Skip to main content
NCBI home page
Protein & Cell logoLink to Protein & Cell
. 2012 Mar 17;3(2):132–139. doi: 10.1007/s13238-012-2011-z

Study of drug function based on similarity of pathway fingerprint

Hao Ye 1,2, Kailin Tang 2, Linlin Yang 1,2, Zhiwei Cao 3, Yixue Li 1,2,✉
PMCID: PMC4875411  PMID: 22426982

Abstract

Drugs sharing similar therapeutic function may not bind to the same group of targets. However, their targets may be involved in similar pathway profiles which are associated with certain pathological process. In this study, pathway fingerprint was introduced to indicate the profile of significant pathways being influenced by the targets of drugs. Then drug-drug network was further constructed based on significant similarity of pathway fingerprints. In this way, the functions of a drug may be hinted by the enriched therapeutic functions of its neighboring drugs. In the test of 911 FDA approved drugs with more than one known target, 471 drugs could be connected into networks. 760 significant associations of drug-therapeutic function were generated, among which around 60% of them were supported by scientific literatures or ATC codes of drug functional classification. Therefore, pathway fingerprints may be useful to further study on the potential function of known drugs, or the unknown function of new drugs.

Electronic Supplementary Material

Supplementary material is available for this article at 10.1007/s13238-012-2011-z and is accessible for authorized users.

Keywords: pathway fingerprint, drug-drug network, therapeutic target, function prediction

Electronic supplementary material

13238_2012_2011_MOESM1_ESM.xls (325.5KB, xls)

Supplementary material, approximately 325 KB.

Footnotes

Electronic Supplementary Material

Supplementary material is available for this article at 10.1007/s13238-012-2011-z and is accessible for authorized users.

References

  1. Adams C.P., Brantner V.V. Estimating the cost of new drug development: is it really 802 million dollars? Health Aff (Millwood) 2006;25:420–428. doi: 10.1377/hlthaff.25.2.420. [DOI] [PubMed] [Google Scholar]
  2. Anandarajah A.P., Schwarz E.M., Totterman S., Monu J., Feng C.Y., Shao T., Haas-Smith S.A., Ritchlin C.T. The effect of etanercept on osteoclast precursor frequency and enhancing bone marrow oedema in patients with psoriatic arthritis. Ann Rheum Dis. 2008;67:296–301. doi: 10.1136/ard.2007.076091. [DOI] [PubMed] [Google Scholar]
  3. Andraos J. Kinetic plasticity and the determination of product ratios for kinetic schemes leading to multiple products without rate laws: new methods based on directed graphs. Can J Chem. 2008;86:342–357. doi: 10.1139/v08-020. [DOI] [Google Scholar]
  4. Azzoli C.G., Krug L.M., Miller V.A., Kris M.G., Mass R. Trastuzumab in the treatment of non-small cell lung cancer. Semin Oncol. 2002;29:59–65. doi: 10.1053/sonc.2002.31526. [DOI] [PubMed] [Google Scholar]
  5. Barabási A.L., Oltvai Z.N. Network biology: understanding the cell’s functional organization. Nat Rev Genet. 2004;5:101–113. doi: 10.1038/nrg1272. [DOI] [PubMed] [Google Scholar]
  6. Chen L., Feng K.Y., Cai Y.D., Chou K.C., Li H.P. Predicting the network of substrate-enzyme-product triads by combining compound similarity and functional domain composition. BMC Bioinformatics. 2010;11:293. doi: 10.1186/1471-2105-11-293. [DOI] [PMC free article] [PubMed] [Google Scholar]
  7. Chong C.R., Sullivan D.J., Jr New uses for old drugs. Nature. 2007;448:645–646. doi: 10.1038/448645a. [DOI] [PubMed] [Google Scholar]
  8. Chou K.C. Low-frequency resonance and cooperativity of hemoglobin. Trends Biochem Sci. 1989;14:212–213. doi: 10.1016/0968-0004(89)90026-1. [DOI] [PubMed] [Google Scholar]
  9. Chou K.C. A novel approach to predicting protein structural classes in a (20-1)-D amino acid composition space. Proteins. 1995;21:319–344. doi: 10.1002/prot.340210406. [DOI] [PubMed] [Google Scholar]
  10. Chou K.C. Review: Prediction of HIV protease cleavage sites in proteins. Anal Biochem. 1996;233:1–14. doi: 10.1006/abio.1996.0001. [DOI] [PubMed] [Google Scholar]
  11. Chou K. C. Prediction of protein cellular attributes using pseudo amino acid composition. PROTEINS: Structure,^Function, and Genetics. 2001;43:246–255. doi: 10.1002/prot.1035. [DOI] [PubMed] [Google Scholar]
  12. Chou K.C. Graphic rule for drug metabolism systems. Curr Drug Metab. 2010;11:369–378. doi: 10.2174/138920010791514261. [DOI] [PubMed] [Google Scholar]
  13. Chou K.C., Shen H.B. Signal-CF: a subsite-coupled and window-fusing approach for predicting signal peptides. Biochem Biophys Res Commun. 2007;357:633–640. doi: 10.1016/j.bbrc.2007.03.162. [DOI] [PubMed] [Google Scholar]
  14. Chou K.C., Wu Z.C., Xiao X. iLoc-Euk: a multi-label classifier for predicting the subcellular localization of singleplex and multiplex eukaryotic proteins. PLoS One. 2011;6:e18258. doi: 10.1371/journal.pone.0018258. [DOI] [PMC free article] [PubMed] [Google Scholar]
  15. Deitchman D., Braselton J.P., Hayes D.C., Stratman R.L. The ganglion-blocked, angiotensin II-supported rat: a model for demonstrating antihypertensive vasodilator activity. J Pharmacol Methods. 1980;3:311–321. doi: 10.1016/0160-5402(80)90073-X. [DOI] [PubMed] [Google Scholar]
  16. Delord J.P., Quideau S., Rochaix P., Caselles O., Couderc B., Hennebelle I., Courbon F., Canal P., Allal B.C. Trastuzumab induced in vivo tissue remodelling associated in vitro with inhibition of the active forms of AKT and PTEN and RhoB induction in an ovarian carcinoma model. Br J Cancer. 2010;103:61–72. doi: 10.1038/sj.bjc.6605699. [DOI] [PMC free article] [PubMed] [Google Scholar]
  17. Fintini D., Brufani C., Cappa M. Profile of mecasermin for the long-term treatment of growth failure in children and adolescents with severe primary IGF-1 deficiency. Ther Clin Risk Manag. 2009;5:553–559. doi: 10.2147/tcrm.s6178. [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Fodor M. [Current views on the metabolic syndrome and the effects of antipsychotic drugs] Psychiatr Hung. 2011;26:196–200. [PubMed] [Google Scholar]
  19. He Z., Zhang J., Shi X.H., Hu L.L., Kong X., Cai Y.D., Chou K.C. Predicting drug-target interaction networks based on functional groups and biological features. PLoS One. 2010;5:e9603. doi: 10.1371/journal.pone.0009603. [DOI] [PMC free article] [PubMed] [Google Scholar]
  20. Hu L.L., Chen C., Huang T., Cai Y.D., Chou K.C. Predicting biological functions of compounds based on chemical-chemical interactions. PLoS One. 2011;6:e29491. doi: 10.1371/journal.pone.0029491. [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. Hu L.L., Huang T., Cai Y.D., Chou K.C. Prediction of body fluids where proteins are secreted into based on protein interaction network. PLoS One. 2011;6:e22989. doi: 10.1371/journal.pone.0022989. [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. Huang T., Shi X.H., Wang P., He Z., Feng K.Y., Hu L., Kong X., Li Y.X., Cai Y.D., Chou K.C. Analysis and prediction of the metabolic stability of proteins based on their sequential features, subcellular locations and interaction networks. PLoS One. 2010;5:e10972. doi: 10.1371/journal.pone.0010972. [DOI] [PMC free article] [PubMed] [Google Scholar]
  23. Huang W., Sherman B.T., Lempicki R.A. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc. 2009;4:44–57. doi: 10.1038/nprot.2008.211. [DOI] [PubMed] [Google Scholar]
  24. Kamikawa Y., Shimo Y. Different spasmolytic effects of smooth muscle relaxants on the guinea-pig esophageal muscularis mucosae contracted by carbachol or high potassium in vitro. Eur J Pharmacol. 1987;136:39–48. doi: 10.1016/0014-2999(87)90776-X. [DOI] [PubMed] [Google Scholar]
  25. Kanehisa M., Goto S., Furumichi M., Tanabe M., Hirakawa M. KEGG for representation and analysis of molecular networks involving diseases and drugs. Nucleic Acids Res. 2010;38:D355–D360. doi: 10.1093/nar/gkp896. [DOI] [PMC free article] [PubMed] [Google Scholar]
  26. Keating G.M. Mecasermin. BioDrugs. 2008;22:177–188. doi: 10.2165/00063030-200822030-00004. [DOI] [PubMed] [Google Scholar]
  27. Knox C., Law V., Jewison T., Liu P., Ly S., Frolkis A., Pon A., Banco K., Mak C., Neveu V., et al. DrugBank 3.0: a comprehensive resource for ‘omics’ research on drugs. Nucleic Acids Res. 2011;39:D1035–D1041. doi: 10.1093/nar/gkq1126. [DOI] [PMC free article] [PubMed] [Google Scholar]
  28. Kondo N., Ishiguro Y., Kimura M., Sano D., Fujita K., Sakakibara A., Taguchi T., Toth G., Matsuda H., Tsukuda M. Antitumor effect of gefitinib on head and neck squamous cell carcinoma enhanced by trastuzumab. Oncol Rep. 2008;20:373–378. [PubMed] [Google Scholar]
  29. Krantz A. Protein-site targeting. Diversification of the drug discovery process. Nat Biotechnol. 1998;16:1294. doi: 10.1038/4243. [DOI] [PubMed] [Google Scholar]
  30. Lazner F., Gowen M., Pavasovic D., Kola I. Osteopetrosis and osteoporosis: two sides of the same coin. Hum Mol Genet. 1999;8:1839–1846. doi: 10.1093/hmg/8.10.1839. [DOI] [PubMed] [Google Scholar]
  31. Liao C.H., Chang C.S., Wei W.C., Chang S.N., Liao C.C., Lane H.Y., Sung F.C. Schizophrenia patients at higher risk of diabetes, hypertension and hyperlipidemia: a population-based study. Schizophr Res. 2011;126:110–116. doi: 10.1016/j.schres.2010.12.007. [DOI] [PubMed] [Google Scholar]
  32. Mineo J.F., Bordron A., Quintin-Roué I., Loisel S., Ster K.L., Buhé V., Lagarde N., Berthou C. Recombinant humanized anti-HER2/neu antibody (Herceptin) induces cellular death of glioblastomas. Br J Cancer. 2004;91:1195–1199. doi: 10.1038/sj.bjc.6602089. [DOI] [PMC free article] [PubMed] [Google Scholar]
  33. Nacher J.C., Schwartz J.M. A global view of drug-therapy interactions. BMC Pharmacol. 2008;8:5. doi: 10.1186/1471-2210-8-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
  34. Timberlake W.H., Schwab R.S. Experimental preparation W-483 in the treatment of Parkinson’s disease. N Engl J Med. 1952;247:98–100. doi: 10.1056/NEJM195207172470306. [DOI] [PubMed] [Google Scholar]
  35. Tobinick E.L. The value of drug repositioning in the current pharmaceutical market. Drug News Perspect. 2009;22:119–125. doi: 10.1358/dnp.2009.22.2.1343228. [DOI] [PubMed] [Google Scholar]
  36. Wermuth C.G. Multitargeted drugs: the end of the “one-target-one-disease” philosophy? Drug Discov Today. 2004;9:826–827. doi: 10.1016/S1359-6446(04)03213-1. [DOI] [PubMed] [Google Scholar]
  37. Willett P., Barnard J.M., Downs G.M. Chemical similarity searching. J Chem Inf Comput Sci. 1998;38:983–996. doi: 10.1021/ci9800211. [DOI] [Google Scholar]
  38. Xiao X., Lin W.Z., Chou K.C. Using grey dynamic modeling and pseudo amino acid composition to predict protein structural classes. J Comput Chem. 2008;29:2018–2024. doi: 10.1002/jcc.20955. [DOI] [PubMed] [Google Scholar]
  39. Xiao X., Wang P., Chou K.C. GPCR-CA: A cellular automaton image approach for predicting G-protein-coupled receptor functional classes. J Comput Chem. 2009;30:1414–1423. doi: 10.1002/jcc.21163. [DOI] [PubMed] [Google Scholar]
  40. Yildirim M.A., Goh K.I., Cusick M.E., Barabási A.L., Vidal M. Drug-target network. Nat Biotechnol. 2007;25:1119–1126. doi: 10.1038/nbt1338. [DOI] [PubMed] [Google Scholar]
  41. Zhou G.P., Deng M.H. An extension of Chou’s graphic rules for deriving enzyme kinetic equations to systems involving parallel reaction pathways. Biochem J. 1984;222:169–176. doi: 10.1042/bj2220169. [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

13238_2012_2011_MOESM1_ESM.xls (325.5KB, xls)

Supplementary material, approximately 325 KB.

Articles from Protein & Cell are provided here courtesy of Oxford University Press

RESOURCES