Computational systems approach for drug target discovery

Expert Opinion on Drug Discovery

Volumn 4, Issue 12, 2009, Pages 1221-1236

  Chandra, Nagasuma ^a  

^a INDIAN INSTITUTE OF SCIENCE   (India)

Author keywords

Network analysis; Pathway modeling; Simulations; Systems biology; Targetability

Indexed keywords

AMINO ACID SEQUENCE; COMPUTATIONAL SYSTEM; DRUG BIOAVAILABILITY; DRUG DESIGN; DRUG METABOLISM; DRUG RESEARCH; DRUG TARGETING; GENE SEQUENCE; GENOME; HIGH THROUGHPUT SCREENING; HOST PATHOGEN INTERACTION; METABOLIC REGULATION; PHARMACODYNAMICS; PRIORITY JOURNAL; PROTEIN PROTEIN INTERACTION; PROTEIN STRUCTURE; REVIEW; SIMULATION; SYSTEMS BIOLOGY; THEORETICAL MODEL;

EID: 73549086984     PISSN: 17460441     EISSN: None     Source Type: Journal    
DOI: 10.1517/17460440903380422     Document Type: Review

References (94)

1. Drews J. Drug Discovery: a Historical Perspective. Science 2000;287(5460):1960-1964
2. Butcher EC, Berg EL, Kunkel EJ. Systems biology in drug discovery. Nat Biotechnol 2004;22(10):1253-1259
3. Butcher E. Can cell systems biology rescue drug discovery? Nat Rev Drug Discov 2005;4(6):461-467
4. Materi W, Wishart DS. Computational systems biology in drug discovery and development: methods and applications. Drug Discov Today 2007;12(7-8):295-303
5. Lindsay MA. Target discovery. Nat Rev Drug Discov 2003;2(10):831-838
6. Leon D, Markel S. In silico technologies in drug target identification validation. CRC Press, Taylor and Francis; June 13, 2006
7. Kitano H. Systems biology: a brief overview. Science 2002;295(5560):1662- 1664
8. Aderem A. Systems biology: its practice and challenges. Cell 2005;121(4):511-513
9. Auffray C, Chen Z, Hood L. Systems medicine: the future of medical genomics and healthcare. Genome Med 2009;1(1):2
10. Noble D. From the hodgkin-huxley axon to the virtual heart. J Physiol 2007;580:15-22
11. Hasan S, Daugelat S, Rao PSS, Schreiber M. Prioritizing genomic drug targets in pathogens: application to mycobacterium tuberculosis. PLoS Comput Biol 2006;2(6):e61
12. Kauffman KJ, Prakash P, Edwards JS. Advances in flux balance analysis. Curr Opin Biotechnol 2003;14(5):491-496
13. Bakker BM, Michels PAM, Opperdoes FR, Westerhoff HV. Glycolysis in bloodstream form trypanosoma brucei can be understood in terms of the kinetics of the glycolytic enzymes. J Biol Chem 1997;272(6):3207-3215
14. Barabasi A-L, Oltvai ZN. Network biology: understanding the cell's functional organization. Nat Rev Genet 2004;5(2):101-113
15. Hardy S, Robillard PN. Phenomenological and molecular-level petri net modeling and simulation of long-term potentiation. Biosystems 2005;82(1):26-38
16. Eisenthal R, Cornish-Bowden A. Prospects for antiparasitic drugs. The case of trypanosoma brucei, the causative agent of african sleeping sickness. J Biol Chem 1998;273(10):5500-5505
17. Stelling J. Mathematical models in microbial systems biology. Curr Opin Microbiol 2004;7(5):513-518
18. Wiechert W. Modeling and simulation: tools for metabolic engineering. J Biotechnol 2002;94(1):37-63
19. Cascante M, Boros LG, Comin-Anduix B, et al. Metabolic control analysis in drug discovery and disease. Nat Biotechnol 2002;20(3):243-249
20. Covert M, Schilling C, Famili I, et al. Metabolic modeling of microbial strains in silico. Trends Biochem Sci 2001;26(3):179-186
21. Ogata H, Goto S, Sato K, et al. KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res 1999;27(1):29-34
22. Karp PD, Ouzounis CA, Moore-Kochlacs C, et al. Expansion of the BioCyc collection of pathway/genome databases to 160 genomes. Nucleic Acids Res 2005;33(19):6083-6089
23. Le Novere N, Bornstein B, Broicher A, et al. Biomodels database: a free, centralized database of curated, published, quantitative kinetic models of biochemical and cellular systems. Nucleic Acids Res 2006;34(Database issue):D689-91
24. Hucka M, Finney A, Sauro HM, et al. The systems biology markup language (SBML): a medium for representation and exchange of biochemical network models. Bioinformatics 2003;19(4):524-531
25. Raman K, Rajagopalan P, Chandra N. Principles and practices of pathway modelling. Curr Bioinform 2006;1:147-160
26. Price ND, Reed JL, Palsson BO. Genome-scale models of microbial cells: evaluating the consequences of constraints. Nat Rev Microbiol 2004;2(11):886-897
27. Albert M-A, Haanstra JR, Hannaert V, et al. Experimental and in silico analysis of glycolytic flux control in bloodstream-form trypanosoma brucei. J Biol Chem 2005;280(31):28306-28315
28. Yang K, Ma W, Liang H, et al. Dynamic simulations on the arachidonic acid metabolic network. PLoS Comput Biol 2007;3(3):0523-530
29. Feist AM, Herrgard MJ, Thiele I, et al. Reconstruction of biochemical networks in microorganisms. Nat Rev Microbiol 2009;7(2):129-143
30. Lee JM, Gianchandani EP, Papin JA. Flux balance analysis in the era of metabolomics. Brief Bioinform 2006 2006;7(2):140-150
31. Duarte NC, Herrgayrd MJ, Palsson BO. Reconstruction and validation of saccharomyces cerevisiae iND750, a fully compartmentalized genome-scale metabolic model. Genome Res 2004;14(7):1298-1309
32. Förster J, Famili I, Palsson BO, Nielsen J. Large-scale evaluation of in silico gene deletions in Saccharomyces cerevisiae. OMICS 2003;7(2):193-202
33. Edwards JS, Palsson BO. The Escherichia coli MG1655 in silico metabolic genotype: Its definition, characteristics, and capabilities. Proc Natl Acad Sci USA 2000;97(10):5528-5533
34. Raman K, Rajagopalan P, Chandra N. Flux balance analysis of mycolic acid pathway: targets for anti-tubercular drugs. PLoS Comput Biol 2005;1(5):e46
35. Beste D, Hooper T, Stewart G, et al. GSMN-TB: a web-based genome-scale network model of mycobacterium tuberculosis metabolism. Genome Biol 2007;8(5):R89
36. Jamshidi N, Palsson B. Investigating the metabolic capabilities of mycobacterium tuberculosis H37Rv using the in silico strain iNJ661 and proposing alternative drug targets. BMC Syst Biol 2007;1(1):26
37. Lee D-S, Burd H, Liu J, et al. Comparative genome-scale metabolic reconstruction and flux balance analysis of multiple staphylococcus aureus genomes identify novel antimicrobial drug targets. J Bacteriol 2009;191(12):4015-4024
38. Wildermuth M. Metabolic control analysis: biological applications and insights. Genome Biol 2000;1(6): reviews1031.1-reviews.5
39. Stephanopoulos G, Aristidou A, Nielsen J. Metabolic engineering - principles and methodologies. Academic Press; 1998
40. Raamsdonk LM, Teusink B, Broadhurst D, et al. A functional genomics strategy that uses metabolome data to reveal the phenotype of silent mutations. Nat Biotechnol 2001;19(1):45-50
41. Teusink B, Baganz F, Westerhoff HV, Oliver SG. Metabolic control analysis as a tool in the elucidation of the function of novel genes. Yeast gene analysis. San Diego: Academic Press Inc; 1998. p. 297-336
42. Agius L. The physiological role of glucokinase binding and translocation in hepatocytes. In: Weber G, Weber CEF, editors. 38th Symposium on Regulation of Enzyme Activity and Synthesis in Normal and Neoplastic Tissues; 1997 Sep 29-30; Indianapolis, Indiana: Pergamon Press Ltd; 1997. p. 303-331
43. Gerber S, Aßmus H, Bakker B, Klipp E. Drug-efficacy depends on the inhibitor type and the target position in a metabolic network-A systematic study. J Theor Biol 2008;252(3):442-455
44. Carole K, Jean-Michel F, Guillaume B. The topology of the protein network influences the dynamics of gene order: from systems biology to a systemic understanding of evolution. Artif Life 2008;14(1):149-156
45. Chautard E, Thierry-Mieg N, Ricard-Blum S. Interaction networks: from protein functions to drug discovery. A review. Pathol Biol 2009;57(4):324-333
46. B Ganter, C Giroux. Emerging applications of network and pathway analysis in drug discovery and development. Curr Opin Drug Discov Devel 2008;11:86-94
47. Verkhedkar KD, Raman K, Chandra NR, Vishveshwara S. Metabolome based reaction graphs of M. tuberculosis and M. leprae: a comparative network analysis. PLoS ONE 2007;2(9):e881
48. Strong M, Graeber TG, Beeby M, et al. Visualization and interpretation of protein networks in Mycobacterium tuberculosis based on hierarchical clustering of genome-wide functional linkage maps. Nucleic Acids Res 2003;31(24):7099-7109
49. Jensen LJ, Kuhn M, Stark M, et al. STRING 8-a global view on proteins and their functional interactions in 630 organisms. Nucl Acids Res 2009;37(Suppl 1):D412-6
50. Raman K, Yeturu K, Chandra N. targetTB: a target identification pipeline for Mycobacterium tuberculosis through an interactome, reactome and genome-scale structural analysis. BMC Syst Biol 2008;2(1):109
51. Cui T, Zhang L, Wang X, He Z-G. Uncovering new signaling proteins and potential drug targets through the interactome analysis of Mycobacterium tuberculosis. BMC Genomics 2009;10(1):118
52. Balazsi G, Heath AP, Shi L, Gennaro ML. The temporal response of the mycobacterium tuberculosis gene regulatory network during growth arrest. Mol Syst Biol 2008;4:225
53. Kitano H. Towards a Theory of Biological Robustness. Mol Syst Biol 2007;3:137
54. Chen Y, Zhu J, Lum PY, et al. Variations in DNA elucidate molecular networks that cause disease. Nature 2008;452(7186):429-435
55. Ooi SL, Pan X, Peyser BD, et al. Global synthetic-lethality analysis and yeast functional profiling. Trends Genet 2006;22(1):56-63
56. Whitehurst AW, Bodemann BO, Cardenas J, et al. Synthetic lethal screen identification of chemosensitizer loci in cancer cells. Nature 2007;446(7137):815-819
57. Raman K, Vashisht R, Chandra N. Strategies for efficient disruption of metabolism in Mycobacterium tuberculosis from network analysis. Mol BioSyst; 2009; June 29 [Epub ahead of print]
58. Goemann B, Wingender E, Potapov A. An approach to evaluate the topological significance of motifs and other patterns in regulatory networks. BMC Syst Biol 2009;3(1):53
59. Tamada Y, Araki H, Imoto S, et al. Unraveling dynamic activities of autocrine pathways that control drug-response transcriptome networks. Pac Symp Biocomput 2009;14:251-263
60. Hopkins AL. Network pharmacology: the next paradigm in drug discovery. Nat Chem Biol 2008;4(11):682-690
61. Mount DW. Bioinformatics - sequence and genome analysis: CBS Publishers and Distributors; 2001
62. Available from: http://www.ncbi.nlm.nih. gov
63. Li Q, Lai L. Prediction of potential drug targets based on simple sequence properties. BMC Bioinform 2007;8(1):353
64. Sanseau P. Impact of human genome sequencing for in silico target discovery. Drug Discov Today 2001;6(6):316-323
65. Wishart DS. Discovering drug targets through the web. Comp Biochem Physiol D Genomics Proteomics 2007;2(1):9-17
66. Holm L, Sander C. Protein structure comparison by alignment of distance matrices. J Mol Biol 1993;233(1):123-138
67. Scapin G. Structural biology and drug discovery. Curr Pharm Des 2006;12(17):2087-2097
68. Berman HM, Westbrook J, Feng Z, et al. The protein data bank. Nucleic Acids Res 2000;28(1):235-242
69. Todd AE, Marsden RL, Thornton JM, Orengo CA. Progress of structural genomics initiatives: an analysis of solved target structures. J Mol Biol 2005;348(5):1235-1260
70. Available from: http://www.rcsb.org/pdb/
71. MartÃ--Renom MA, Stuart AC, Fiser As, et al. Comparative protein structure modeling of genes and genomes. Annu Rev Biophys Biomol Struct 2000;29(1):291-325
72. Riccardo MB-L, Alex DH, Michael JES, Lawrence AK. Exploring the extremes of sequence/structure space with ensemble fold recognition in the program Phyre. Proteins Struct Funct Bioinform 2008;70(3):611-625
73. Murzin AG, Brenner SE, Hubbard T, Chothia C. SCOP: a structural classification of proteins database for the investigation of sequences and structures. J Mol Biol 1995;247(4):536-540
74. Orengo CA, Michie AD, Jones S, et al. CATH a hierarchic classification of protein domain structures. Structure 1997;5(8):1093-1109
75. Taylor WR, Orengo CA. Protein structure alignment. J Mol Biol 1989;208:1-22
76. Kalidas Y, Chandra N. PocketDepth: a new depth based algorithm for identification of ligand binding sites in proteins. J Struct Biol 2008;161(1):31-42
77. Yeturu K, Chandra N. PocketMatch: a new algorithm to compare binding sites in protein structures. BMC Bioinform 2008;9(1):543
78. An J, Totrov M, Abagyan R. Pocketome via comprehensive identification and classification of ligand binding envelopes. Mol Cell Proteomics 2005;4(6):752-761
79. Egner U, Hillig RC. A structural biology view of target drugability. Expert Opin Drug Discov 2008;3(4):391-401
80. Xie L, Li J, Xie L, Bourne PE. Drug discovery using chemical systems biology: identification of the protein-ligand binding network to explain the side effects of CETP inhibitors. PLoS Comput Biol 2009;5(5):e1000387
81. Campillos M, Kuhn M, Gavin A-C, et al. Drug target identification using side-effect similarity. Science 2008;321(5886):263-266
82. Forst CV. Network genomics - a novel approach for the analysis of biological systems in the post-genomic era. Mol Biol Rep 2002;29(3):265-280
83. Yamanishi Y, Araki M, Gutteridge A, et al. Prediction of drug-target interaction networks from the integration of chemical and genomic spaces. Bioinformatics 2008;24(13):i232-40
84. Yildirim MA, Goh K-I, Cusick ME, et al. Drug target network. Nat Biotechnol 2007;25(10):1119-1126
85. Heller MJ. DNA microarray technology: devices, systems, and applications. Annu Rev Biomed Eng 2002;4(1):129-153
86. Yamamoto M, Wakatsuki T, Hada A, Ryo A. Use of serial analysis of gene expression (SAGE) technology. J Immunol Methods 2001;250(1-2):45-66
87. Weinberg GA, Erdman DD, Edwards KM, et al. Superiority of reverse transcription polymerase chain reaction to conventional viral culture in the diagnosis of acute respiratory tract infections in children. J Infect Dis 2004;189(4):706-710
88. Bajorath J. Rational drug discovery revisited: interfacing experimental programs with bio- and chemo-informatics. Drug Discov Today 2001;6(19):989-995
89. Stefanie F, Shoudan L, Xiling W, Roland S. Target finding in genomes and proteomes. In: Thomas L, editor, Bioinformatics - From Genomes to Drugs 2004. p. 119-135
90. Sassetti CM, Boyd DH, Rubin EJ. Genes required for mycobacterium growth defined by high density mutagenesis. Mol Microbiol 2003;48:77-84
91. Wright GD. The antibiotic resistome: the nexus of chemical and genetic diversity. Nat Rev Microbiol 2007;5(3):175-186
92. Raman K, Chandra N. Mycobacterium tuberculosis interactome analysis unravels potential pathways to drug resistance. BMC Microbiol 2008;8(1):234
93. Hunter PJ, Borg TK. Integration from proteins to organs: the physiome project. Nat Rev Mol Cell Biol 2003;4(3):237-243
94. Avialable from: http://www.entelos.com/