Local Topological Signatures for Network-Based Prediction of Biological Function

  • Wynand Winterbach
  • Piet Van Mieghem
  • Marcel J. T. Reinders
  • Huijuan Wang
  • Dick de Ridder
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7986)


In biology, similarity in structure or sequence between molecules is often used as evidence of functional similarity. In protein interaction networks, structural similarity of nodes (i.e., proteins) is often captured by comparing node signatures (vectors of topological properties of neighborhoods surrounding the nodes).

In this paper, we ask how well such topological signatures predict protein function, using protein interaction networks of the organism Saccharomyces cerevisiae. To this end, we compare two node signatures from the literature – the graphlet degree vector and a signature based on the graph spectrum – and our own simple node signature based on basic topological properties.

We find the connection between topology and protein function to be weak but statistically significant. Surprisingly, our node signature, despite its simplicity, performs on par with the other more sophisticated node signatures. In fact, we show that just two metrics, the link count and transitivity, are enough to classify protein function at a level on par with the other signatures suggesting that detailed topological characteristics are unlikely to aid in protein function prediction based on protein interaction networks.


Radial Basis Function Protein Interaction Network Receiver Operator Curve Node Signature Biological Category 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Ravasz, E., Somera, A.L., Mongru, D.A., Oltvai, Z.N., Barabási, A.L.: Hierarchical organization of modularity in metabolic networks. Science 297(5586), 1551–1555 (2002)CrossRefGoogle Scholar
  2. 2.
    Liao, C.S., Lu, K., Baym, M., Singh, R., Berger, B.: IsoRankN: spectral methods for global alignment of multiple protein networks. Bioinformatics 25(12), i253–i258 (2009)Google Scholar
  3. 3.
    Milenković, T., Ng, W.L.L., Hayes, W., Pržulj, N.: Optimal network alignment with graphlet degree vectors. Cancer Informatics 9, 121–137 (2010)CrossRefGoogle Scholar
  4. 4.
    Patro, R., Kingsford, C.: Global network alignment using multiscale spectral signatures. Bioinformatics (2012)Google Scholar
  5. 5.
    Milenković, T., Memišević, V., Ganesan, A.K., Pržulj, N.: Systems-level cancer gene identification from protein interaction network topology applied to melanogenesis-related functional genomics data. Journal of The Royal Society Interface 7(44), 423–437 (2010)CrossRefGoogle Scholar
  6. 6.
    Milenković, T., Pržulj, N.: Uncovering biological network function via graphlet degree signatures. Cancer Informatics 6, 257–273 (2008)Google Scholar
  7. 7.
    Milenkovic, T., Lai, J., Pržulj, N.: GraphCrunch: A tool for large network analyses. BMC Bioinformatics 9(1),  70 (2008)Google Scholar
  8. 8.
    Kim, W.K., Marcotte, E.M.: Age-Dependent evolution of the yeast protein interaction network suggests a limited role of gene duplication and divergence. PLoS Computatinal Biology 4(11) (November 2008)Google Scholar
  9. 9.
    McGary, K., Lee, I., Marcotte, E.: Broad network-based predictability of saccharomyces cerevisiae gene loss-of-function phenotypes. Genome Biology 8(12), R258 (2007)Google Scholar
  10. 10.
    Krogan, N.J., Cagney, G., Yu, H., Zhong, G., Guo, X., Ignatchenko, A., Li, J., Pu, S., Datta, N., Tikuisis, A.P., Punna, T., Peregrín-Alvarez, J.M., Shales, M., Zhang, X., Davey, M., Robinson, M.D., Paccanaro, A., Bray, J.E., Sheung, A., Beattie, B., Richards, D.P., Canadien, V., Lalev, A., Mena, F., Wong, P., Starostine, A., Canete, M.M., Vlasblom, J., Wu, S., Orsi, C., Collins, S.R., Chandran, S., Haw, R., Rilstone, J.J., Gandi, K., Thompson, N.J., Musso, G., St Onge, P., Ghanny, S., Lam, M.H.Y., Butland, G., Altaf-Ul, A.M., Kanaya, S., Shilatifard, A., O’Shea, E., Weissman, J.S., Ingles, C.J., Hughes, T.R., Parkinson, J., Gerstein, M., Wodak, S.J., Emili, A., Greenblatt, J.F.: Global landscape of protein complexes in the yeast saccharomyces cerevisiae. Nature 440(7084), 637–643 (2006)CrossRefGoogle Scholar
  11. 11.
    von Mering, C., Krause, R., Snel, B., Cornell, M., Oliver, S.G., Fields, S., Bork, P.: Comparative assessment of large-scale data sets of protein-protein interactions. Nature 417(6887), 399–403 (2002)CrossRefGoogle Scholar
  12. 12.
    Ruepp, A., Zollner, A., Maier, D., Albermann, K., Hani, J., Mokrejs, M., Tetko, I., Güldener, U., Mannhaupt, G., Münsterkötter, M., Mewes, H.W.: The funcat, a functional annotation scheme for systematic classification of proteins from whole genomes. Nucleic Acids Research 32(18), 5539–5545 (2004)CrossRefGoogle Scholar
  13. 13.
    Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., Duchesnay, E.: Scikit-learn: Machine learning in Python. Journal of Machine Learning Research 12, 2825–2830 (2011)Google Scholar
  14. 14.
    Wessels, L.F.A., Reinders, M.J.T., Hart, A.A.M., Veenman, C.J., Dai, H., He, Y.D., van’t Veer, L.J.: A protocol for building and evaluating predictors of disease state based on microarray data. Bioinformatics 21(19), 3755–3762 (2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Wynand Winterbach
    • 1
    • 2
  • Piet Van Mieghem
    • 1
  • Marcel J. T. Reinders
    • 2
    • 3
    • 4
  • Huijuan Wang
    • 1
  • Dick de Ridder
    • 2
    • 3
    • 4
  1. 1.Network Architectures and Services GroupDelft University of TechnologyDelftThe Netherlands
  2. 2.Delft Bioinformatics Lab, Department of Intelligent Systems, Faculty of Electrical Engineering, Mathematics and Computer ScienceDelft University of TechnologyDelftThe Netherlands
  3. 3.Netherlands Bioinformatics CenterNijmegenThe Netherlands
  4. 4.Kluyver Centre for Genomics of Industrial FermentationDelftThe Netherlands

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