Chemical-protein interactome and its application in off-target identification

Abstract

Drugs exert their therapeutic and adverse effects by interacting with molecular targets. Although designed to interact with specific targets in a desirable manner, drug molecules often bind to unexpected proteins (off-targets). By activating or inhibiting off-targets and the associated biological processes and pathways, the resulting chemical-protein interactions can influence drug reaction directly or indirectly. Exploring the relationship between drug and off-targets and the downstream drug reaction can help understand the polypharmacology of the drug, hence significantly advance the drug repositioning pipeline and the application of personalized medicine in understanding and preventing adverse drug reaction. This review summarizes works on predicting off-targets via chemical-protein interactome (CPI), an interaction strength matrix of drugs across multiple human proteins aiming at exploring the unexpected drug-protein interactions, with a variety of computational strategies, including docking, chemical structure comparison and text-mining etc. Effective recall on previous knowledge, de novo prediction and subsequent experimental validation conferred us strong confidence in these methods. Such studies present prospect of large scale in silico methodologies for off-target discovery with low cost and high efficiency.

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References

  1. [1]

    Aerts, S., Lambrechts, D., Maity, S., Van Loo, P., Coessens, B., De Smet, F., Tranchevent, L.C., De Moor, B., Marynen, P., Hassan, B., Carmeliet, P., Moreau, Y. 2006. Gene prioritization through genomic data fusion. Nat Biotechnol 24, 537–544.

    PubMed  Article  CAS  Google Scholar 

  2. [2]

    Berger, S.I., Iyengar, R. 2010. Role of systems pharmacology in understanding drug adverse events. Wiley Interdisciplinary Reviews: Systems Biology and Medicine.In press. DOI: 10.1002/wsbm.114.

  3. [3]

    Berman, H.M., Battistuz, T., Bhat, T.N., Bluhm, W.F., Bourne, P.E., Burkhardt, K., Feng, Z., Gilliland, G.L., Iype, L., Jain, S., Fagan, P., Marvin, J., Padilla, D., Ravichandran, V., Schneider, B., Thanki, N., Weissig, H., Westbrook, J.D., Zardecki, C. 2002. The Protein Data Bank. Acta Crystallogr D Biol Crystallogr 58, 899–907.

    PubMed  Article  Google Scholar 

  4. [4]

    Berman, H.M., Westbrook, J., Feng, Z., Gilliland, G., Bhat, T.N., Weissig, H., Shindyalov, I.N., Bourne, P.E. 2000. The Protein Data Bank. Nucleic Acids Res 28, 235–242.

    PubMed  Article  CAS  Google Scholar 

  5. [5]

    Brooksbank, C., Cameron, G., Thornton, J. 2005. The European Bioinformatics Institute’s data resources: towards systems biology. Nucleic Acids Res 33, D46–53.

    PubMed  Article  CAS  Google Scholar 

  6. [6]

    Butcher, E.C., Berg, E.L., Kunkel, E.J. 2004. Systems biology in drug discovery. Nat Biotechnol 22, 1253–1259.

    PubMed  Article  CAS  Google Scholar 

  7. [7]

    Campillos, M., Kuhn, M., Gavin, A.C., Jensen, L.J., Bork, P. 2008. Drug target identification using side-effect similarity. Science 321, 263–266.

    PubMed  Article  CAS  Google Scholar 

  8. [8]

    Chen, J., Xu, H., Aronow, B.J., Jegga, A.G. 2007. Improved human disease candidate gene prioritization using mouse phenotype. BMC Bioinformatics 8, 392.

    PubMed  Article  Google Scholar 

  9. [9]

    Chen, J., Aronow, B.J., Jegga, A.G. 2009a. Disease candidate gene identification and prioritization using protein interaction networks. BMC Bioinformatics 10, 73.

    PubMed  Article  Google Scholar 

  10. [10]

    Chen, J., Bardes, E.E., Aronow, B.J., Jegga, A.G. 2009b. ToppGene Suite for gene list enrichment analysis and candidate gene prioritization. Nucleic Acids Res 37, W305–311.

    PubMed  Article  CAS  Google Scholar 

  11. [11]

    Chen, J.H., Linstead, E., Swamidass, S.J., Wang, D., Baldi, P. 2007. ChemDB update-full-text search and virtual chemical space. Bioinformatics 23, 2348–2351.

    PubMed  Article  CAS  Google Scholar 

  12. [12]

    Chen, Y.Z., 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.

    PubMed  Article  CAS  Google Scholar 

  13. [13]

    Chessman, D., Kostenko, L., Lethborg, T., Purcell, A.W., Williamson, N.A., Chen, Z., Kjer-Nielsen, L., Mifsud, N.A., Tait, B.D., Holdsworth, R., Almeida, C.A., Nolan, D., Macdonald, W.A., Archbold, J.K., Kellerher, A.D., Marriott, D., Mallal, S., Bharadwaj, M., Rossjohn, J., McCluskey, J. 2008. Human leukocyte antigen class I-restricted activation of CD8+ T cells provides the immunogenetic basis of a systemic drug hypersensitivity. Immunity 28, 822–832.

    PubMed  Article  CAS  Google Scholar 

  14. [14]

    de Franchi, E., Schalon, C., Messa, M., Onofri, F., Benfenati, F., Rognan, D. 2010. Binding of protein kinase inhibitors to synapsin I inferred from pair-wise binding site similarity measurements. PLoS ONE 5.

  15. [15]

    DesJarlais, R.L., Seibel, G.L., Kuntz, I.D., Furth, P.S., Alvarez, J.C., Ortiz de Montellano, P.R., DeCamp, D.L., Babe, L.M., Craik, C.S. 1990. Structure-based design of nonpeptide inhibitors specific for the human immunodeficiency virus 1 protease. Proc Natl Acad Sci USA 87, 6644–6648.

    PubMed  Article  CAS  Google Scholar 

  16. [16]

    Dunkel, M., Gunther, S., Ahmed, J., Wittig, B., Preissner, R. 2008. SuperPred: Drug classification and target prediction. Nucleic Acids Res 36, W55–59.

    PubMed  Article  CAS  Google Scholar 

  17. [17]

    Gordus, A., Krall, J.A., Beyer, E.M., Kaushansky, A., Wolf-Yadlin, A., Sevecka, M., Chang, B.H., Rush, J., MacBeath, G. 2009. Linear combinations of docking affinities explain quantitative differences in RTK signaling. Mol Syst Biol 5, 235.

    PubMed  Article  Google Scholar 

  18. [18]

    Gunther, S., Kuhn, M., Dunkel, M., Campillos, M., Senger, C., Petsalaki, E., Ahmed, J., Urdiales, E.G., Gewiess, A., Jensen, L.J., Schneider, R., Skoblo, R., Russell, R.B., Bourne, P.E., Bork, P., Preissner, R. 2008. SuperTarget and Matador: Resources for exploring drug-target relationships. Nucleic Acids Res 36, D919–922.

    PubMed  Article  Google Scholar 

  19. [19]

    Hamasaki, K., Rando, R.R. 1997. Specific binding of aminoglycosides to a human rRNA construct based on a DNA polymorphism which causes aminoglycoside-induced deafness. Biochemistry 36, 12323–12328.

    PubMed  Article  CAS  Google Scholar 

  20. [20]

    Hopkins, A.L. 2008. Network pharmacology: The next paradigm in drug discovery. Nat Chem Biol 4, 682–690.

    PubMed  Article  CAS  Google Scholar 

  21. [21]

    Huang, X.P., Setola, V., Yadav, P.N., Allen, J.A., Rogan, S.C., Hanson, B.J., Revankar, C., Robers, M., Doucette, C., Roth, B.L. 2009. Parallel functional activity profiling reveals valvulopathogens are potent 5-hydroxytryptamine(2B) receptor agonists: implications for drug safety assessment. Mol Pharmacol 76, 710–722.

    PubMed  Article  CAS  Google Scholar 

  22. [22]

    Hung, S.I., Chung, W.H., Liou, L.B., Chu, C.C., Lin, M., Huang, H.P., Lin, Y.L., Lan, J.L., Yang, L.C., Hong, H.S., Chen, M.J., Lai, P.C., Wu, M.S., Chu, C.Y., Wang, K.H., Chen, C.H., Fann, C.S., Wu, J.Y., Chen, Y.T. 2005. HLA-B*5801 allele as a genetic marker for severe cutaneous adverse reactions caused by allopurinol. Proc Natl Acad Sci USA 102, 4134–4139.

    PubMed  Article  CAS  Google Scholar 

  23. [23]

    Iorio, F., Bosotti, R., Scacheri, E., Belcastro, V., Mithbaokar, P., Ferriero, R., Murino, L., Tagliaferri, R., Brunetti-Pierri, N., Isacchi, A., di Bernardo, D. 2010. Discovery of drug mode of action and drug repositioning from transcriptional responses. Proc Natl Acad Sci U S A 107, 14621–14626.

    PubMed  Article  CAS  Google Scholar 

  24. [24]

    Irwin, J.J., Shoichet, B.K. 2005. ZINC — a free database of commercially available compounds for virtual screening. J Chem Inf Model 45, 177–182.

    PubMed  Article  CAS  Google Scholar 

  25. [25]

    Jordan, V.C., O’Malley, B.W. 2007. Selective estrogen-receptor modulators and antihormonal resistance in breast cancer. J Clin Oncol 25, 5815–5824.

    PubMed  Article  CAS  Google Scholar 

  26. [26]

    Keiser, M.J., Roth, B.L., Armbruster, B.N., Ernsberger, P., Irwin, J.J., Shoichet, B.K. 2007. Relating protein pharmacology by ligand chemistry. Nat Biotechnol 25, 197–206.

    PubMed  Article  CAS  Google Scholar 

  27. [27]

    Keiser, M.J., Setola, V., Irwin, J.J., Laggner, C., Abbas, A.I., Hufeisen, S.J., Jensen, N.H., Kuijer, M.B., Matos, R.C., Tran, T.B., Whaley, R., Glennon, R.A., Hert, J., Thomas, K.L., Edwards, D.D., Shoichet, B.K., Roth, B.L. 2009. Predicting new molecular targets for known drugs. Nature 462, 175–181.

    PubMed  Article  CAS  Google Scholar 

  28. [28]

    Kinnings, S.L., Liu, N., Buchmeier, N., Tonge, P.J., Xie, L., Bourne, P.E. 2009. Drug discovery using chemical systems biology: repositioning the safe medicine Comtan to treat multi-drug and extensively drug resistant tuberculosis. PLoS Comput Biol 5, e1000423.

    PubMed  Article  Google Scholar 

  29. [29]

    Kitchen, D.B., Decornez, H., Furr, J.R., Bajorath, J. 2004. Docking and scoring in virtual screening for drug discovery: Methods and applications. Nat Rev Drug Discov 3, 935–949.

    PubMed  Article  CAS  Google Scholar 

  30. [30]

    Kuhn, M., Campillos, M., Gonzalez, P., Jensen, L.J., Bork, P. 2008. Large-scale prediction of drug-target relationships. FEBS Lett 582, 1283–1290.

    PubMed  Article  CAS  Google Scholar 

  31. [31]

    Kuhn, M., Campillos, M., Letunic, I., Jensen, L.J., Bork, P. 2010. A side effect resource to capture phenotypic effects of drugs. Mol Syst Biol 6, 343.

    PubMed  Article  Google Scholar 

  32. [32]

    Kuntz, I.D. 1992. Structure-based strategies for drug design and discovery. Science 257, 1078–1082.

    PubMed  Article  CAS  Google Scholar 

  33. [33]

    Li, H., Gao, Z., Kang, L., Zhang, H., Yang, K., Yu, K., Luo, X., Zhu, W., Chen, K., Shen, J., Wang, X., Jiang, H. 2006. TarFisDock: A web server for identifying drug targets with docking approach. Nucleic Acids Res 34, W219–224.

    PubMed  Article  CAS  Google Scholar 

  34. [34]

    Li, J., Zhu, X., Chen, J.Y. 2009. Building diseasespecific drug-protein connectivity maps from molecular interaction networks and PubMed abstracts. PLoS Comput Biol 5, e1000450.

    PubMed  Article  Google Scholar 

  35. [35]

    Liu, T., Lin, Y., Wen, X., Jorissen, R.N., Gilson, M.K. 2007. BindingDB: a web-accessible database of experimentally determined protein-ligand binding affinities. Nucleic Acids Res 35, D198–201.

    PubMed  Article  CAS  Google Scholar 

  36. [36]

    Lomenick, B., Hao, R., Jonai, N., Chin, R.M., Aghajan, M., Warburton, S., Wang, J., Wu, R.P., Gomez, F., Loo, J.A., Wohlschlegel, J.A., Vondriska, T.M., Pelletier, J., Herschman, H.R., Clardy, J., Clarke, C.F., Huang, J. 2009. Target identification using drug affinity responsive target stability (DARTS). Proc Natl Acad Sci USA 106, 21984–21989.

    PubMed  Article  CAS  Google Scholar 

  37. [37]

    Nobeli, I., Favia, A.D., Thornton, J.M. 2009. Protein promiscuity and its implications for biotechnology. Nat Biotechnol 27, 157–167.

    PubMed  Article  CAS  Google Scholar 

  38. [38]

    Ong, S.E., Blagoev, B., Kratchmarova, I., Kristensen, D.B., Steen, H., Pandey, A., Mann, M. 2002. Stable isotope labeling by amino acids in cell culture, SILAC, as a simple and accurate approach to expression proteomics. Molecular & Cellular Proteomics: MCP 1, 376–386.

    Article  CAS  Google Scholar 

  39. [39]

    Ong, S.E., Schenone, M., Margolin, A.A., Li, X., Do, K., Doud, M.K., Mani, D.R., Kuai, L., Wang, X., Wood, J.L., Tolliday, N.J., Koehler, A.N., Marcaurelle, L.A., Golub, T.R., Gould, R.J., Schreiber, S.L., Carr, S.A. 2009. Identifying the proteins to which small-molecule probes and drugs bind in cells. Proc Natl Acad Sci USA 106, 4617–4622.

    PubMed  Article  CAS  Google Scholar 

  40. [40]

    Paolini, G.V., Shapland, R.H., van Hoorn, W.P., Mason, J.S., Hopkins, A.L. 2006. Global mapping of pharmacological space. Nat Biotechnol 24, 805–815.

    PubMed  Article  CAS  Google Scholar 

  41. [41]

    Raschi, E., Ceccarini, L., De Ponti, F., Recanatini, M. 2009. hERG-related drug toxicity and models for predicting hERG liability and QT prolongation. Expert Opin Drug Metab Toxicol 5, 1005–1021.

    PubMed  Article  CAS  Google Scholar 

  42. [42]

    Sayers, E.W., Barrett, T., Benson, D.A., Bryant, S.H., Canese, K., Chetvernin, V., Church, D.M., DiCuccio, M., Edgar, R., Federhen, S., Feolo, M., Geer, L.Y., Helmberg, W., Kapustin, Y., Landsman, D., Lipman, D.J., Madden, T.L., Maglott, D.R., Miller, V., Mizrachi, I., Ostell, J., Pruitt, K.D., Schuler, G.D., Sequeira, E., Sherry, S.T., Shumway, M., Sirotkin, K., Souvorov, A., Starchenko, G., Tatusova, T.A., Wagner, L., Yaschenko, E., Ye, J. 2009. Database resources of the National Center for Biotechnology Information. Nucleic Acids Res 37, D5–15.

    PubMed  Article  CAS  Google Scholar 

  43. [43]

    Schneider, G., Fechner, U. 2005. Computer-based de novo design of drug-like molecules. Nat Rev Drug Discov 4, 649–663.

    PubMed  Article  CAS  Google Scholar 

  44. [44]

    Seiler, K.P., George, G.A., Happ, M.P., Bodycombe, N.E., Carrinski, H.A., Norton, S., Brudz, S., Sullivan, J.P., Muhlich, J., Serrano, M., Ferraiolo, P., Tolliday, N.J., Schreiber, S.L., Clemons, P.A. 2008. ChemBank: A small-molecule screening and cheminformatics resource database. Nucleic Acids Res 36, D351–359.

    PubMed  Article  CAS  Google Scholar 

  45. [45]

    Singhal, S., Mehta, J., Desikan, R., Ayers, D., Roberson, P., Eddlemon, P., Munshi, N., Anaissie, E., Wilson, C., Dhodapkar, M., Zeddis, J., Barlogie, B. 1999. Antitumor activity of thalidomide in refractory multiple myeloma. N Engl J Med 341, 1565–1571.

    PubMed  Article  CAS  Google Scholar 

  46. [46]

    Soignet, S.L., Maslak, P., Wang, Z.G., Jhanwar, S., Calleja, E., Dardashti, L.J., Corso, D., DeBlasio, A., Gabrilove, J., Scheinberg, D.A., Pandolfi, P.P., Warrell, R.P.J. 1998. Complete remission after treatment of acute promyelocytic leukemia with arsenic trioxide. N Engl J Med 339, 1341–1348.

    PubMed  Article  CAS  Google Scholar 

  47. [47]

    Tatonetti, N.P., Liu, T., Altman, R.B. 2009. Predicting drug side-effects by chemical systems biology. Genome Biol 10, 238.

    PubMed  Article  Google Scholar 

  48. [48]

    Topol, E.J. 2004. Failing the public health-rofecoxib, Merck, and the FDA. N Engl J Med 351, 1707–1709.

    PubMed  Article  CAS  Google Scholar 

  49. [49]

    Vigers, G.P., Rizzi, J.P. 2004. Multiple active site corrections for docking and virtual screening. J Med Chem 47, 80–89.

    PubMed  Article  CAS  Google Scholar 

  50. [50]

    Wallach, I., Jaitly, N., Lilien, R. 2010. A structurebased approach for mapping adverse drug reactions to the perturbation of underlying biological pathways. PLoS ONE 5, e12063.

    PubMed  Article  Google Scholar 

  51. [51]

    Wishart, D.S., Knox, C., Guo, A.C., Shrivastava, S., Hassanali, M., Stothard, P., Chang, Z., Woolsey, J. 2006. DrugBank: A comprehensive resource for in silico drug discovery and exploration. Nucleic Acids Res 34, D668–672.

    PubMed  Article  CAS  Google Scholar 

  52. [52]

    Wishart, D.S., Knox, C., Guo, A.C., Cheng, D., Shrivastava, S., Tzur, D., Gautam, B., Hassanali, M. 2008. DrugBank: A knowledgebase for drugs, drug actions and drug targets. Nucleic Acids Res 36, D901–906.

    PubMed  Article  CAS  Google Scholar 

  53. [53]

    Xie, L., Wang, J., Bourne, P.E. 2007. In silico elucidation of the molecular mechanism defining the adverse effect of selective estrogen receptor modulators. PLoS Comput Biol 3, e217.

    PubMed  Article  Google Scholar 

  54. [54]

    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, i232–240.

    PubMed  Article  CAS  Google Scholar 

  55. [55]

    Yang, L., Chen, J., He, L. 2009a. Harvesting candidate genes responsible for serious adverse drug reactions from a chemical-protein interactome. PLoS Comput Biol 5, e1000441.

    PubMed  Article  Google Scholar 

  56. [56]

    Yang, L., Luo, H., Chen, J., Xing, Q., He, L. 2009b. 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, W406–412.

    PubMed  Article  CAS  Google Scholar 

  57. [57]

    Yang, L., Chen, J., Shi, L., Hudock, M., He, L. 2010a. Identifying unexpected therapeutic targets via chemical-protein interactome. PLoS ONE 5, e9568.

    PubMed  Article  Google Scholar 

  58. [58]

    Yang, L., Wang, K., Chen, J., Jegga, A.G., Wan, C., Guo, X., Qin S., He, G., Feng G., He, L. 2010b. Exploration of the off-targets and the off-systems for clozapine-induced agranulocytosis via the chemicalprotein interactome. PLoS Comput Biol in submission.

  59. [59]

    Young, D.W., Bender, A., Hoyt, J., McWhinnie, E., Chirn, G.W., Tao, C.Y., Tallarico, J.A., Labow, M., Jenkins, J.L., Mitchison, T.J., Feng, Y. 2008. Integrating high-content screening and ligand-target prediction to identify mechanism of action. Nat Chem Biol 4, 59–68.

    PubMed  Article  CAS  Google Scholar 

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Correspondence to Lun Yang or Lin He.

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Yang, L., Wang, KJ., Wang, LS. et al. Chemical-protein interactome and its application in off-target identification. Interdiscip Sci Comput Life Sci 3, 22–30 (2011). https://doi.org/10.1007/s12539-011-0051-8

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Key words

  • drug repositioning
  • adverse drug reaction
  • chemical-protein interactome
  • off-target
  • off-system