Protein Interactions: Mapping Interactome Networks to Support Drug Target Discovery and Selection

  • Javier De Las RivasEmail author
  • Carlos Prieto
Part of the Methods in Molecular Biology book series (MIMB, volume 910)


Proteins are biomolecular structures that build the microscopic working machinery of any living system. Proteins within the cells and biological systems do not act alone, but rather team up into macromolecular structures enclosing intricate physicochemical dynamic connections to undertake biological functions. A critical step towards unraveling the complex molecular relationships in living systems is the mapping of protein-to-protein physical “interactions”. The complete map of protein interactions that can occur in a living organism is called the “interactome”. Achieving an adequate atlas of all the protein interactions within a living system should allow to build its interaction network and to identity the “central nodes” that can be critical for the function, the homeostasis, and the movement of such system. Focusing on human studies, the data about the human interactome are most relevant for current biomedical research, because it is clear that the location of the proteins in the interactome network will allow to evaluate their centrality and to redefine the potential value of each protein as a drug target. This chapter presents our current knowledge on the human protein–protein interactome and explains how such knowledge can help us to select adequate targets for drugs.

Key words

Protein interaction PPI Interactome Protein network Drug target 


  1. 1.
    Cusick ME et al (2005) Interactome: gateway into systems biology. Hum Mol Genet 14(2):R171–R181PubMedCrossRefGoogle Scholar
  2. 2.
    Merico D, Gfeller D, Bader GD (2009) How to visually interpret biological data using networks. Nat Biotechnol 27(10):921–924PubMedCrossRefGoogle Scholar
  3. 3.
    Boone C, Bussey H, Andrews BJ (2007) Exploring genetic interactions and networks with yeast. Nat Rev Genet 8(6):437–449PubMedCrossRefGoogle Scholar
  4. 4.
    De Las Rivas J, de Luis A (2004) Interactome data and databases: different types of protein interaction. Comp Funct Genomics 5(2):173–178PubMedCrossRefGoogle Scholar
  5. 5.
    De Las Rivas J, Fontanillo C (2010) Protein–protein interactions essentials: key concepts to building and analyzing interactome networks. PLoS Comput Biol 6(6):e1000807PubMedCrossRefGoogle Scholar
  6. 6.
    Prieto C et al (2008) Human gene coexpression landscape: confident network derived from tissue transcriptomic profiles. PLoS One 3(12):e3911PubMedCrossRefGoogle Scholar
  7. 7.
    Przytycka TM, Singh M, Slonim DK (2010) Toward the dynamic interactome: it’s about time. Brief Bioinform 11(1):15–29PubMedCrossRefGoogle Scholar
  8. 8.
    Boehr DD, Wright PE (2008) Biochemistry. How do proteins interact? Science 320(5882):1429–1430PubMedCrossRefGoogle Scholar
  9. 9.
    Mackay JP et al (2007) Protein interactions: is seeing believing? Trends Biochem Sci 32(12):530–531PubMedCrossRefGoogle Scholar
  10. 10.
    Prieto C, De Las Rivas J (2006) APID: Agile Protein Interaction Data analyzer. Nucleic Acids Res 34(Web Server Issue):W298–W302PubMedCrossRefGoogle Scholar
  11. 11.
    Dreze M et al (2010) High-quality binary interactome mapping. Methods Enzymol 470:281–315PubMedCrossRefGoogle Scholar
  12. 12.
    Suter B, Kittanakom S, Stagljar I (2008) Two-hybrid technologies in proteomics research. Curr Opin Biotechnol 19(4):316–323PubMedCrossRefGoogle Scholar
  13. 13.
    Uetz P et al (2000) A comprehensive analysis of protein–protein interactions in Saccharomyces cerevisiae. Nature 403(6770):623–627PubMedCrossRefGoogle Scholar
  14. 14.
    Ito T et al (2001) A comprehensive two-hybrid analysis to explore the yeast protein interactome. Proc Natl Acad Sci USA 98(8):4569–4574PubMedCrossRefGoogle Scholar
  15. 15.
    Giot L et al (2003) A protein interaction map of Drosophila melanogaster. Science 302(5651):1727–1736PubMedCrossRefGoogle Scholar
  16. 16.
    Formstecher E et al (2005) Protein interaction mapping: a Drosophila case study. Genome Res 15(3):376–384PubMedCrossRefGoogle Scholar
  17. 17.
    Stelzl U et al (2005) A human protein–protein interaction network: a resource for annotating the proteome. Cell 122(6):957–968PubMedCrossRefGoogle Scholar
  18. 18.
    Rual JF et al (2005) Towards a proteome-scale map of the human protein–protein interaction network. Nature 437(7062):1173–1178PubMedCrossRefGoogle Scholar
  19. 19.
    Cusick ME et al (2009) Literature-curated protein interaction datasets. Nat Methods 6(1):39–46PubMedCrossRefGoogle Scholar
  20. 20.
    Salwinski L et al (2009) Recurated protein interaction datasets. Nat Methods 6(12):860–861PubMedCrossRefGoogle Scholar
  21. 21.
    Stark C et al (2006) BioGRID: a general repository for interaction datasets. Nucleic Acids Res 34(Database Issue):D535–D539PubMedCrossRefGoogle Scholar
  22. 22.
    Salwinski L et al (2004) The database of interacting proteins: 2004 update. Nucleic Acids Res 32(Database Issue):D449–D451PubMedCrossRefGoogle Scholar
  23. 23.
    Peri S et al (2003) Development of human protein reference database as an initial platform for approaching systems biology in humans. Genome Res 13(10):2363–2371PubMedCrossRefGoogle Scholar
  24. 24.
    Hermjakob H et al (2004) IntAct: an open source molecular interaction database. Nucleic Acids Res 32(Database Issue):D452–D455PubMedCrossRefGoogle Scholar
  25. 25.
    Ceol A et al (2010) MINT, the molecular interaction database: 2009 update. Nucleic Acids Res 38(Database Issue):D532–D539PubMedCrossRefGoogle Scholar
  26. 26.
    Turner B et al (2010) iRefWeb: interactive analysis of consolidated protein interaction data and their supporting evidence. Database 2010:baq023Google Scholar
  27. 27.
    Hernandez-Toro J, Prieto C, De Las Rivas J (2007) APID2NET: unified interactome graphic analyzer. Bioinformatics 23(18):2495–2497PubMedCrossRefGoogle Scholar
  28. 28.
    Perez-Fernandez J et al (2007) The 90S preribosome is a multimodular structure that is assembled through a hierarchical mechanism. Mol Cell Biol 27(15):5414–5429PubMedCrossRefGoogle Scholar
  29. 29.
    Yu H et al (2008) High-quality binary protein interaction map of the yeast interactome network. Science 322(5898):104–110PubMedCrossRefGoogle Scholar
  30. 30.
    Albert R (2005) Scale-free networks in cell biology. J Cell Sci 118(Pt 21):4947–4957PubMedCrossRefGoogle Scholar
  31. 31.
    Watts DJ, Strogatz SH (1998) Collective dynamics of ‘small-world’ networks. Nature 393(6684):440–442PubMedCrossRefGoogle Scholar
  32. 32.
    Albert R et al (2002) Statistical mechanics of complex networks. Rev Mod Phys 74(1):47CrossRefGoogle Scholar
  33. 33.
    Han JD et al (2004) Evidence for dynamically organized modularity in the yeast protein–protein interaction network. Nature 430(6995):88–93PubMedCrossRefGoogle Scholar
  34. 34.
    Jeong H et al (2001) Lethality and centrality in protein networks. Nature 411(6833):41–42PubMedCrossRefGoogle Scholar
  35. 35.
    Said MR et al (2004) Global network analysis of phenotypic effects: protein networks and toxicity modulation in Saccharomyces cerevisiae. Proc Natl Acad Sci USA 101(52):18006–18011PubMedCrossRefGoogle Scholar
  36. 36.
    Shannon P et al (2003) Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res 13(11):2498–2504PubMedCrossRefGoogle Scholar
  37. 37.
    Assenov Y et al (2008) Computing topological parameters of biological networks. Bioinformatics 24(2):282–284PubMedCrossRefGoogle Scholar
  38. 38.
    Bader GD, Hogue CW (2003) An automated method for finding molecular complexes in large protein interaction networks. BMC Bioinform 4:2CrossRefGoogle Scholar
  39. 39.
    Rivera CG, Vakil R, Bader JS (2010) NeMo: network module identification in Cytoscape. BMC Bioinform 11(Suppl 1):S61CrossRefGoogle Scholar
  40. 40.
    Satoh JI, Tabunoki H, Yamamura T (2009) Molecular network of the comprehensive multiple sclerosis brain-lesion proteome. Mult Scler 15(5):531–541PubMedCrossRefGoogle Scholar
  41. 41.
    Goni J et al (2008) A computational analysis of protein–protein interaction networks in neurodegenerative diseases. BMC Syst Biol 2:52PubMedCrossRefGoogle Scholar
  42. 42.
    Soler-Lopez M et al (2010) Interactome mapping suggests new mechanistic details underlying Alzheimer’s disease. Genome Res 21:364–376PubMedCrossRefGoogle Scholar
  43. 43.
    Pujana MA et al (2007) Network modeling links breast cancer susceptibility and centrosome dysfunction. Nat Genet 39(11):1338–1349PubMedCrossRefGoogle Scholar
  44. 44.
    Su J, Yoon BJ, Dougherty ER (2010) Identification of diagnostic subnetwork markers for cancer in human protein–protein interaction network. BMC Bioinform 11(Suppl 6):S8CrossRefGoogle Scholar
  45. 45.
    Wu ZJ et al (2010) Constructing the HBV-human protein interaction network to understand the relationship between HBV and hepatocellular carcinoma. J Exp Clin Cancer Res 29:146PubMedCrossRefGoogle Scholar
  46. 46.
    Yao C et al (2010) Multi-level reproducibility of signature hubs in human interactome for breast cancer metastasis. BMC Syst Biol 4:151PubMedCrossRefGoogle Scholar
  47. 47.
    Hopkins AL (2008) Network pharmacology: the next paradigm in drug discovery. Nat Chem Biol 4(11):682–690PubMedCrossRefGoogle Scholar
  48. 48.
    Gandhi TK et al (2006) Analysis of the human protein interactome and comparison with yeast, worm and fly interaction datasets. Nat Genet 38(3):285–293PubMedCrossRefGoogle Scholar
  49. 49.
    Jonsson PF, Bates PA (2006) Global topological features of cancer proteins in the human interactome. Bioinformatics 22(18):2291–2297PubMedCrossRefGoogle Scholar
  50. 50.
    Xu J, Li Y (2006) Discovering disease-genes by topological features in human protein–protein interaction network. Bioinformatics 22(22):2800–2805PubMedCrossRefGoogle Scholar
  51. 51.
    Goh KI et al (2007) The human disease network. Proc Natl Acad Sci USA 104(21):8685–8690PubMedCrossRefGoogle Scholar
  52. 52.
    Kitano H (2007) A robustness-based approach to systems-oriented drug design. Nat Rev Drug Discov 6(3):202–210PubMedCrossRefGoogle Scholar
  53. 53.
    Missiuro PV et al (2009) Information flow analysis of interactome networks. PLoS Comput Biol 5(4):e1000350PubMedCrossRefGoogle Scholar
  54. 54.
    Hwang WC, Zhang A, Ramanathan M (2008) Identification of information flow-modulating drug targets: a novel bridging paradigm for drug discovery. Clin Pharmacol Ther 84(5):563–572PubMedCrossRefGoogle Scholar
  55. 55.
    Grunberg R, Serrano L (2010) Strategies for protein synthetic biology. Nucleic Acids Res 38(8):2663–2675PubMedCrossRefGoogle Scholar
  56. 56.
    Florez AF et al (2010) Protein network prediction and topological analysis in Leishmania major as a tool for drug target selection. BMC Bioinform 11:484CrossRefGoogle Scholar
  57. 57.
    Liu X et al (2010) A network approach to predict pathogenic genes for Fusarium graminearum. PLoS One 5(10):e13021PubMedCrossRefGoogle Scholar
  58. 58.
    Wang Y et al (2010) Global protein–protein interaction network in the human pathogen Mycobacterium tuberculosis H37Rv. J Proteome Res 9(12):6665–6677PubMedCrossRefGoogle Scholar
  59. 59.
    Vicent S et al (2008) A novel lung cancer signature mediates metastatic bone colonization by a dual mechanism. Cancer Res 68(7):2275–2285PubMedCrossRefGoogle Scholar
  60. 60.
    Schrattenholz A, Groebe K, Soskic V (2010) Systems biology approaches and tools for analysis of interactomes and multi-target drugs. Methods Mol Biol 662:29–58PubMedCrossRefGoogle Scholar
  61. 61.
    Meyerson M, Gabriel S, Getz G (2010) Advances in understanding cancer genomes through second-generation sequencing. Nat Rev Genet 11(10):685–696PubMedCrossRefGoogle Scholar
  62. 62.
    Schrattenholz A, Soskic V (2008) What does systems biology mean for drug development? Curr Med Chem 15(15):1520–1528PubMedCrossRefGoogle Scholar
  63. 63.
    Csermely P, Agoston V, Pongor S (2005) The efficiency of multi-target drugs: the network approach might help drug design. Trends Pharmacol Sci 26(4):178–182PubMedCrossRefGoogle Scholar
  64. 64.
    Boran AD, Iyengar R (2010) Systems approaches to polypharmacology and drug discovery. Curr Opin Drug Discov Dev 13(3):297–309Google Scholar
  65. 65.
    Fliri AF, Loging WT, Volkmann RA (2010) Cause–effect relationships in medicine: a protein network perspective. Trends Pharmacol Sci 31(11):547–555PubMedCrossRefGoogle Scholar
  66. 66.
    Chautard E, Thierry-Mieg N, Ricard-Blum S (2009) Interaction networks: from protein functions to drug discovery. A review. Pathol Biol (Paris) 57(4):324–333CrossRefGoogle Scholar
  67. 67.
    Barrett T et al (2011) NCBI GEO: archive for functional genomics data sets—10 years on. Nucleic Acids Res 39(Database Issue):D1005–D1010PubMedCrossRefGoogle Scholar
  68. 68.
    Parkinson H et al (2011) ArrayExpress update—an archive of microarray and high-throughput sequencing-based functional genomics experiments. Nucleic Acids Res 39(Database Issue): D1002–D1004Google Scholar
  69. 69.
    Carter GW (2005) Inferring network interactions within a cell. Brief Bioinform 6(4):380–389PubMedCrossRefGoogle Scholar
  70. 70.
    Deng M, Sun F, Chen T (2003) Assessment of the reliability of protein–protein interactions and protein function prediction. Pac Symp Biocomput 2003:140–151Google Scholar
  71. 71.
    Lin CC et al (2010) Dynamic functional modules in co-expressed protein interaction networks of dilated cardiomyopathy. BMC Syst Biol 4:138PubMedCrossRefGoogle Scholar
  72. 72.
    Bossi A, Lehner B (2009) Tissue specificity and the human protein interaction network. Mol Syst Biol 5:260PubMedCrossRefGoogle Scholar
  73. 73.
    Hu Z et al (2009) VisANT 3.5: multi-scale network visualization, analysis and inference based on the gene ontology. Nucleic Acids Res 37(Web Server Issue):W115–W121PubMedCrossRefGoogle Scholar
  74. 74.
    Cline MS et al (2007) Integration of biological networks and gene expression data using Cytoscape. Nat Protoc 2(10):2366–2382PubMedCrossRefGoogle Scholar
  75. 75.
    Gene Ontology Consortium (2010) The gene ontology in 2010: extensions and refinements. Nucleic Acids Res 38(Database Issue):D331–D335CrossRefGoogle Scholar
  76. 76.
    Ng A et al (2006) Resources for integrative systems biology: from data through databases to networks and dynamic system models. Brief Bioinform 7(4):318–330PubMedCrossRefGoogle Scholar
  77. 77.
    George RA et al (2006) Analysis of protein sequence and interaction data for candidate disease gene prediction. Nucleic Acids Res 34(19):e130PubMedCrossRefGoogle Scholar
  78. 78.
    Jiang JQ, Dress AW, Chen M (2010), Towards prediction and prioritization of disease genes by the modularity of human phenome-genome assembled network. J Integr Bioinform 7(2):149Google Scholar
  79. 79.
    Lage K et al (2007) A human phenome-interactome network of protein complexes implicated in genetic disorders. Nat Biotechnol 25(3):309–316PubMedCrossRefGoogle Scholar
  80. 80.
    Wu X, Liu Q, Jiang R (2009) Align human interactome with phenome to identify causative genes and networks underlying disease families. Bioinformatics 25(1):98–104PubMedCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2012

Authors and Affiliations

  1. 1.Bioinformatics and Functional Genomics GroupCancer Research Center (IBMCC, CSIC/USAL)SalamancaSpain
  2. 2.Biotechnology Institute of Leon (INBIOTEC)LeonSpain

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