Targethunter: An in silico target identification tool for predicting therapeutic potential of small organic molecules based on chemogenomic database

AAPS Journal

Volumn 15, Issue 2, 2013, Pages 395-406

  Wang, Lirong ^a,b,c   Ma, Chao ^a,c,d   Wipf, Peter ^b,c   Liu, Haibin ^a,e   Su, Weiwei ^e   Xie, Xiang Qun ^a,b,c,d  

^a UNIVERSITY OF PITTSBURGH   (United States)

^b Center for Chemical Methodologies and Library Development (UPCMLD) Department of Chemistry   (United States)

^c Drug Discovery Institute   (United States)

^d UNIVERSITY OF PITTSBURGH SCHOOL OF MEDICINE   (United States)

^e SUN YAT SEN UNIVERSITY   (China)

Author keywords

Chembl; chemogenomics; machine learning; Target hunter; target identification

Indexed keywords

ACETYLCHOLINESTERASE; BIOLOGICAL PRODUCT; COMPOUND CHEMBL 1711746; COMPOUND CHEMBL 1724922; COMPOUND CID 46907796; CYCLOOXYGENASE 1; CYCLOOXYGENASE 2; DARIFENACIN; DOPAMINE 1 RECEPTOR; EPIDERMAL GROWTH FACTOR RECEPTOR; EPIDERMAL GROWTH FACTOR RECEPTOR ERBB1; HUMAN IMMUNODEFICIENCY VIRUS PROTEINASE; HUMAN IMMUNODEFICIENCY VIRUS TYPE 1 PROTEASE; HUMAN IMMUNODEFICIENCY VIRUS TYPE 1 REVERSE TRANSCRIPTASE; MITOGEN ACTIVATED PROTEIN KINASE 14; NUCLEAR FACTOR ERYTHROID 2 RELATED FACTOR 2 INHIBITOR; PEROXISOME PROLIFERATOR ACTIVATED RECEPTOR GAMMA; POLYTHIAZIDE; RNA DIRECTED DNA POLYMERASE; SMALL ORGANIC MOLECULE; THROMBIN; UK 201844; UNCLASSIFIED DRUG;

ACCURACY; ALGORITHM; ARTICLE; BIOLOGICAL ACTIVITY; CHEMOGENOMICS; COMPUTER MODEL; DATA ANALYSIS SOFTWARE; DATA BASE; DATA MINING; DRUG ACTIVITY; DRUG POTENCY; DRUG STRUCTURE; GENOMICS; LIGAND BINDING; MACHINE LEARNING; MATHEMATICAL ANALYSIS; MOLECULAR DOCKING; ONLINE SYSTEM; PREDICTION; PROTEIN TARGETING;

ALGORITHMS; ANTI-HIV AGENTS; ANTIHYPERTENSIVE AGENTS; ANTINEOPLASTIC AGENTS; ARTIFICIAL INTELLIGENCE; BENZOFURANS; COMPUTER GRAPHICS; COMPUTER SIMULATION; DATA MINING; DATABASES, CHEMICAL; DRUG DISCOVERY; DRUG REPOSITIONING; MODELS, MOLECULAR; MOLECULAR DOCKING SIMULATION; MOLECULAR STRUCTURE; MUSCARINIC ANTAGONISTS; POLYTHIAZIDE; PYRROLIDINES; REPRODUCIBILITY OF RESULTS; SOFTWARE; STRUCTURE-ACTIVITY RELATIONSHIP; USER-COMPUTER INTERFACE;

EID: 84877055652     PISSN: 15507416     EISSN: None     Source Type: Journal    
DOI: 10.1208/s12248-012-9449-z     Document Type: Article

References (55)

1. Nettles JH, Jenkins JL, Bender A, Deng Z, Davies JW, Glick M. Bridging chemical and biological space: "target fishing" using 2D and 3D molecular descriptors. J Med Chem. 2006;49(23):6802-10. doi: 10.1021/jm060902w.
2. Lipinski CA, Lombardo F, Dominy BW, Feeney PJ. Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv Drug Deliv Rev. 2001;46(1-3):3-26.
3. Wang Y, Xiao J, Suzek TO, Zhang J, Wang J, Bryant SH. PubChem: A public information system for analyzing bioactivities of small molecules. Nucleic Acids Res. 2009;37 suppl 2:W623-33. doi: 10.1093/nar/gkp456.
4. Bolton EE, Wang Y, Thiessen PA, Bryant SH. PubChem: Integrated platform of small molecules and biological activities. Annu Rep Comput Chem. 2008;4:217-41. doi: 10.1016/S1574-1400(08)00012-1.
5. Xie X-QS. Exploiting PubChem for virtual screening. Expert Opin Drug Discov. 2010;5(12):1205-20. doi: 10.1517/17460441.2010.524924.
6. Gaulton A, Bellis LJ, Bento AP, Chambers J, Davies M, Hersey A, et al. ChEMBL: A large-scale bioactivity database for drug discovery. Nucleic Acids Res. 2012;40(1):D1100-7. doi: 10.1093/nar/gkr777.
7. Nidhi, Glick M, Davies JW, Jenkins JL. Prediction of biological targets for compounds using multiple-category Bayesian models trained on chemogenomics databases. J Chem Inf Model. 2006;46(3):1124-33. doi: 10.1021/ci060003g.
8. Jenkins JL, Bender A, Davies JW. In silico target fishing: predicting biological targets from chemical structure. Drug Discov Today Technol. 2007;3(4):413-21. doi: 10.1016/j.ddtec.2006.12.008.
9. Abraham VC, Taylor DL, Haskins JR. High content screening applied to large-scale cell biology. Trends Biotechnol. 2004;22(1):15-22.
10. Bleicher KH, Bohm HJ, Muller K, Alanine AI. Hit and lead generation: Beyond high-throughput screening. Nat Rev Drug Discov. 2003;2(5):369-78. doi: 10.1038/nrd1086.
11. Carpenter AE, Sabatini DM. Systematic genome-wide screens of gene function. Nat Rev Genet. 2004;5(1):11-22. doi: 10.1038/nrg1248.
12. Oprea TI, Bauman JE, Bologa CG, Buranda T, Chigaev A, Edwards BS, et al. Drug repurposing from an academic perspective. Drug Discov Today Ther Strateg. 2011. doi: 10.1016/j.ddstr.2011.10.002.
13. Rognan D. Structureâ based approaches to target fishing and ligand profiling. Mol Inf. 2010;29(3):176-87. doi: 10.1002/minf.200900081.
14. Rognan D, editors. Computational approaches to target fishing and ligand profiling. Theory and applications in computational chemistry: The first decade of the second millennium: International Congress TACC-2012; 2012.
15. Bender A, Young DW, Jenkins JL, Serrano M, Mikhailov D, Clemons PA, et al. Chemogenomic data analysis: prediction of small-molecule targets and the advent of biological fingerprint. Comb Chem High Throughput Screen. 2007;10(8):719-31.
16. Martin YC, Kofron JL, Traphagen LM. Do structurally similar molecules have similar biological activity? J Med Chem. 2002;45(19):4350-8. doi: 10.1021/jm020155c.
17. Schuffenhauer A, Floersheim P, Acklin P, Jacoby E. Similarity metrics for ligands reflecting the similarity of the target proteins. J Chem Inf Comput Sci. 2003;43(2):391-405. doi: 10.1021/ci025569t.
18. Mitchell JBO. The relationship between the sequence identities of alpha helical proteins in the PDB and the molecular similarities of their ligands. J Chem Inf Comput Sci. 2001;41(6):1617-22. doi: 10.1021/ci010364q.
19. Brown RD, Martin YC. The information content of 2D and 3D structural descriptors relevant to ligand-receptor binding. J Chem Inf Comput Sci. 1997;37(1):1-9. doi: 10.1021/ci960373c.
20. Johnson M. Concepts and applications of molecular similarity. J Med Chem. 1991;34(12):3409. doi: 10.1021/jm00116a601.
21. Bender A, Glen RC. Molecular similarity: A key technique in molecular informatics. Org Biomol Chem. 2004;2(22):3204-18. doi: 10.1039/B409813G.
22. Patterson DE, Cramer RD, Ferguson AM, Clark RD, Weinberger LE. Neighborhood behavior: A useful concept for validation of "molecular diversity" descriptors. J Med Chem. 1996;39(16):3049-59. doi: 10.1021/jm960290n.
23. Tanimoto TT. IBM Internal Report. November 17, 1957.
24. Gregori-Puigjané E, Mestres J. SHED: Shannon entropy descriptors from topological feature distributions. J Chem Inf Model. 2006;46(4):1615-22. doi: 10.1021/ci0600509.
25. Keiser MJ, Setola V, Irwin JJ, Laggner C, Abbas AI, Hufeisen SJ, et al. Predicting new molecular targets for known drugs. Nature. 2009;462(7270):175-81. doi: 10.1038/nature08506.
26. Lounkine E, Keiser MJ, Whitebread S, Mikhailov D, Hamon J, Jenkins JL, et al. Large-scale prediction and testing of drug activity on side-effect targets. Nature. 2012;486(7403):361-7. doi: doi:10.1038/nature11159.
27. Olah M, Mracec M, Ostopovici L, Rad R, Bora A, Hadaruga N, et al. WOMBAT: World of molecular bioactivity. Chemoinformatics in Drug Discov. 2005. doi: 10.1002/3527603743.ch9.
28. MDDR. 2012. http://accelrys.com/products/databases/bioactivity/mddr.html. Accessed Oct 12 2012.
29. Bender A, Scheiber J, Glick M, Davies JW, Azzaoui K, Hamon J, et al. Analysis of pharmacology data and the prediction of adverse drug reactions and offâ target effects from chemical structure. ChemMedChem. 2007;2(6):861-73. doi: 10.1002/cmdc.200700026.
30. Li H, Gao Z, Kang L, Zhang H, Yang K, Yu K, et al. TarFisDock: A web server for identifying drug targets with docking approach. Nucleic Acids Res. 2006;34(Web Server issue):W219-24. doi: 10.1093/nar/gkl114.
31. Cai J, Han C, Hu T, Zhang J, Wu D, Wang F, et al. Peptide deformylase is a potential target for antiâHelicobacter pylori drugs: Reverse docking, enzymatic assay, and Xâray crystallography validation. Protein Sci. 2006;15(9):2071-81. doi: 10.1110/ps.062238406.
32. Chen YZ, Zhi DG. Ligand-protein inverse docking and its potential use in the computer search of protein targets of a small molecule. Proteins. 2001;43(2):217-26. doi:10.1002/1097-0134(20010501)43:2<217::AID- PROT1032>3.0.CO;2-G.
33. Zhu W, Qiu XH, Xu XJ, Lu CJ. Computational network pharmacological research of Chinese medicinal plants for chronic kidney disease. SCIENCE CHINA Chem. 2010;53(11):2337-42. doi: 10.1007/s11426-010-4082-0.
34. Krejsa CM, Horvath D, Rogalski SL, Penzotti JE, Mao B, Barbosa F, et al. Predicting ADME properties and side effects: The BioPrint approach. Curr Opin Drug Discov Dev. 2003;6(4):470-80.
35. Fliri AF, Loging WT, Thadeio PF, Volkmann RA. Biological spectra analysis: Linking biological activity profiles to molecular structure. Proc Natl Acad Sci U S A. 2005;102(2):261-6. doi: 10.1073/pnas.0407790101.
36. Cheng T, Li Q, Wang Y, Bryant SH. Identifying compound-target associations by combining bioactivity profile similarity search and public databases mining. J Chem Inf Model. 2011;51(9):2440-8. doi: 10.1021/ci200192v.
37. Bender A. Databases: Compound bioactivities go public. Nat Chem Biol. 2010;6(5):309. doi: 10.1038/nchembio.354.
38. Heikamp K, Bajorath J. Large-scale similarity search profiling of ChEMBL compound data sets. J Chem Inf Model. 2011;51(8):1831-9. doi: 10.1021/ci200199u.
39. Rogers D, Hahn M. Extended-connectivity fingerprints. J Chem Inf Model. 2010;50(5):742-54. doi: 10.1021/ci100050t.
40. O'Boyle NM, Banck M, James CA, Morley C, Vandermeersch T, Hutchison GR. Open Babel: An open chemical toolbox. J Cheminform. 2011;3:33. doi: 10.1186/1758-2946-3-33.
41. Tan KP, Yang M, Ito S. Activation of nuclear factor (erythroid-2 like) factor 2 by toxic bile acids provokes adaptive defense responses to enhance cell survival at the emergence of oxidative stress. Mol Pharmacol. 2007;72(5):1380. doi: 10.1124/mol.107.039370.
42. Bozkurt TE, Sahin-Erdemli I. M1 and M3 muscarinic receptors are involved in the release of urinary bladder-derived relaxant factor. Pharmacol Res. 2009;59(5):300-5. doi: 10.1016/j.phrs.2009.01.013.
43. Blair WS, Cao J, Jackson L, Jimenez J, Peng Q, Wu H, et al. Identification and characterization of UK-201844, a novel inhibitor that interferes with human immunodeficiency virus type 1 gp160 processing. Antimicrob Agents Chemother. 2007;51(10):3554-61. doi: 10.1128/AAC.00643-07.
44. Sjoblom T, Jones S, Wood LD, Parsons DW, Lin J, Barber TD, et al. The consensus coding sequences of human breast and colorectal cancers. Science. 2006;314(5797):268-74. doi: 10.1126/science.1133427.
45. Shibata T, Kokubu A, Gotoh M, Ojima H, Ohta T, Yamamoto M, et al. Genetic alteration of Keap1 confers constitutive Nrf2 activation and resistance to chemotherapy in gallbladder cancer. Gastroenterology. 2008. doi: 10.1053/j.gastro.2008.06.082.
46. Gregori-Puigjané E, Setola V, Hert J, Crews BA, Irwin JJ, Lounkine E, et al. Identifying mechanism-of-action targets for drugs and probes. Proc Natl Acad Sci U S A. 2012;109(28):11178-83. doi: 10.1073/pnas.1204524109.
47. Gregori-Puigjané E, Keiser MJ. Chemoinformatic approaches to target identification. In: Harris CJ, Morphy JR, editors. Designing multi-target drugs. London: Royal Society of Chemistry; 2012.
48. Brummond KM, Goodell J, LaPorte M, Wang L, Xie X-Q. Synthesis and in silico screening of a library of carboline-containing compounds. Beilstein J Org Chem. 2012;8:1048-58. doi: 10.3762/bjoc.8.117.
49. Sanseau P, Agarwal P, Barnes MR, Pastinen T, Richards JB, Cardon LR, et al. Use of genome-wide association studies for drug repositioning. Nat Biotechnol. 2012;30(4):317-20. doi: 10.1038/nbt.2151.
50. Hu G, Agarwal P. Human disease-drug network based on genomic expression profiles. PLoS One. 2009;4(8):e6536. doi: 10.1371/journal.pone.0006536.
51. Wang L, Ma C, Xie X-Q. Linear and non-linear support vector machine for the classification of human 5-HT1A ligand functionality. Mol Inf. 2012;31(1):85-95. doi: 10.1002/minf.201100126.
52. Geppert H, Humrich J, Stumpfe D, Gärtner T, Bajorath J. Ligand prediction from protein sequence and small molecule information using support vector machines and fingerprint descriptors. J Chem Inf Model. 2009;49(4):767-79. doi: 10.1021/ci900004a.
53. Ma C, Wang L, Xie XQ. GPU accelerated chemical similarity calculation for compound library comparison. J Chem Inf Model. 2011;51(7):1521-7. doi: 10.1021/ci1004948.
54. Yang L, Agarwal P. Systematic drug repositioning based on clinical side-effects. PLoS One. 2011;6(12):e28025. doi: 10.1371/journal.pone.0028025.
55. Yao L, Zhang Y, Li Y, Sanseau P, Agarwal P. Electronic health records: Implications for drug discovery. Drug Discov Today. 2011;16(13-14):594-9. doi: 10.1016/j.drudis.2011.05.009.