A systematic prediction of multiple drug-target interactions from chemical, genomic, and pharmacological data

PLoS ONE

Volumn 7, Issue 5, 2012, Pages

  Yu, Hua ^a   Chen, Jianxin ^b   Xu, Xue ^a   Li, Yan ^c   Zhao, Huihui ^b   Fang, Yupeng ^a   Li, Xiuxiu ^a   Zhou, Wei ^a   Wang, Wei ^b   Wang, Yonghua ^a  

^a NORTHWEST A F UNIVERSITY   (China)

^b BEIJING UNIVERSITY OF CHINESE MEDICINE   (China)

^c DALIAN UNIVERSITY OF TECHNOLOGY   (China)

Author keywords

[No Author keywords available]

Indexed keywords

3,4 METHYLENEDIOXYMETHAMPHETAMINE; BROMFENAC; CELL NUCLEUS RECEPTOR; DIETHYLSTILBESTROL; G PROTEIN COUPLED RECEPTOR; IDOXURIDINE; INDOMETACIN; ION CHANNEL; OXYPHENBUTAZONE; PENTOSTATIN; RESVERATROL; TRIFLURIDINE; VALACICLOVIR; VIDARABINE; DRUG;

ARTICLE; DRUG TARGETING; GENOMICS; PREDICTION; RANDOM FOREST; RELIABILITY; SENSITIVITY AND SPECIFICITY; SIMULATION; SUPPORT VECTOR MACHINE; VALIDATION PROCESS; ARTIFICIAL NEURAL NETWORK; CHEMISTRY; DISCRIMINANT ANALYSIS; FACTUAL DATABASE; METABOLISM; MOLECULARLY TARGETED THERAPY; PHARMACOLOGY; PROTEIN BINDING; REGRESSION ANALYSIS; STATISTICAL MODEL;

DATABASES, FACTUAL; DISCRIMINANT ANALYSIS; GENOMICS; LEAST-SQUARES ANALYSIS; LINEAR MODELS; MOLECULAR TARGETED THERAPY; NEURAL NETWORKS (COMPUTER); PHARMACEUTICAL PREPARATIONS; PHARMACOLOGICAL PROCESSES; PROTEIN BINDING;

EID: 84861610134     PISSN: None     EISSN: 19326203     Source Type: Journal    
DOI: 10.1371/journal.pone.0037608     Document Type: Article

References (73)

1. Paul SM, Mytelka DS, Dunwiddie CT, Persinger CC, Munos BH, et al. (2010) How to improve R&D productivity: the pharmaceutical industry's grand challenge. Nature Reviews Drug Discovery 9: 203-214.
2. Haggarty SJ, Koeller KM, Wong JC, Butcher RA, Schreiber SL, (2003) Multidimensional chemical genetic analysis of diversity-oriented synthesis-derived deacetylase inhibitors using cell-based assays. Chemistry & Biology 10: 383-396.
3. Kuruvilla FG, Shamji AF, Sternson SM, Hergenrother PJ, Schreiber SL, (2002) Dissecting glucose signalling with diversity-oriented synthesis and small-molecule microarrays. Nature 416: 653-657.
4. Dobson CM, (2004) Chemical space and biology. Nature 432: 824-828.
5. Lipinski C, Hopkins A, (2004) Navigating chemical space for biology and medicine. Nature 432: 855-861.
6. MacDonald ML, Lamerdin J, Owens S, Keon BH, Bilter GK, et al. (2006) Identifying off-target effects and hidden phenotypes of drugs in human cells. Nature Chemical Biology 2: 329-337.
7. Paolini GV, Shapland RHB, van Hoorn WP, Mason JS, Hopkins AL, (2006) Global mapping of pharmacological space. Nature Biotechnology 24: 805-815.
8. Krejsa CM, Horvath D, Rogalski SL, Penzotti JE, Mao B, et al. (2003) Predicting ADME properties and side effects: the BioPrint approach. Current Opinion in Drug Discovery & Development 6: 470-480.
9. Byvatov E, Fechner U, Sadowski J, Schneider G, (2003) Comparison of support vector machine and artificial neural network systems for drug/nondrug classification. Journal of Chemical Information and Computer Sciences 43: 1882-1889.
10. Ballesteros J, Palczewski K, (2001) G protein-coupled receptor drug discovery: implications from the crystal structure of rhodopsin. Current Opinion in Drug Discovery & Development 4: 561-574.
11. Zhu S, Okuno Y, Tsujimoto G, Mamitsuka H, (2005) A probabilistic model for mining implicit 'chemical compound-gene' relations from literature. Bioinformatics 21: ii245-ii251.
12. Jacoby E, Schuffenhauer A, Floersheim P, (2003) Chemogenomics knowledge-based strategies in drug discovery. Drug News Perspect 16: 93-102.
13. Jacob L, Vert JP, (2008) Protein-ligand interaction prediction: an improved chemogenomics approach. Bioinformatics 24: 2149-2156.
14. Balakin KV, Tkachenko SE, Lang SA, Okun I, Ivashchenko AA, et al. (2002) Property-based design of GPCR-targeted library. Journal of Chemical Information and Computer Sciences 42: 1332-1342.
15. Klabunde T, (2006) Chemogenomics approaches to ligand design. Ligand Design for G Protein-coupled Receptors ch7: 115-135.
16. Frimurer TM, Ulven T, Elling CE, Gerlach LO, Kostenis E, et al. (2005) A physicogenetic method to assign ligand-binding relationships between 7TM receptors. Bioorganic & Medicinal Chemistry Letters 15: 3707-3712.
17. Nagamine N, Sakakibara Y, (2007) Statistical prediction of protein chemical interactions based on chemical structure and mass spectrometry data. Bioinformatics 23: 2004-2012.
18. He Z, Zhang J, Shi XH, Hu LL, Kong X, et al. (2010) Predicting drug-target interaction networks based on functional groups and biological features. PloS one 5: e9603.
19. Xia Z, Zhou X, Sun Y, Wu L, (2009) Semi-supervised Drug-Protein Interaction Prediction from Heterogeneous Spaces. The Third International Symposium on Optimization and Systems Biology 11: 123-131.
20. Yamanishi Y, Pauwels E, Saigo H, Stoven V, (2011) Extracting sets of chemical substructures and protein domains governing drug-target interactions. Journal of Chemical Information and Modeling 51: 1183-1194.
21. Breiman L, (2001) Random forests. Machine learning 45: 5-32.
22. Overington JP, Al-Lazikani B, Hopkins AL, (2006) How many drug targets are there? Nature Reviews Drug Discovery 5: 993-996.
23. Imming P, Sinning C, Meyer A, (2006) Drugs, their targets and the nature and number of drug targets. Nature Reviews Drug Discovery 5: 821-834.
24. Li Z, Lin H, Han L, Jiang L, Chen X, et al. (2006) PROFEAT: a web server for computing structural and physicochemical features of proteins and peptides from amino acid sequence. Nucleic Acids Research 34: W32-W37.
25. Hao M, Li Y, Wang Y, Zhang S, (2011) A Classification Study of Respiratory Syncytial Virus (RSV) Inhibitors by Variable Selection with Random Forest. International Journal Molecular Sciences 12: 1259-1280.
26. Hao M, Li Y, Wang Y, Zhang S, (2010) Prediction of PKCθ inhibitory activity using the random forest algorithm. International Journal Molecular Sciences 11: 3413-3433.
27. Li Y, Wang Y, Ding J, Chang Y, Zhang S, (2009) In silico prediction of androgenic and nonandrogenic compounds using random forest. QSAR & Combinatorial Science 28: 396-405.
28. Svetnik V, Liaw A, Tong C, Culberson JC, Sheridan RP, et al. (2003) Random forest: a classification and regression tool for compound classification and QSAR modeling. Journal of Chemical Information and Computer Sciences 43: 1947-1958.
29. Zhao CY, Zhang HX, Zhang XY, Liu MC, Hu ZD, et al. (2006) Application of support vector machine (SVM) for prediction toxic activity of different data sets. Toxicology 217: 105-119.
30. Sun X, Li Y, Liu X, Ding J, Wang Y, et al. (2008) Classification of bioaccumulative and non-bioaccumulative chemicals using statistical learning approaches. Molecular Diversity 12: 157-169.
31. Burbidge R, Trotter M, Buxton B, Holden S, (2001) Drug design by machine learning: support vector machines for pharmaceutical data analysis. Computers & Chemistry 26: 5-14.
32. Vapnik VN, (2000) The nature of statistical learning theory Berlin Springer Verlag.
33. Platt JC, (1998) Sequential minimal optimization: A fast algorithm for training support vector machines. Advances in Kernel MethodsSupport Vector Learning 208: 1-21.
34. Doucet JP, Barbault F, Xia H, Panaye A, Fan B, (2007) Nonlinear SVM approaches to QSPR/QSAR studies and drug design. Current Computer-Aided Drug Design 3: 263-289.
35. Baldi P, Brunak S, Chauvin Y, Andersen CAF, Nielsen H, (2000) Assessing the accuracy of prediction algorithms for classification: an overview. Bioinformatics 16: 412-424.
36. Erhan D, L'Heureux PJ, Yue SY, Bengio Y, (2006) Collaborative filtering on a family of biological targets. Journal of chemical information and modeling 46: 626-635.
37. Bock JR, Gough DA, (2005) Virtual screen for ligands of orphan G protein-coupled receptors. Journal of Chemical Information and Modeling 45: 1402-1414.
38. Yamanishi Y, Araki M, Gutteridge A, Honda W, Kanehisa M, (2008) Prediction of drug-target interaction networks from the integration of chemical and genomic spaces. Bioinformatics 24: i232-i240.
39. Yamanishi Y, Kotera M, Kanehisa M, Goto S, (2010) Drug-target interaction prediction from chemical, genomic and pharmacological data in an integrated framework. Bioinformatics 26: i246-i254.
40. Wold S, Esbensen K, Geladi P, (1987) Principal component analysis. Chemometrics and Intelligent Laboratory Systems 2: 37-52.
41. David Waterbury L, Silliman D, Jolas T, (2006) Comparison of cyclooxygenase inhibitory activity and ocular anti-inflammatory effects of ketorolac tromethamine and bromfenac sodium. Current Medical Research and Opinion 22: 1133-1140.
42. Ackerstaff E, Gimi B, Artemov D, Bhujwalla ZM, (2007) Anti-inflammatory agent indomethacin reduces invasion and alters metabolism in a human breast cancer cell line. Neoplasia 9: 222-235.
43. Vane J, Botting R, (1998) Anti-inflammatory drugs and their mechanism of action. Inflammation Research 47: 78-87.
44. Dong J, Horvath S, (2007) Understanding network concepts in modules. BMC Systems Biology 1: 24.
45. Li YY, An J, Jones SJM, (2011) A Computational Approach to Finding Novel Targets for Existing Drugs. PLoS Computational Biology 7: e1002139.
46. Mithoefer MC, Wagner MT, Mithoefer AT, Jerome L, Doblin R, (2011) The safety and efficacy of ±3, 4-methylenedioxymethamphetamine-assisted psychotherapy in subjects with chronic, treatment-resistant posttraumatic stress disorder: the first randomized controlled pilot study. Journal of Psychopharmacology 25: 439-452.
47. Jang M, Cai L, Udeani GO, Slowing KV, Thomas CF, et al. (1997) Cancer chemopreventive activity of resveratrol, a natural product derived from grapes. Science 275: 218-220.
48. Donnelly LE, Newton R, Kennedy GE, Fenwick PS, Leung RHF, et al. (2004) Anti-inflammatory effects of resveratrol in lung epithelial cells: molecular mechanisms. American Journal of Physiology-Lung Cellular and Molecular Physiology 287: L774-L783.
49. Lyon RA, Glennon RA, Titeler M, (1986) 3, 4-Methylenedioxymethamphetamine (MDMA): stereoselective interactions at brain 5-HT 1 and 5-HT 2 receptors. Psychopharmacology 88: 525-526.
50. Biezonski DK, Meyer JS, (2010) Effects of 3, 4-methylenedioxymethamphetamine (MDMA) on serotonin transporter and vesicular monoamine transporter 2 protein and gene expression in rats: implications for MDMA neurotoxicity. Journal of Neurochemistry 112: 951-962.
51. Shankaran M, Yamamoto BK, Gudelsky GA, (1999) Involvement of the serotonin transporter in the formation of hydroxyl radicals induced by 3, 4-methylenedioxymethamphetamine. European Journal of Pharmacology 385: 103-110.
52. Hansen JP, Riddle EL, Sandoval V, Brown JM, Gibb JW, et al. (2002) Methylenedioxymethamphetamine decreases plasmalemmal and vesicular dopamine transport: mechanisms and implications for neurotoxicity. Journal of Pharmacology and Experimental Therapeutics 300: 1093-1100.
53. Fleckenstein AE, Volz TJ, Riddle EL, Gibb JW, Hanson GR, (2007) New insights into the mechanism of action of amphetamines. Annual Review of Pharmacology and Toxicology 47: 681-698.
54. Sprague JE, Banks ML, Cook VJ, Mills EM, (2003) Hypothalamic-pituitary-thyroid axis and sympathetic nervous system involvement in hyperthermia induced by 3, 4-methylenedioxymethamphetamine (Ecstasy). Journal of Pharmacology and Experimental Therapeutics 305: 159-166.
55. Selken J, Nichols DE, (2007) [alpha] 1-Adrenergic receptors mediate the locomotor response to systemic administration of (±)-3, 4-methylenedioxymethamphetamine (MDMA) in rats. Pharmacology Biochemistry and Behavior 86: 622-630.
56. Buryanovskyy L, Fu Y, Boyd M, Ma Y, Hsieh T, et al. (2004) Crystal structure of quinone reductase 2 in complex with resveratrol. Biochemistry 43: 11417-11426.
57. Chen X, Ji Z, Chen YZ, (2002) TTD: therapeutic target database. Nucleic Acids Research 30: 412-415.
58. Nagamine M, Suzutani T, Saijo M, Hayashi K, Azuma M, (2000) Comparison of polymorphism of thymidine kinase gene and restriction fragment length polymorphism of genomic DNA in herpes simplex virus type 1. Journal of Clinical Microbiology 38: 2750-2752.
59. Datta S, (2003) Comparisons and validation of statistical clustering techniques for microarray gene expression data. Bioinformatics 19: 459-466.
60. Cecil C, Bridges J, (1966) Hierarchical cluster analysis. Psychological Reports 18: 851-854.
61. Wild K, Bohner T, Folkers G, Schulz GE, (1997) The structures of thymidine kinase from herpes simplex virus type 1 in complex with substrates and a substrate analogue. Protein Science 6: 2097-2106.
62. Kaufman H, (1985) Update on antiviral agents. Ophthalmology 92: 533-536.
63. Satoh T, Teh BS, Timme TL, Mai WY, Gdor Y, et al. (2004) Enhanced systemic T-cell activation after in situ gene therapy with radiotherapy in prostate cancer patients. International Journal of Radiation Oncology* Biology* Physics 59: 562-571.
64. Suzuki M, Okuda T, Shiraki K, (2006) Synergistic antiviral activity of acyclovir and vidarabine against herpes simplex virus types 1 and 2 and varicella-zoster virus. Antiviral Research 72: 157-161.
65. Roth BL, Sheffler DJ, Kroeze WK, (2004) Magic shotguns versus magic bullets: selectively non-selective drugs for mood disorders and schizophrenia. Nature Reviews Drug Discovery 3: 353-359.
66. Frantz S, (2005) Drug discovery: playing dirty. Nature 437: 942-943.
67. Jeong H, Mason SP, Barabasi AL, Oltvai ZN, (2001) Lethality and centrality in protein networks. Nature 441: 41-42.
68. Schmitt BM, (2004) The concept of "buffering" in systems and control theory: From metaphor to math. ChemBioChem 5: 1384-1392.
69. Yabuuchi H, Niijima S, Takematsu H, Ida T, Hirokawa T, et al. (2011) Analysis of multiple compound-protein interactions reveals novel bioactive molecules. Molecular systems biology 7: 472.
70. Karchin R, Karplus K, Haussler D, (2002) Classifying G-protein coupled receptors with support vector machines. Bioinformatics 18: 147-159.
71. Bock JR, Gough DA, (2001) Predicting protein-protein interactions from primary structure. Bioinformatics 17: 455-460.
72. Chou KC, (2000) Prediction of protein subcellular locations by incorporating quasi-sequence-order effect. Biochemical and Biophysical Research Communications 278: 477-483.
73. Han L, Cai C, Ji Z, Cao Z, Cui J, et al. (2004) Predicting functional family of novel enzymes irrespective of sequence similarity: a statistical learning approach. Nucleic acids research 32: 6437-6444.