Historeceptomic fingerprints for drug-like compounds

Frontiers in Physiology

Volumn 6, Issue DEC, 2015, Pages

  Shmelkov, Evgeny ^a   Grigoryan, Arsen ^a   Swetnam, James ^b   Xin, Junyang ^a   Tivon, Doreen ^c   Shmelkov, Sergey V ^a   Cardozo, Timothy ^a  

^a NEW YORK UNIVERSITY SCHOOL OF MEDICINE   (United States)

^b GOOGLE INC   (United States)

^c GeneCentrix Inc   (United States)

Author keywords

Drug target; Gene expression; Mechanism of drug action; Molecular docking simulation; Polypharmacology

Indexed keywords

CHEMICAL COMPOUND; MEMBRANE RECEPTOR;

ANALYTIC METHOD; ARTICLE; BIOLOGICAL ACTIVITY; CHEMICAL ANALYSIS; COMPUTER MODEL; CONCENTRATION (PARAMETERS); DATA ANALYSIS; GENE EXPRESSION; HISTORECEPTOMIC FINGERPRINT; IN VIVO STUDY; MOLECULAR DOCKING; MOLECULAR DYNAMICS; PHENOTYPE; PROTEIN DATABASE; SCORING SYSTEM; STRUCTURE ANALYSIS;

EID: 84953218845     PISSN: None     EISSN: 1664042X     Source Type: Journal    
DOI: 10.3389/fphys.2015.00371     Document Type: Article

References (40)

1. Abagyan, R., and Kufareva, I. (2009). The flexible pocketome engine for structural chemogenomics. Methods Mol. Biol. 575, 249-279. doi: 10.1007/978-1-60761-274-2_11
2. Allen, J. A., and Roth, B. L. (2011). Strategies to discover unexpected targets for drugs active at G protein-coupled receptors. Annu. Rev. Pharmacol. Toxicol. 51, 117-144. doi: 10.1146/annurev-pharmtox-010510-100553
3. Arvanov, V. L., Liang, X. F., Russo, A., and Wang, R. Y. (1999). LSD and DOB: interaction with 5-HT2A receptors to inhibit NMDA receptor-mediated transmission in the rat prefrontal cortex. Eur. J. Neurosci. 11, 3064-3072. doi: 10.1046/j.1460-9568.1999.00726.x
4. Ashburn, T. T., and Thor, K. B. (2004). Drug repositioning: identifying and developing new uses for existing drugs. Nat. Rev. Drug Discov. 3, 673-683. doi: 10.1038/nrd1468
5. Bolton, E. E., Chen, J., Kim, S., Han, L., He, S., Shi, W., et al. (2011). PubChem3D: a new resource for scientists. J. Cheminform. 3:32. doi: 10.1186/1758-2946-3-32
6. Brown, J. B., and Okuno, Y. (2012). Systems biology and systems chemistry: new directions for drug discovery. Chem. Biol. 19, 23-28. doi: 10.1016/j.chembiol.2011.12.012
7. Cereto-Massagué, A., Ojeda, M. J., Valls, C., Mulero, M., Pujadas, G., and Garcia-Vallve, S. (2015). Tools for in silico target fishing. Methods 71, 98-103. doi: 10.1016/j.ymeth.2014.09.006
8. Chen, Y. Z., and Zhi, D. G. (2001). Ligand-protein inverse docking and its potential use in the computer search of protein targets of a small molecule. Proteins 43, 217-226. doi: 10.1002/1097-0134(20010501)43:2<217::AID-PROT1032>3.0.CO;2-G
9. Durrant, J. D., Amaro, R. E., Xie, L., Urbaniak, M. D., Ferguson, M. A., Haapalainen, A., et al. (2010). A multidimensional strategy to detect polypharmacological targets in the absence of structural and sequence homology. PLoS Comput. Biol. 6:e1000648. doi: 10.1371/journal.pcbi.1000648
10. Gao, Z., Li, H., Zhang, H., Liu, X., Kang, L., Luo, X., et al. (2008). PDTD: a web-accessible protein database for drug target identification. BMC bioinformatics 9:104. doi: 10.1186/1471-2105-9-104
11. Gaulton, A., Bellis, L. J., Bento, A. P., Chambers, J., Davies, M., Hersey, A., et al. (2012). ChEMBL: a large-scale bioactivity database for drug discovery. Nucleic Acids Res. 40, D1100-D1107. doi: 10.1093/nar/gkr777
12. Grinter, S. Z., Liang, Y., Huang, S. Y., Hyder, S. M., and Zou, X. (2011). An inverse docking approach for identifying new potential anti-cancer targets. J. Mol. Graph. Model. 29, 795-799. doi: 10.1016/j.jmgm.2011.01.002
13. Husby, J., Bottegoni, G., Kufareva, I., Abagyan, R., and Cavalli, A. (2015). Structure-based predictions of activity cliffs. J. Chem. Inf. Model. 55, 1062-1076. doi: 10.1021/ci500742b
14. Keiser, M. J., Roth, B. L., Armbruster, B. N., Ernsberger, P., Irwin, J. J., and Shoichet, B. K. (2007). Relating protein pharmacology by ligand chemistry. Nat. Biotechnol. 25, 197-206. doi: 10.1038/nbt1284
15. Keiser, M. J., Setola, V., Irwin, J. J., Laggner, C., Abbas, A. I., Hufeisen, S. J., et al. (2009). Predicting new molecular targets for known drugs. Nature 462, 175-181. doi: 10.1038/nature08506
16. Knight, Z. A., Lin, H., and Shokat, K. M. (2010). Targeting the cancer kinome through polypharmacology. Nat. Rev. Cancer 10, 130-137. doi: 10.1038/nrc2787
17. Kufareva, I., Ilatovskiy, A. V., and Abagyan, R. (2012). Pocketome: an encyclopedia of small-molecule binding sites in 4D. Nucleic Acids Res. 40, D535-D540. doi: 10.1093/nar/gkr825
18. Landrum, G. (2008). RDKit: Open-source Cheminformatics. Available online at: http://www.rdkit.org
19. Li, L., Bum-Erdene, K., Baenziger, P. H., Rosen, J. J., Hemmert, J. R., Nellis, J. A., et al. (2010a). BioDrugScreen: a computational drug design resource for ranking molecules docked to the human proteome. Nucleic Acids Res. 38, D765-D773. doi: 10.1093/nar/gkp852
20. Li, L., Li, J., Khanna, M., Jo, I., Baird, J. P., and Meroueh, S. O. (2010b). Docking small molecules to predicted off-targets of the cancer drug erlotinib leads to inhibitors of lung cancer cell proliferation with suitable in vitro pharmacokinetic properties. ACS Med. Chem. Lett. 1, 229-233. doi: 10.1021/ml100031a
21. Mestres, J., Gregori-Puigjané, E., Valverde, S., and Solé, R. V. (2009). The topology of drug-target interaction networks: implicit dependence on drug properties and target families. Mol. Biosyst. 5, 1051-1057. doi: 10.1039/b905821b
22. Metz, J. T., and Hajduk, P. J. (2010). Rational approaches to targeted polypharmacology: creating and navigating protein-ligand interaction networks. Curr. Opin. Chem. Biol. 14, 498-504. doi: 10.1016/j.cbpa.2010.06.166
23. Muschamp, J. W., Regina, M. J., Hull, E. M., Winter, J. C., and Rabin, R. A. (2004). Lysergic acid diethylamide and [-]-2,5-dimethoxy-4-methylamphetamine increase extracellular glutamate in rat prefrontal cortex. Brain Res. 1023, 134-140. doi: 10.1016/j.brainres.2004.07.044
24. Nichols, D. E. (2004). Hallucinogens. Pharmacol. Ther. 101, 131-181. doi: 10.1016/j.pharmthera.2003.11.002
25. Paul, N., Kellenberger, E., Bret, G., Müller, P., and Rognan, D. (2004). Recovering the true targets of specific ligands by virtual screening of the protein data bank. Proteins 54, 671-680. doi: 10.1002/prot.10625
26. Peters, J. U. (2011). Polypharmacology in Drug Discovery. Hoboken, NJ: John Wiley & Sons, Inc.
27. Pieper, U., Eswar, N., Davis, F. P., Braberg, H., Madhusudhan, M. S., Rossi, A., et al. (2006). MODBASE: a database of annotated comparative protein structure models and associated resources. Nucleic Acids Res. 34, D291-D295. doi: 10.1093/nar/gkj059
28. Reardon, S. (2013). Project ranks billions of drug interactions. Nature 503, 449-450. doi: 10.1038/503449a
29. Rognan, D. (2013). Proteome-scale docking: myth and reality. Drug Discov. Today Technol. 10, e403-e409. doi: 10.1016/j.ddtec.2013.01.003
30. Rueda, M., Bottegoni, G., and Abagyan, R. (2010). Recipes for the selection of experimental protein conformations for virtual screening. J. Chem. Inf. Model. 50, 186-193. doi: 10.1021/ci9003943
31. Sharman, J. L., Mpamhanga, C. P., Spedding, M., Germain, P., Staels, B., Dacquet, C., et al. (2011). IUPHAR-DB: new receptors and tools for easy searching and visualization of pharmacological data. Nucleic Acids Res. 39, D534-D538. doi: 10.1093/nar/gkq1062
32. Su, A. I., Wiltshire, T., Batalov, S., Lapp, H., Ching, K. A., Block, D., et al. (2004). A gene atlas of the mouse and human protein-encoding transcriptomes. Proc. Natl. Acad. Sci. U.S.A. 101, 6062-6067. doi: 10.1073/pnas.0400782101
33. Trott, O., and Olson, A. J. (2010). AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J. Comput. Chem. 31, 455-461. doi: 10.1002/jcc.21334
34. Vollenweider, F. X., and Geyer, M. A. (2001). A systems model of altered consciousness: integrating natural and drug-induced psychoses. Brain Res. Bull. 56, 495-507. doi: 10.1016/S0361-9230(01)00646-3
35. Wang, Y., Xiao, J., Suzek, T. O., Zhang, J., Wang, J., and Bryant, S. H. (2009). PubChem: a public information system for analyzing bioactivities of small molecules. Nucleic Acids Res. 37, W623-W633. doi: 10.1093/nar/gkp456
36. Wu, C., Macleod, I., and Su, A. I. (2013). BioGPS and MyGene.info: organizing online, gene-centric information. Nucleic Acids Res. 41, D561-D565. doi: 10.1093/nar/gks1114
37. Wu, C., Orozco, C., Boyer, J., Leglise, M., Goodale, J., Batalov, S., et al. (2009). BioGPS: an extensible and customizable portal for querying and organizing gene annotation resources. Genome Biol. 10:R130. doi: 10.1186/gb-2009-10-11-r130
38. Xie, L., Kinnings, S. L., and Bourne, P. E. (2012). Novel computational approaches to polypharmacology as a means to define responses to individual drugs. Annu. Rev. Pharmacol. Toxicol. 52, 361-379. doi: 10.1146/annurev-pharmtox-010611-134630
39. Yang, L., Luo, H., Chen, J., Xing, Q., and He, L. (2009). SePreSA: a server for the prediction of populations susceptible to serious adverse drug reactions implementing the methodology of a chemical-protein interactome. Nucleic Acids Res. 37, W406-W412. doi: 10.1093/nar/gkp312
40. Yang, L., Wang, K. J., Wang, L. S., Jegga, A. G., Qin, S. Y., He, G., et al. (2011). Chemical-protein interactome and its application in off-target identification. Interdiscip. Sci. 3, 22-30. doi: 10.1007/s12539-011-0051-8