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.
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References
Lohse, M. J. (1998) The future of pharmacology. Trends Pharmacol. Sci. 19, 198–200.
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.
National Center for Biotechnology Information, http://www.ncbi.nlm.nih.gov.
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).
DNA Data Bank of Japan, http://www.ddbj.nig.ac.jp.
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).
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)
Tatusov, R. L., Fedorova, N. D., Jackson, J. D., et al. (2003) The COG database: an updated version includes eukaryotes. BMC Bioinfor. 4, 41.
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.
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/).
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/).
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).
Uchiyama, I. (2003) MBGD: microbial genome database for comparative analysis. Nucleic Acids Res. 31, 58–62 (http://mbgd.genome.ad.jp).
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).
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).
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).
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/).
NCGR, National Center for Genome Resources, http://www.ncgr.org/pathdb/.
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).
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).
Frishman, D., Mokrejs, M., Kosykh, D., et al. (2003) The PEDANT genome database. Nucleic Acids Res. 31, 207–211 (http://pedant.gsf.de).
Galperin, M. Y. (2004) The Molecular Biology Database Collection: 2004 update. Nucleic Acids Res. 32(database issue), D3–D22.
Freiberg, C. (2001) Novel computation methods in anti-microbial target identification. Drug Discov. Today 6, S72–S80.
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).
National Cancer Institute: Pure Chemicals Repository, http://www.dtp.nci.nih.gov/branches/dscb/repo_open.html.
MDL Drug Data Report, MDL Information Systems, http://www.mdl.com.
Comprehensive Medicinal Chemistry, MDL Information Systems, http://www.mdl.com.
ASINEX Ltd., http://www.asinex.com.
ChemBridge Corporation, http://www.chembridge.com.
Maybridge, http://www.maybridge.com.
SYBYL 6.7.1, Tripos Inc., http://www.tripos.com.
Spaltmann, F., Blunck, M., and Ziegelbauer, K. (1999) Computer-aided target selectionprioritizing targets for antifungal drug discovery. Drug Discov. Today 4, 17–26.
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).
Genedatar, http://www.genedata.com.
The Perl Directory, http://www.perl.org.
Python, http://www.python.org.
Mangalam, H. (2002) The Bio* toolkits—a brief overview. Brief Bioinform. 3, 296–302.
Bioperl, http://www.bioperl.org.
Biopython, http://www.biopython.org.
Entrez Programming Utilities, http://www.ncbi.nlm.nih.gov/entrez/query/static/eutils_help.html.
Accelrys, http://www.accelrys.com.
Case, D. A., Darden, T. A., Cheatham, T. E. III, et al. (2004) AMBER 8, University of California, San Francisco (http://amber.scripps.edu).
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).
Allsop, A. E. (1998) New antibiotic discovery, novel screens, novel targets and impact of microbial genomics. Curr. Opin. Microbiol. 1, 530–534.
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.
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.
Rost, B., Liu, J., Wrzeszczynski, K. O., and Ofran, Y. (2003) Automatic prediction of protein fuction. Cell. Mol. Life Sci. 60, 2637–2650.
Eisenberg, D., Marcotte, E. M., Xenarios, I., and Yeates, T. O. (2000) Protein function in the post-genomic era. Nature 2000 405, 823–826.
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.
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).
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.
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.
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.
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.
Boguslavsky, J. (2002) Target validation: finding a needle in a haystack. Drug Discov. Dev. 5, 41–48.
Lau, A. T., He, Q. Y., and Chiu, J. F. (2003) Proteomic technology and its biomedical application. Acta Biochim. Biophys. Sinica 35, 965–975.
Walgren, J. L. and Thompson, D. C. (2004) Application of proteomic technologies in the drug development process. Toxicol. Lett. 149, 377–385.
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.
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.
Lopez, M. F. (1998) Proteomic databases: roadmaps for drug discovery. Am. Clin. Lab. 17, 16–18.
Jones, S. and Thornton, J. M. (1995) Protein-protein interactions: a review of protein dimer structures. Prog. Biophys. Mol. Biol. 63, 31–65.
Wilkinson, K. D. (2004) Quantitative analysis of protein-protein interactions. Methods Mol. Biol. 261, 15–32.
Nedelkov, D. and Nelson, R. W. (2003) Delineating protein-protein interactions via biomolecular interaction analysis-mass spectrometry. J. Mol. Recognit. 16, 9–14.
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.
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.
Butcher, S. P. (2003) Target discovery and validation in the post-genomic era. Neurochem. Res. 28, 367–371.
Williams, M. (2003) Target validation. Curr. Opin. Pharmacol. 3, 571–577.
Cowman, A. F. and Crabb, B. S. (2003) Functional genomics: identifying drug targets for parasitic diseases. Trends Parasitol. 19, 538–543.
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.
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.
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).
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.
Jacobs, G. H. (1992) Determination of the base recognition positions of zinc finger from sequence-analysis. EMBO J. 11, 4507–4517.
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.
Suzuki, M., Gerstein, M. B., and Yagi, N. (1994) Stereochemical basis of DNA recognition by Zn fingers. Nucleic Acids Res. 22, 3397–3405.
Cech, T. R. (1992) Ribozyme engineering. Curr. Opin. Struct. Biol. 2, 605–609.
Breaker, R. R. (1997) In vitro selection of catalytic polynucleotides. Chem. Rev. 97, 371–390.
Usman, N., Beigelman, L., and McSwiggen, J. A. (1996) Hammerhead ribozyme engineering. Curr. Opin. Struct. Biol. 6, 527–533.
Uhlenbeck, O. C. (1987) A small catalytic oligoribonucleotide. Nature 328, 596–600.
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.
Goodchild, J. (2002) Hammerhead ribozymes for target validation. Expert Opin. Ther. Targets 6, 235–247.
Lehner, B., Fraser, A. G., and Sanderson, C. M. (2004) Technique review: how to use RNA interference. Brief Funct. Genomic Proteomic 3, 68–83.
Jain, K. K. (2004) RNAi and siRNA in target validation. Drug Discov. Today 9, 307–309.
Henning, S. W. and Beste, G. (2002) Loss-function strategies in drug target validation. Curr. Drug Discov. 5, 17–21.
Baker, B. F. and Monia, B. P. (1999) Novel mechanisms for antisense mediated regulation of gene expression. Biochim. Biophys. Acta 1489, 3–18.
Inouye, M. (1988) Antisense RNA: its functions and applications in gene regulation—a review. Gene 72, 25–34.
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.
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.
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.
Taylor, M. F. (2001) Target validation and functional analyses using antisense oligonucleotides. Expert Opin. Ther. Targets 5, 297–301.
Dean, N. M. (2001) Functional genomics and target validation approaches using antisense oligonucleotide technology. Curr. Opin. Biotechnol. 12, 622–625.
Koller, E., Gaarde, W. A., and Monia, B. P. (2000) Elucidating cell signaling mechanisms using antisense technology. Trends Pharmacol. Sci. 21, 142–148.
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.
Ho, S. P. and Hartig, P. R. (1999) Antisense oligonucleotides for target validation in the CNS. Curr. Opin. Mol. Ther. 1, 336–343.
Somagenics, http://www.somagenics.com/platform.html.
Pellestor, F. and Paulasova, P. (2004) The peptide nucleic acids, efficient tools for molecular diagnosis (review). Int. J. Mol. Med. 13, 521–525.
Gambari, R. (2001) Peptide-nucleic acids (PNAs): a tool for the development of gene expression modifiers. Curr. Pharm. Des. 7, 1839–1862.
Demidov, V. V. (2002) PNA comes of age: from infancy to maturity. Drug Discov. Today 7, 153–155.
Ganesh, K. N. and Nielsen, P. E. (2000) Peptide nucleic acids: analogs and derivatives. Curr. Organic Chem. 4, 916–928.
Winters, T. A. (2000) Gene targeting agents, new opportunities for rational drug development. Curr. Opin. Mol. Ther. 2, 670–681.
Nielsen, P. E. (2000) Antisense peptide nucleic acids. Curr. Opin. Mol. Ther. 2, 282–287.
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.
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.
Banker, D. D. (2001) Monoclonal antibodies: a review. Indian J. Med. Sci. 55, 651–654.
Peet, N. P. (2003) What constitutes target validation? Targets 2, 125–127.
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.
Jay, D. G. (1988) Selective destruction of protein function by chromophore-assisted laser inactivation. Proc. Natl. Acad. Sci. USA 85, 5454–5458.
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).
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.
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.
Bradbury, A. (2003) scFvs and beyond. Drug Discov. Today 8, 737–739.
Chowdhury, P. S. and Vasmatzis, G. (2003) Engineering scFvs for improved stability. Methods Mol. Biol. 207, 237–254.
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.
Toleikis, L., Broders, O., and Dubel, S. (2004) Cloning single-chain antibody fragments (scFv) from hybridoma cells. Methods Mol. Med. 94, 447–458.
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.
Donini, M., Morea, V., Desiderio, A., et al. (2003) Engineering stable cytoplasmic intrabodies with designed specificity. J. Mol. Biol. 330, 323–332.
Cohen, P. A. (2002) Intrabodies: targeting scFv expression to eukaryotic intracellular compartments. Methods Mol. Biol. 178, 367–378.
Marasco, W. A. (1997) Intrabodies: turning the humoral immune system outside in for intracellular immunization. Gene Ther. 4, 11–15.
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).
Rimmele, M. (2003) Nucleic acid aptamers as tools and drugs: recent developments. Chembiochemistry 4, 963–971.
Burgstaller, P., Girod, A., and Blind, M. (2002) Aptamers as tools for target prioritization and lead identification. Drug Discov. Today 7, 1221–1228.
Toulme, J. J., Di Primo, C., and Boucard, D. (2004) Regulating eukaryotic gene expression with aptamers. FEBS Lett. 567, 55–62.
Ulrich, H., Martins, A. H., and Pesquero, J. B. (2004) RNA and DNA aptamers in cytomics analysis. Cytometry 59A, 220–231.
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.
Burgstaller, P., Jenne, A., and Blind, M. (2002) Aptamers and aptazymes: accelerating small molecule drug discovery. Curr. Opin. Drug Discov. Dev. 5, 690–700.
Kubinyi, H. (2002) High throughput in drug discovery. Drug Discov. Today 7, 707–709.
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.
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.
Flook, P. K., Yan, L., and Szalma, S. (2003) Target validation through high throughput proteomics analysis. Targets 2, 217–223.
Harris, S. (2001) Transgenic knockouts as part of high-throughput, evidence-based target selection and validation strategies. Drug Discov. Today 6, 628–636.
Xin, H., Bernal, A., Amato, F. A., et al. (2004) High-throughput siRNA-based functional target validation. J. Biomol. Screen. 9, 286–293.
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.
Sinibaldi, R. (2004) Gene expression analysis and R&D. Drug Discov. World 5, 37–43.
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.
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.
Barsky, V., Perov, A., Tokalov, S., et al. (2002) Fluorescence data analysis on gel-based biochips. J. Biomol. Screen. 7, 247–257.
Rubina, A. Y., Dementieva, E. I., Stomakhin, A. A., et al. (2003) Hydrogel-based protein microchips: manufacturing, properties, and applications. Biotechniques 34, 1008–1022.
Matthews, D. and Kopczynski, J. (2001) Using model-system genetics for drug-based target discovery. Drug Discov. Today 6, 141–149.
Tornell, J. and Snaith, M. (2002) Transgenic systems in drug discovery: from target identification to humanized mice. Drug Discov. Today 7, 461–470.
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.
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.
Rubinstein, A. L. (2003) Zebrafish: from disease modeling to drug discovery. Curr. Opin. Drug Discov. Devel. 6, 218–223.
Sumanas, S. and Lin, S. (2004) Zebrafish as a model system for drug target screening and validation. Drug Discov. Today Targets 3, 89–96.
Sommer, R. J. (2000) Comparative genetics: a third model nematode species. Curr. Biol. 10, R879–R881.
Sternberg, P. W. and Han, M. (1998) Genetics of RAS signaling in C. elegans. Trends Genet. 14, 466–472.
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.
Wassarman, D. A., Therrien, M., and Rubin, G. M. (1995) The Ras signaling pathway in Drosophila. Curr. Opin. Genet. Dev. 5, 44–50.
VITA (Validation In Vivo of Targets and Assays for Antiinfectives) technology (http://www.cubist.com/ar2000text/discovery.html).
Chopra, I. (2000) New drugs for the superbugs. Microbiol. Today 27, 4–6.
Jackson, L. K. and Phillips, M. A. (2002) Target validation for drug discovery in parasitic organisms. Curr. Top. Med. Chem. 2, 425–438.
Carter, C. W. Jr. and Sweet, R. M. (eds.) (2003) Methods in Enzymology. Volume 368: Macromolecular Crystallography, Part C, Academic, San Diego.
Downing, A. K. (2004) Protein NMR Techniques, 2nd ed. Humana, Totowa, NJ.
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.
Grisshammer, R. and Tate, C. G. (1995) Overexpression of integral membrane proteins for structural studies. Q. Rev. Biophys. 28, 315–422.
Eswar, N., John, B., Mirkovic, N., et al. (2003) Tools for comparative protein structure modeling and analysis. Nucleic Acids Res. 31, 3375–3380.
Fiser, A. and Sali, A. (2003) Modeller: generation and refinement of homology-based protein structure models. Methods Enzymol. 374, 461–491.
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.
Protein Structure Prediction Center, http://predictioncenter.llnl.gov.
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.
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).
Protable. http://www.tripos.com/sciTech/inSilicoDisc/media/LITCTR/PROTABLE.PDF.
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).
Vriend, G. (1990) WHAT IF: a molecular modeling and drug design program. J. Mol. Graph. 8, 52–56 (http://cmbi.kun.nl/whatif/).
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).
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).
Myers, P. L. (1997) Will combinatorial chemistry deliver real medicines? Curr. Opin. Biotechnol. 8, 701–707.
Fernandes, P. B. (1998) Technological advances in high-throughput screening. Curr. Opin. Chem. Biol. 2, 597–603.
Entzeroth, M. (2003) Emerging trends in high-throughput screening. Curr. Opin. Pharmacol. 3, 522–529.
Clark, D. E. and Pickett, S. D. (2000) Computational methods for the prediction of “druglikeness.” Drug Discov. Today 5, 49–58.
Kubinyi, H. (1998) Structure-based design of enzyme inhibitors and receptor ligands. Curr. Opin. Drug Discov. Dev. 1, 4–15.
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).
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.
Hubbard, R. E. (1997) Can drugs be designed? Curr. Opin. Biotechol. 8, 696–700.
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.
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.
Kettmann, V. and Holtje, H.-D. (1998) Mapping of the benzothiazepine binding site on the calcium channel. Quant. Struct.-Act. Relat. 17, 91–101.
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.
Schleifer, K.-J. (2000) Pseudoreceptor model for ryanodine derivatives at calcium release channels. J. Comput.-Aided Mol. Des. 14, 467–475.
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.
Kubinyi, H. (1994) Variable selection in QSAR studies. I. An evolutionary algorithm. Quant. Struct.-Act. Relat. 13, 285–294.
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.
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.
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.
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.
MDL Information Systems, http://www.mdl.com.
UNITY® 4.4.2 Tripos Inc., http://www.tripos.com.
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.
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.
BioSolveIT GmbH, http://www.biosolveit.de.
DockSearch. http://Imgdd.ibmh.msk.su/lab/docksearch.
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.
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.
Molecular Networks GmbH, http://www.mol-net.de.
Pearlman, R. S. (1987) Rapid generation of high quality approximate 3-dimension molecular structures. Chem. Des. Auto. News 2, 1–7.
Pearlman, R.S. “Concord User’s Manual,” distributed by Tripos Inc., http://www.tripos.com.
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).
CONFLEX Corporation, http://www.conflex.us.
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.
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.
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).
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.
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.
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).
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.
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.
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.
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.
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.
LeapFrog: SYBYL® 6.9.2, http://www.tripos.com/sciTech/inSilicoDisc/media/LITCTR/LEAPFROG.PDF.
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/).
Biacore 3000 preprint (http://www.biacore.com/lifesciences/products/systems_overview/3000/system_information/index.html).
Biacore S51 preprint (http://www.biacore.com/lifesciences/products/systems_overview/s51/system_information/index.html).
Nagata, K. and Handa, H. (eds.). (2000) Real-Time Analysis of Biomolecular Interactions: Applications of Biacore, Springer-Verlag, Tokyo.
Rich, R. L. and Myszka, D. G. (2000) Advances in surface plasmon resonance biosensor analysis. Curr. Opin. Biotechnol. 11, 54–61.
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.
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.
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).
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.
HINT® (Hydropathic INTeractions), http://www.edusoft-lc.com/hint/.
HyperChem, http://www.hyper.com/products/.
Advanced Chemistry Development (ACD), http://www.acdlabs.com/products.
Schonbrun, J., Wedemeyer, W. J., and Baker, D. (2002) Protein structure prediction in 2002. Curr. Opin. Struct. Biol. 12, 348–354.
Fiser, A., Do, R. K., and Sali, A. (2000) Modeling of loops in protein structures. Protein Sci. 9, 1753–1773.
Ooms, F. (2000) Molecular modeling and computer aided drug design: examples of their applications in medicinal chemistry. Curr. Med. Chem. 7, 141–158.
Amzel, L. M. (1998) Structure-based drug design. Curr. Opin. Biotechnol. 9, 366–369.
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.
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.
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Ivanov, A.S., Veselovsky, A.V., Dubanov, A.V., Skvortsov, V.S. (2006). Bioinformatics Platform Development. In: Larson, R.S. (eds) Bioinformatics and Drug Discovery. Methods in Molecular Biology, vol 316. Humana Press. https://doi.org/10.1385/1-59259-964-8:389
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