Prediction of Essential Proteins by Integration of PPI Network Topology and Protein Complexes Information

  • Jun Ren
  • Jianxin Wang
  • Min Li
  • Huan Wang
  • Binbin Liu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6674)


Identifying essential proteins is important for understandingthe minimal requirements for cellular survival and development.Numerouscomputational methodshave been proposed to identify essential proteins from protein-protein interaction (PPI) network.However most of methods only use the PPI network topology information. HartGT indicated that essentiality is a product of theprotein complex rather than the individual protein. Based on these, we propose a new method ECC to identify essential proteins by integration of subgraph centrality (SC) of PPI network and protein complexes information.We apply ECC method and six centrality methods on the yeast PPI network. The experimental results show that the performance of ECCis much better than that of six centrality methods, whichmeans that the prediction of essential proteins based on both networktopology and protein complexes set is much better than that only based on networktopology. Moreover, ECC has a significant improvement in predictionof low-connectivity essential proteins.


essential proteins protein complexes subgraph centrality 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Kamath, R.S., Fraser, A.G., et al.: Systematic functional analysis of the Caenorhabditis elegans genome using RNAi. Nature 421, 231–237 (2003)CrossRefGoogle Scholar
  2. 2.
    Pal, C., Papp, B., Hurst, L.: Genomic function: Rate of evolution and gene dispensability. Nature 411(6841), 1046–1049 (2003)Google Scholar
  3. 3.
    Zhang, J.Z., He, X.L.: Significant impact of protein dispensability on the instantaneous rate of protein evolution. Mol. Biol. Evol. 22(4), 1147–1155 (2005)CrossRefGoogle Scholar
  4. 4.
    Kondrashov, F.A., Ogurtsov, A.Y., Kondrashov, A.S.: Bioinformatical assay of human gene morbidity. Nucl. Acids Res. 32(5), 1731–1737 (2004)CrossRefGoogle Scholar
  5. 5.
    Furney, S.J., Alba, M.M., Lopez-Bigas, N.: Differencesin the evolutionary history of disease genes affected by dominantor recessive mutations. BMC Genomics 7, 165 (2006)CrossRefGoogle Scholar
  6. 6.
    Jeong, H., Mason, S.P., Barabasi, A.L., Oltvai, Z.N.: Lethality and centrality in protein networks. Nature 411, 41–42 (2001)CrossRefGoogle Scholar
  7. 7.
    Li, M., Wang, J.X., Wang, H., Pan, Y.: Essential Proteins Discovery from Weighted Protein Interaction Networks. In: Borodovsky, M., Gogarten, J.P., Przytycka, T.M., Rajasekaran, S. (eds.) ISBRA 2010. LNCS, vol. 6053, pp. 89–100. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  8. 8.
    He, X.L., Zhang, J.Z.: Why Do Hubs Tend to Be Essential in Protein Networks? PloS Genetics 2(6), 826–834 (2006)CrossRefGoogle Scholar
  9. 9.
    Zotenko, E., Mestre, J., O’Leary, D.P., Przytycka, T.M.: Why Do Hubs in the Yeast Protein Interaction Network Tend To Be Essential: Reexamining the Connection between the Network Topology and Essentiality. PLoS Comput. Biol. 4(8), 1–16 (2008)MathSciNetCrossRefGoogle Scholar
  10. 10.
    Ernesto, E.: Virtual identification of essential proteins within the protein interaction network of yeast. Proteomics 6(1), 35–40 (2006)CrossRefGoogle Scholar
  11. 11.
    Chua, H.N., Tew, K.L., Li, X.L., Ng, S.-K.: A Unified Scoring Scheme for Detecting Essential Proteins in Protein Interaction Networks. In: 20th ICTAI, vol. 2, pp. 66–73 (2008)Google Scholar
  12. 12.
    Acencio, M.L., Lemke, N.: Towards the prediction of essential genes by integration of nework topology, cellular localization and biological process information. BMC Bioinformatics 10, 290 (2009)CrossRefGoogle Scholar
  13. 13.
    Hart, G.T., Lee, I., Marcotte, E.: A high-accuracy consensus map of yeast protein complexes reveals modular nature of gene essentiality. BMC Bioinformatics 8, 236 (2007)CrossRefGoogle Scholar
  14. 14.
    Liu, G.M., Wong, L., Chua, N.: Complex Discovery from Weighted PPI Networks. Bioinformatics 25(15), 1891–1897 (2009)CrossRefGoogle Scholar
  15. 15.
    Maslov, S., Sneppen, K.: Specificity and stability in topology of protein networks. Science 296(5569), 910–913 (2002)CrossRefGoogle Scholar
  16. 16.
    Jacob, R., Koschtzki, D., Lehmann, K.A., et al.: Algorithms for Centrality Indices. In: Brandes, U., Erlebach, T. (eds.) Network Analysis. LNCS, vol. 3418, pp. 62–82. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  17. 17.
    Mason, O., Verwoerd, M.: Graph theory and networks in biology. IET Systems Biology 1(2), 89–119 (2006)CrossRefGoogle Scholar
  18. 18.
    Estrada, E., Rodríguez-Velázquez, J.: Subgraph centrality in complex networks. Phys. Rev. E. 71(5), 056103 (2005)MathSciNetCrossRefGoogle Scholar
  19. 19.
    Milo, R., Itzkovitz, S., Kashtan, N., et al.: Superfamilies of designed and evolved networks. Science 303(5663), 1538–1542 (2004)CrossRefGoogle Scholar
  20. 20.
    Xenarios, I., Salwínski, L., Duan, X.J., et al.: DIP, the Database of Interacting Proteins: a research tool for studying cellular networks of protein interactions. Nucleic Acids Res. 30, 303–305 (2002)CrossRefGoogle Scholar
  21. 21.
    Mewes, H.W., Frishman, D., Gruber, C., et al.: MIPS: a database for genomes and protein sequences. Nucleic Acids Res. 28, 37–40 (2000)CrossRefGoogle Scholar
  22. 22.
    Radicchi, F., Castellano, C., Cecconi, F., et al.: Defining and identifying communities in networks. Proc. Natl. Acad. Sci. USA 101(9), 2658–2663 (2004)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Jun Ren
    • 1
    • 2
  • Jianxin Wang
    • 1
  • Min Li
    • 1
  • Huan Wang
    • 1
  • Binbin Liu
    • 1
  1. 1.School of Information Science and EngineeringCentral South UniversityChangshaChina
  2. 2.College of Information Science and TechnologyHunan Agricultural UniversityChangshaChina

Personalised recommendations