2 A resolution crystal structure of a designed zinc finger protein bound to DNA. Nat. Struct. Biol. 3, 940–945.PubMedCrossRefGoogle Scholar
  74. 74.
    Jacobs, G. H. (1992) Determination of the base recognition positions of zinc finger from sequence-analysis. EMBO J. 11, 4507–4517.PubMedGoogle Scholar
  75. 75.
    Pavletich, N. P. and Pabo, C. O. (1991) Zinc finger-DNA recognition: crystal structure of a Zif268-DNA complex at 2.1A. Science 252, 809–817.PubMedCrossRefGoogle Scholar
  76. 76.
    Suzuki, M., Gerstein, M. B., and Yagi, N. (1994) Stereochemical basis of DNA recognition by Zn fingers. Nucleic Acids Res. 22, 3397–3405.PubMedCrossRefGoogle Scholar
  77. 77.
    Cech, T. R. (1992) Ribozyme engineering. Curr. Opin. Struct. Biol. 2, 605–609.CrossRefGoogle Scholar
  78. 78.
    Breaker, R. R. (1997) In vitro selection of catalytic polynucleotides. Chem. Rev. 97, 371–390.PubMedCrossRefGoogle Scholar
  79. 79.
    Usman, N., Beigelman, L., and McSwiggen, J. A. (1996) Hammerhead ribozyme engineering. Curr. Opin. Struct. Biol. 6, 527–533.PubMedCrossRefGoogle Scholar
  80. 80.
    Uhlenbeck, O. C. (1987) A small catalytic oligoribonucleotide. Nature 328, 596–600.PubMedCrossRefGoogle Scholar
  81. 81.
    Jarvis, T. C., Bouhana, K. S., Lesch, M. E., et al. (2000) Ribozymes as tools for therapeutic target validation in arthritis. J. Immunol. 165, 493–498.PubMedGoogle Scholar
  82. 82.
    Goodchild, J. (2002) Hammerhead ribozymes for target validation. Expert Opin. Ther. Targets 6, 235–247.PubMedCrossRefGoogle Scholar
  83. 83.
    Lehner, B., Fraser, A. G., and Sanderson, C. M. (2004) Technique review: how to use RNA interference. Brief Funct. Genomic Proteomic 3, 68–83.PubMedCrossRefGoogle Scholar
  84. 84.
    Jain, K. K. (2004) RNAi and siRNA in target validation. Drug Discov. Today 9, 307–309.PubMedCrossRefGoogle Scholar
  85. 85.
    Henning, S. W. and Beste, G. (2002) Loss-function strategies in drug target validation. Curr. Drug Discov. 5, 17–21.Google Scholar
  86. 86.
    Baker, B. F. and Monia, B. P. (1999) Novel mechanisms for antisense mediated regulation of gene expression. Biochim. Biophys. Acta 1489, 3–18.PubMedGoogle Scholar
  87. 87.
    Inouye, M. (1988) Antisense RNA: its functions and applications in gene regulation—a review. Gene 72, 25–34.PubMedCrossRefGoogle Scholar
  88. 88.
    Ravichandran, L. V., Dean, N. M., and Marcusson, E. G. (2004) Use of antisense oligonucleotides in functional genomics and target validation. Oligonucleotides 14, 49–64.PubMedCrossRefGoogle Scholar
  89. 89.
    Ji, Y., Yin, D., Fox, B., Holmes, D. J., Payne, D., and Rosenberg, M. (2004) Validation of antibacterial mechanism of action using regulated antisense RNA expression in Staphylococcus aureus. FEMS Microbiol. Lett. 231, 177–184.PubMedCrossRefGoogle Scholar
  90. 90.
    Lavery, K. S. and King, T. H. (2003) Antisense and RNAi: powerful tools in drug target discovery and validation. Curr. Opin. Drug Discov. Dev. 6, 561–569.Google Scholar
  91. 91.
    Taylor, M. F. (2001) Target validation and functional analyses using antisense oligonucleotides. Expert Opin. Ther. Targets 5, 297–301.PubMedCrossRefGoogle Scholar
  92. 92.
    Dean, N. M. (2001) Functional genomics and target validation approaches using antisense oligonucleotide technology. Curr. Opin. Biotechnol. 12, 622–625.PubMedCrossRefGoogle Scholar
  93. 93.
    Koller, E., Gaarde, W. A., and Monia, B. P. (2000) Elucidating cell signaling mechanisms using antisense technology. Trends Pharmacol. Sci. 21, 142–148.PubMedCrossRefGoogle Scholar
  94. 94.
    Bennett, C. F. and Cowsert, L. M. (1999) Application of antisense oligonucleotides for gene functionalization and target validation. Curr. Opin. Mol. Ther. 1, 359–371.PubMedGoogle Scholar
  95. 95.
    Ho, S. P. and Hartig, P. R. (1999) Antisense oligonucleotides for target validation in the CNS. Curr. Opin. Mol. Ther. 1, 336–343.PubMedGoogle Scholar
  96. 96.
  97. 97.
    Pellestor, F. and Paulasova, P. (2004) The peptide nucleic acids, efficient tools for molecular diagnosis (review). Int. J. Mol. Med. 13, 521–525.PubMedGoogle Scholar
  98. 98.
    Gambari, R. (2001) Peptide-nucleic acids (PNAs): a tool for the development of gene expression modifiers. Curr. Pharm. Des. 7, 1839–1862.PubMedCrossRefGoogle Scholar
  99. 99.
    Demidov, V. V. (2002) PNA comes of age: from infancy to maturity. Drug Discov. Today 7, 153–155.PubMedCrossRefGoogle Scholar
  100. 100.
    Ganesh, K. N. and Nielsen, P. E. (2000) Peptide nucleic acids: analogs and derivatives. Curr. Organic Chem. 4, 916–928.Google Scholar
  101. 101.
    Winters, T. A. (2000) Gene targeting agents, new opportunities for rational drug development. Curr. Opin. Mol. Ther. 2, 670–681.PubMedGoogle Scholar
  102. 102.
    Nielsen, P. E. (2000) Antisense peptide nucleic acids. Curr. Opin. Mol. Ther. 2, 282–287.PubMedGoogle Scholar
  103. 103.
    Demidov, V. V. and Frank-Kamenetskii, M. D. (2001) Sequence-specific targeting of duplex DNA by peptide nucleic acids via triplex strand invasion. Methods 23, 108–122.PubMedCrossRefGoogle Scholar
  104. 104.
    Ray, A. and Norden, B. (2000) Peptide nucleic acid (PNA): its medical and biotechnological applications and promise for the future. FASEB J. 14, 1041–1060.PubMedGoogle Scholar
  105. 105.
    Banker, D. D. (2001) Monoclonal antibodies: a review. Indian J. Med. Sci. 55, 651–654.PubMedGoogle Scholar
  106. 106.
    Peet, N. P. (2003) What constitutes target validation? Targets 2, 125–127.CrossRefGoogle Scholar
  107. 107.
    Liao, J. C., Roider, J., and Jay, D. G. (1994) Chromophore-assisted laser inactivation of proteins is mediated by the photogeneration of free radicals. Proc. Natl. Acad. Sci. USA 91, 2659–2663.PubMedCrossRefGoogle Scholar
  108. 108.
    Jay, D. G. (1988) Selective destruction of protein function by chromophore-assisted laser inactivation. Proc. Natl. Acad. Sci. USA 85, 5454–5458.PubMedCrossRefGoogle Scholar
  109. 109.
    Niewohner, J., Rubenwolf, S., Meyer, E., and Rudert, F. (2001) Laser-mediated protein inactivation for target validation. Am. Genomic/Proteomic Technol. 4, 28–33. (http://www.iscpubs.com/articles/agpt/g0108nie.pdf).Google Scholar
  110. 110.
    Eustace, B. K. and Jay, D. G. (2003) Fluorophore-assisted light inactivation for multiplex analysis of protein function in cellular processes. Methods Enzymol. 360, 649–660.PubMedCrossRefGoogle Scholar
  111. 111.
    Beck, S., Sakurai, T., Eustace, B. K., Beste, G., Schier, R., Rudert, F., and Jay, D. G. (2002) Fluorophore-assisted light inactivation: a high-throughput tool for direct target validation of proteins. Proteomics 2, 247–255.PubMedCrossRefGoogle Scholar
  112. 112.
    Bradbury, A. (2003) scFvs and beyond. Drug Discov. Today 8, 737–739.PubMedCrossRefGoogle Scholar
  113. 113.
    Chowdhury, P. S. and Vasmatzis, G. (2003) Engineering scFvs for improved stability. Methods Mol. Biol. 207, 237–254.PubMedGoogle Scholar
  114. 114.
    van Wyngaardt, W., Malatji, T., Mashau, C., et al. (2004) A large semi-synthetic single-chain Fv phage display library based on chicken immunoglobulin genes. BMC Biotechnol. 4, 6.PubMedCrossRefGoogle Scholar
  115. 115.
    Toleikis, L., Broders, O., and Dubel, S. (2004) Cloning single-chain antibody fragments (scFv) from hybridoma cells. Methods Mol. Med. 94, 447–458.PubMedGoogle Scholar
  116. 116.
    Tanaka, T., Lobato, M. N., and Rabbitts, T. H. (2003) Single domain intracellular antibodies: a minimal fragment for direct in vivo selection of antigen-specific intrabodies. J. Mol. Biol. 331, 1109–1120.PubMedCrossRefGoogle Scholar
  117. 117.
    Donini, M., Morea, V., Desiderio, A., et al. (2003) Engineering stable cytoplasmic intrabodies with designed specificity. J. Mol. Biol. 330, 323–332.PubMedCrossRefGoogle Scholar
  118. 118.
    Cohen, P. A. (2002) Intrabodies: targeting scFv expression to eukaryotic intracellular compartments. Methods Mol. Biol. 178, 367–378.PubMedGoogle Scholar
  119. 119.
    Marasco, W. A. (1997) Intrabodies: turning the humoral immune system outside in for intracellular immunization. Gene Ther. 4, 11–15.PubMedCrossRefGoogle Scholar
  120. 120.
    Mundt, K. E. (2002) Intrabodies—valuable tools for target validation. Selection procedures for the use of intrabodies in functional genomics. Reprinted from Eur. Pharm. Contractor Winter 2001 issue. Samedan Ltd. Tech. ed. 10, 1–5. (http://www.esbatech.com/pr/publications/ebr_preview.pdf).Google Scholar
  121. 121.
    Rimmele, M. (2003) Nucleic acid aptamers as tools and drugs: recent developments. Chembiochemistry 4, 963–971.CrossRefGoogle Scholar
  122. 122.
    Burgstaller, P., Girod, A., and Blind, M. (2002) Aptamers as tools for target prioritization and lead identification. Drug Discov. Today 7, 1221–1228.PubMedCrossRefGoogle Scholar
  123. 123.
    Toulme, J. J., Di Primo, C., and Boucard, D. (2004) Regulating eukaryotic gene expression with aptamers. FEBS Lett. 567, 55–62.PubMedCrossRefGoogle Scholar
  124. 124.
    Ulrich, H., Martins, A. H., and Pesquero, J. B. (2004) RNA and DNA aptamers in cytomics analysis. Cytometry 59A, 220–231.CrossRefGoogle Scholar
  125. 125.
    Convery, M. A., Rowsell, S., Stonehouse, N. J., et al. (1998) Crystal structure of an RNA aptamer-protein complex at 2.8 A resolution. Nat. Struct. Biol. 5, 133–139.PubMedCrossRefGoogle Scholar
  126. 126.
    Burgstaller, P., Jenne, A., and Blind, M. (2002) Aptamers and aptazymes: accelerating small molecule drug discovery. Curr. Opin. Drug Discov. Dev. 5, 690–700.Google Scholar
  127. 127.
    Kubinyi, H. (2002) High throughput in drug discovery. Drug Discov. Today 7, 707–709.PubMedCrossRefGoogle Scholar
  128. 128.
    Ilag, L. L., Ng, J. H., Beste, G., and Henning, S. W. (2002) Emerging high-throughput drug target validation technologies. Drug Discov. Today 7, S136–S142.PubMedCrossRefGoogle Scholar
  129. 129.
    Hardy, L. W. and Peet, N. P. (2004) The multiple orthogonal tools approach to define molecular causation in the validation of druggable targets. Drug Discov. Today 9, 117–126.PubMedCrossRefGoogle Scholar
  130. 130.
    Flook, P. K., Yan, L., and Szalma, S. (2003) Target validation through high throughput proteomics analysis. Targets 2, 217–223.CrossRefGoogle Scholar
  131. 131.
    Harris, S. (2001) Transgenic knockouts as part of high-throughput, evidence-based target selection and validation strategies. Drug Discov. Today 6, 628–636.PubMedCrossRefGoogle Scholar
  132. 132.
    Xin, H., Bernal, A., Amato, F. A., et al. (2004) High-throughput siRNA-based functional target validation. J. Biomol. Screen. 9, 286–293.PubMedCrossRefGoogle Scholar
  133. 133.
    Taylor, M. F., Wiederholt, K., and Sverdrup, F. (1999) Antisense oligonucleotides: a systematic high-throughput approach to target validation and gene function determination. Drug Discov. Today 4, 562–567.PubMedCrossRefGoogle Scholar
  134. 134.
    Sinibaldi, R. (2004) Gene expression analysis and R&D. Drug Discov. World 5, 37–43.Google Scholar
  135. 135.
    Sundberg, S. A., Chow, A., Nikiforov, T., and Wada, H. G. (2000) Microchip-based systems for target validation and HTS. Drug Discov. Today 5, 92–103.PubMedCrossRefGoogle Scholar
  136. 136.
    Huels, C., Muellner, S., Meyer, H. E., and Cahill, D. J. (2002) The impact of protein biochips and microarrays on the drug development process. Drug Discov. Today 7, S119–S124.PubMedCrossRefGoogle Scholar
  137. 137.
    Barsky, V., Perov, A., Tokalov, S., et al. (2002) Fluorescence data analysis on gel-based biochips. J. Biomol. Screen. 7, 247–257.PubMedCrossRefGoogle Scholar
  138. 138.
    Rubina, A. Y., Dementieva, E. I., Stomakhin, A. A., et al. (2003) Hydrogel-based protein microchips: manufacturing, properties, and applications. Biotechniques 34, 1008–1022.PubMedGoogle Scholar
  139. 139.
    Matthews, D. and Kopczynski, J. (2001) Using model-system genetics for drug-based target discovery. Drug Discov. Today 6, 141–149.PubMedCrossRefGoogle Scholar
  140. 140.
    Tornell, J. and Snaith, M. (2002) Transgenic systems in drug discovery: from target identification to humanized mice. Drug Discov. Today 7, 461–470.PubMedCrossRefGoogle Scholar
  141. 141.
    Abuin, A., Holt, K. H., Platt, K. A., Sands, A. T., and Zambrowicz, B. P. (2002) Fullspeed mammalian genetics: in vivo target validation in the drug discovery process. Trends Biotechnol. 20, 36–42.PubMedCrossRefGoogle Scholar
  142. 142.
    Russ, A., Stumm, G., Augustin, M., Sedlmeir, R., Wattler, S., and Nehls, M. (2002) Random mutagenesis in the mouse as a toll in drug discovery. Drug Discov. Today 7, 1175–1183.PubMedCrossRefGoogle Scholar
  143. 143.
    Rubinstein, A. L. (2003) Zebrafish: from disease modeling to drug discovery. Curr. Opin. Drug Discov. Devel. 6, 218–223.PubMedGoogle Scholar
  144. 144.
    Sumanas, S. and Lin, S. (2004) Zebrafish as a model system for drug target screening and validation. Drug Discov. Today Targets 3, 89–96.CrossRefGoogle Scholar
  145. 145.
    Sommer, R. J. (2000) Comparative genetics: a third model nematode species. Curr. Biol. 10, R879–R881.PubMedCrossRefGoogle Scholar
  146. 146.
    Sternberg, P. W. and Han, M. (1998) Genetics of RAS signaling in C. elegans. Trends Genet. 14, 466–472.PubMedCrossRefGoogle Scholar
  147. 147.
    Lee, J., Nam, S., Hwang, S. B., et al. (2004) Functional genomic approaches using the nematode Caenorhabditis elegans as a model system. J. Biochem. Mol. Biol. 37, 107–113.PubMedCrossRefGoogle Scholar
  148. 148.
    Wassarman, D. A., Therrien, M., and Rubin, G. M. (1995) The Ras signaling pathway in Drosophila. Curr. Opin. Genet. Dev. 5, 44–50.PubMedCrossRefGoogle Scholar
  149. 149.
    VITA (Validation In Vivo of Targets and Assays for Antiinfectives) technology (http://www.cubist.com/ar2000text/discovery.html).
  150. 150.
    Chopra, I. (2000) New drugs for the superbugs. Microbiol. Today 27, 4–6.Google Scholar
  151. 151.
    Jackson, L. K. and Phillips, M. A. (2002) Target validation for drug discovery in parasitic organisms. Curr. Top. Med. Chem. 2, 425–438.PubMedCrossRefGoogle Scholar
  152. 152.
    Carter, C. W. Jr. and Sweet, R. M. (eds.) (2003) Methods in Enzymology. Volume 368: Macromolecular Crystallography, Part C, Academic, San Diego.Google Scholar
  153. 153.
    Downing, A. K. (2004) Protein NMR Techniques, 2nd ed. Humana, Totowa, NJ.Google Scholar
  154. 154.
    Wallin, E. and Von Heijne, G. (1998) Genome-wide analysis of integral membrane proteins from eubacterial, archaean, and eukaryotic organisms. Protein Sci. 7, 1029–1038.PubMedCrossRefGoogle Scholar
  155. 155.
    Grisshammer, R. and Tate, C. G. (1995) Overexpression of integral membrane proteins for structural studies. Q. Rev. Biophys. 28, 315–422.PubMedCrossRefGoogle Scholar
  156. 156.
    Eswar, N., John, B., Mirkovic, N., et al. (2003) Tools for comparative protein structure modeling and analysis. Nucleic Acids Res. 31, 3375–3380.PubMedCrossRefGoogle Scholar
  157. 157.
    Fiser, A. and Sali, A. (2003) Modeller: generation and refinement of homology-based protein structure models. Methods Enzymol. 374, 461–491.PubMedCrossRefGoogle Scholar
  158. 158.
    Topham, C. M., Thomas, P., Overington, J. P., Johnson, M. S., Eisenmenger, F., and Blundell, T. L. (1990) An assessment of COMPOSER: a rule-based approach to modelling protein structure. Biochem. Soc. Symp. 57, 1–9.PubMedGoogle Scholar
  159. 159.
    Protein Structure Prediction Center, http://predictioncenter.llnl.gov.
  160. 160.
    Moult, J., Fidelis, K., Zemla, A., and Hubbard, T. (2003) Critical assessment of methods of protein structure prediction (CASP)-round V. Proteins 53(Suppl. 6), 334–339.PubMedCrossRefGoogle Scholar
  161. 161.
    Laskowski, R. A., MacArthur, M. W., Moss, D. S., and Thornton, J. M. (1993) PROCHECK: a program to check the stereochemical quality of protein structures. J. Appl. Crystallogr. 26, 283–291 (http://www.biochem.ucl.ac.uk/~roman/procheck/procheck.html).CrossRefGoogle Scholar
  162. 162.
  163. 163.
    Godzik, A., Kolinski, A., and Skolnick, J. (1993) De novo and inverse folding predictions of protein structure and dynamics. J. Comput. Aided Mol. Des. 7, 397–438 (http://www.tripos.com/admin/LitCtr/matchmaker.pdf).PubMedCrossRefGoogle Scholar
  164. 164.
    Vriend, G. (1990) WHAT IF: a molecular modeling and drug design program. J. Mol. Graph. 8, 52–56 (http://cmbi.kun.nl/whatif/).PubMedCrossRefGoogle Scholar
  165. 165.
    Sippl, M. J. (1993) Recognition of errors in three-dimensional structures of proteins. Proteins 17, 355–362 (http://smft.www.came.sbg.ac.at/came-frames/prosa.html).PubMedCrossRefGoogle Scholar
  166. 166.
    Luthy, R., Bowie, J. U., and Eisenberg, D. (1992) Assessment of protein models with three-dimensional profiles. Nature 356, 83–85 (http://www.accelrys.com/products/datasheets/i2_profiles_3d_data.pdf).PubMedCrossRefGoogle Scholar
  167. 167.
    Myers, P. L. (1997) Will combinatorial chemistry deliver real medicines? Curr. Opin. Biotechnol. 8, 701–707.PubMedCrossRefGoogle Scholar
  168. 168.
    Fernandes, P. B. (1998) Technological advances in high-throughput screening. Curr. Opin. Chem. Biol. 2, 597–603.PubMedCrossRefGoogle Scholar
  169. 169.
    Entzeroth, M. (2003) Emerging trends in high-throughput screening. Curr. Opin. Pharmacol. 3, 522–529.PubMedCrossRefGoogle Scholar
  170. 170.
    Clark, D. E. and Pickett, S. D. (2000) Computational methods for the prediction of “druglikeness.” Drug Discov. Today 5, 49–58.PubMedCrossRefGoogle Scholar
  171. 171.
    Kubinyi, H. (1998) Structure-based design of enzyme inhibitors and receptor ligands. Curr. Opin. Drug Discov. Dev. 1, 4–15.Google Scholar
  172. 172.
    Ivanov, A. S., Dubanov, A. V., Skvortsov, V. S., and Archakov, A. I. (2002) Computer aided drug design based on structure of macromolecular target: I. Search and description of ligand binding site in target protein. Vopr. Med. Khim. 48, 304–315 (in Russian).PubMedGoogle Scholar
  173. 173.
    Hoffmann, D., Kramer, B., Washio, T., Steinmetzer, T., Rarey, M., and Lengauer, T. (1999) Two-stage method for protein-ligand docking. J. Med. Chem. 42, 4422–4433.PubMedCrossRefGoogle Scholar
  174. 174.
    Hubbard, R. E. (1997) Can drugs be designed? Curr. Opin. Biotechol. 8, 696–700.CrossRefGoogle Scholar
  175. 175.
    Flohr, S., Kurz, M., Kostenis, E., Brkovich, A., Fournier, A., and Klabunde. T. (2002) Identification of nonpeptidic urotensin II receptor antagonists by virtual screening based on a pharmacophore model derived from structure-activity relationships and nuclear magnetic resonance studies on urotensin II. J. Med. Chem. 45, 1799–1805.PubMedCrossRefGoogle Scholar
  176. 176.
    Ghose, A. K. and Wendoloski, J. J. (1998) Pharmacophore modeling: methods, experimental verification and applications, in Perspectives in Drug Discovery and Design, vol. 9–11, pp. 253–271.CrossRefGoogle Scholar
  177. 177.
    Kettmann, V. and Holtje, H.-D. (1998) Mapping of the benzothiazepine binding site on the calcium channel. Quant. Struct.-Act. Relat. 17, 91–101.CrossRefGoogle Scholar
  178. 178.
    Zbinden, P., Dobler, M., Folkers, G., and Vedani, A. (1998) PrGen: pseudoreceptor modeling using receptor-mediated ligand alignment and pharmacophore equilibration. Quant. Struct.-Act. Relat. 17, 122–129.CrossRefGoogle Scholar
  179. 179.
    Schleifer, K.-J. (2000) Pseudoreceptor model for ryanodine derivatives at calcium release channels. J. Comput.-Aided Mol. Des. 14, 467–475.PubMedCrossRefGoogle Scholar
  180. 180.
    Veselovsky, A. V., Tikhonova, O. V., Skvortsov, V. S., Medvedev, A. E., and Ivanov, A. S. (2001) An approach for visualization of active site of enzymes with unknown threedimensional structures. QSAR SAR Environ. Res. 12, 345–358.CrossRefGoogle Scholar
  181. 181.
    Kubinyi, H. (1994) Variable selection in QSAR studies. I. An evolutionary algorithm. Quant. Struct.-Act. Relat. 13, 285–294.Google Scholar
  182. 182.
    Kim, K. H. (1995) Comparative molecular field analysis (CoMFA), in Molecular Simulation and Drug Design (Dean, P. M., ed.), Blackie Academic & Professional, London, UK, pp. 291–331.Google Scholar
  183. 183.
    Cramer, R. D. III, Petterson, D. E., and Bunce, J. D. (1988) Comparative molecular field analysis (CoMFA). 1. Effect of share on binding of steroids to carrier proteins. J. Am. Chem. Soc. 110, 5959–5967.CrossRefGoogle Scholar
  184. 184.
    Sippl, W. (2000) Receptor-based 3D QSAR analysis of estrogen receptor ligands-merging the accuracy of receptor-based alignments with the computational efficiency of ligandbased methods. J. Comput.-Aided Mol. Des. 14, 559–572.PubMedCrossRefGoogle Scholar
  185. 185.
    Sippl, W., Contreras, J.-M., Parrot, I., Rival, Y. M., and Wermuth, C. G. (2001) Structurebased 3D QSAR and design of novel acetylcholineesterase inhibitors. J. Comput.-Aided Mol. Des. 15, 395–410.PubMedCrossRefGoogle Scholar
  186. 186.
    MDL Information Systems, http://www.mdl.com.
  187. 187.
    UNITY® 4.4.2 Tripos Inc., http://www.tripos.com.
  188. 188.
    Kuntz, I. D., Blaney, J. M., Oatley, S. J., Landridge, R., and Ferrin, T. E. (1982) A geometric approach to macromolecule-ligand interactions. J. Mol. Biol. 161, 269–288.PubMedCrossRefGoogle Scholar
  189. 189.
    Ewing, T. J. A., Makino, S., Skillman, A. G., and Kuntz, I. D. (2001) DOCK 4.0: Search strategies for automated molecular docking of flexible molecule databases. J. Comput.-Aided Mol. Des. 15, 411–428.PubMedCrossRefGoogle Scholar
  190. 190.
    BioSolveIT GmbH, http://www.biosolveit.de.
  191. 191.
  192. 192.
    Raevsky, O. A., Trepalin, S. V., Trepalina, E. P., Gerasimenko, V. A., and Raevskaja, O. E. (2002) SLIPPER-2001—software for predicting molecular properties on the basis of physicochemical descriptors and structural similarity. J. Chem. Inf. Comput. Sci. 42, 540–549.PubMedGoogle Scholar
  193. 193.
    Raevsky, O. A., Schaper, K.-J., van de Waterbeemd, H., and McFarland, J. W. (2000) Hydrogen bond contributions to properties and activities of chemicals and drugs, in Molecular Modelling and Prediction of Bioactivity (Gundertofe, K. and Jorgensen, F., eds.), Kluwer Academic/Plenum, New York, pp. 221–227.Google Scholar
  194. 194.
  195. 195.
    Molecular Networks GmbH, http://www.mol-net.de.
  196. 196.
    Pearlman, R. S. (1987) Rapid generation of high quality approximate 3-dimension molecular structures. Chem. Des. Auto. News 2, 1–7.Google Scholar
  197. 197.
    Pearlman, R.S. “Concord User’s Manual,” distributed by Tripos Inc., http://www.tripos.com.
  198. 198.
    Pearlman, R. S. and Balducci, R. (1998) Confort: a novel algorithm for conformational analysis. National Meeting of the American Chemical Society, New Orleans. (http://www.tripos.com/sciTech/inSilicoDisc/media/LITCTR/CONFORT.PDF).
  199. 199.
    CONFLEX Corporation, http://www.conflex.us.
  200. 200.
    Jones, G., Willett, P., Glen, R., Leach, A. R., and Taylor, R. (1997) Development and validation of a genetic algorithm for flexible docking. J. Mol. Biol. 267, 727–748.PubMedCrossRefGoogle Scholar
  201. 201.
    Muegge, I. and Martin, Y. C. (1999) A general and fast scoring function for proteinligand interactions: a simplified potential approach. J. Med. Chem. 42, 791–804.PubMedCrossRefGoogle Scholar
  202. 202.
    Ewing, T. J. A. and Kuntz, I. D. (1996) Critical evaluation of search algorithms for automated molecular docking and database screening. J. Comp. Chem. 18, 1175–1189 (http://dock.compbio.ucsf.edu).CrossRefGoogle Scholar
  203. 203.
    Eldridge, M. D., Murray, C. W., Auton, T. R., Paolini, G. V., and Mee, R. P. (1997) Empirical scoring functions: I. The development of a fast empirical scoring function to estimate the binding affinity of ligands in receptor complexes. J. Comput.-Aided Mol. Des. 11, 425–445.PubMedCrossRefGoogle Scholar
  204. 204.
    Wang, R., Liu, L., Lai, L., and Tang, Y. (1998) SCORE: A new empirical method for estimating the binding affinity of a protein-ligand complex. J. Mol. Model. 4, 379–394.CrossRefGoogle Scholar
  205. 205.
    Krepets, V. V., Belkina, N. V., Skvortsov, V. S., and Ivanov, A. S. (2000) Prediction of binding affinities for protein-ligand complexes by using non-linear models. Vopr. Med. Chim. 46, 462–474 (in Russian).Google Scholar
  206. 206.
    Clark, R. D., Strizhev, A., Leonard, J. M., Blake, J. F., and Matthew, J. B. (2002) Consensus scoring for ligand/protein interactions. J. Mol. Graph. Model. 20, 281–295.PubMedCrossRefGoogle Scholar
  207. 207.
    Pearlman, D. A. and Rao, B. G. (1998) Free energy calculations: methods and applications, in Encyclopedia of Computational Chemistry (von Schleyer, P. R., Allinger, N. L., Clark, T., Gasteiger, J., Kollman, P. A., and Schaefer, H. F. III, eds.), John Wiley, Chichester, UK, pp. 1036–1061.Google Scholar
  208. 208.
    Bohm, H. J. (1992) The computer program LUDI: a new method for the de novo design of enzyme inhibitors. J. Comput.-Aided. Mol. Des. 6, 61–78.PubMedCrossRefGoogle Scholar
  209. 209.
    Lawrence, M. C. and David, P. C. (1992) CLIX: a search algorithm for finding novel ligands capable of binding protein of known three-dimensional structure. Proteins: Struct. Funct. Genet. 12, 31–41.CrossRefGoogle Scholar
  210. 210.
    Bartlett, P. A., Shea, G. T., Telfer, S. J., and Waterman, S. (1989) CAVEAT: a program to facilitate the structure-derived design of biologically active molecules, in Molecular Recognition in Chemical and Biological Problems, vol. 78 (Roberts, S. M., ed.), Royal Chemistry Society, London, UK, pp. 182–196.Google Scholar
  211. 211.
  212. 212.
    Poroikov, V. V., Filimonov, D. A., Borodina, Yu. V., Lagunin, A. A., and Kos, A. (2000) Robustness of biological activity predicting by computer program PASS for noncongeneric sets of chemical compounds. J. Chem. Inf. Comput. Sci. 40, 1349–1355 (http://www.ibmh.msk.su/PASS/).PubMedGoogle Scholar
  213. 213.
  214. 214.
  215. 215.
    Nagata, K. and Handa, H. (eds.). (2000) Real-Time Analysis of Biomolecular Interactions: Applications of Biacore, Springer-Verlag, Tokyo.Google Scholar
  216. 216.
    Rich, R. L. and Myszka, D. G. (2000) Advances in surface plasmon resonance biosensor analysis. Curr. Opin. Biotechnol. 11, 54–61.PubMedCrossRefGoogle Scholar
  217. 217.
    Altschul, S. F., Madden, T. L., Schaffer, A. A., Zhang, J., Zhang, Z., Miller, W., and Lipman, D. J. (1997) Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res. 25, 3389–3402.PubMedCrossRefGoogle Scholar
  218. 218.
    Freiberg, C., Wieland, B., Spaltmann, F., Ehlert, K., Brotz, H., and Labischinski, H. (2001) Identification of novel essential Escherichia coli genes conserved among pathogenic bacteria. J. Mol. Microbiol. Biotechnol. 3, 483–489.PubMedGoogle Scholar
  219. 219.
    Belkina, N. V., Skvortsov, V. S., Ivanov, A. S., and Archakov, A. I. (1998) Modeling of a three-dimensional structure of cytochrome P-450 1A2 and search for its new ligands. Vopr. Med. Khim. 44, 464–473 (in Russian).PubMedGoogle Scholar
  220. 220.
    Kellogg, G. E., Semus, S. F., and Abraham, D. J. (1991) HINT—A new method of empirical hydrophobic field calculation for CoMFA. J. Comput.-Aided Mol. Des. 5, 545–552.PubMedCrossRefGoogle Scholar
  221. 221.
    HINT® (Hydropathic INTeractions), http://www.edusoft-lc.com/hint/.
  222. 222.
  223. 223.
    Advanced Chemistry Development (ACD), http://www.acdlabs.com/products.
  224. 224.
    Schonbrun, J., Wedemeyer, W. J., and Baker, D. (2002) Protein structure prediction in 2002. Curr. Opin. Struct. Biol. 12, 348–354.PubMedCrossRefGoogle Scholar
  225. 225.
    Fiser, A., Do, R. K., and Sali, A. (2000) Modeling of loops in protein structures. Protein Sci. 9, 1753–1773.PubMedCrossRefGoogle Scholar
  226. 226.
    Ooms, F. (2000) Molecular modeling and computer aided drug design: examples of their applications in medicinal chemistry. Curr. Med. Chem. 7, 141–158.PubMedGoogle Scholar
  227. 227.
    Amzel, L. M. (1998) Structure-based drug design. Curr. Opin. Biotechnol. 9, 366–369.PubMedCrossRefGoogle Scholar
  228. 228.
    Yamamoto, K., Masuno, H., Choi, M., et al. (2000) Three-dimensional modeling of and ligand docking to vitamin D receptor ligand binding domain. Proc. Natl. Acad. Sci. USA 97, 1467–1472.PubMedCrossRefGoogle Scholar
  229. 229.
    Vangrevelinghe, E., Zimmermann, K., Schoepfer, J., Portmann, R., Fabbro, D., and Furet, P. (2003) Discovery of a potent and selective protein kinase CK2 inhibitor by high-throughput docking. J. Med. Chem. 46, 2656–2662.PubMedCrossRefGoogle Scholar

Copyright information

© Humana Press Inc. 2006

Authors and Affiliations

  • Alexis S. Ivanov
    • 1
  • Alexander V. Veselovsky
    • 1
  • Alexander V. Dubanov
    • 1
  • Vladlen S. Skvortsov
    • 1
  1. 1.Institute of Biomedial ChemistryRAMSMoscowRussia

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