Ranked Gene Ontology Based Protein Function Prediction by Analysis of Protein–Protein Interactions

  • Kaustav Sengupta
  • Sovan Saha
  • Piyali Chatterjee
  • Mahantapas Kundu
  • Mita Nasipuri
  • Subhadip Basu
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 701)


Computational function prediction of unknown protein is a challenging task in proteomics. As protein–protein interactions directly contribute to the protein function, recent efforts attempt to infer about proteins’ functional group by studying their interactions. Recently, use of hierarchical relationship between functional groups characterized by Gene Ontology improves prediction ability compared to hierarchy unaware “flat” prediction methods. As a protein may have multiple functional groups with different degrees of evidences, function prediction is viewed as a complex multi-class classification problem. In this paper, we propose a method which assigns multiple Gene Ontology terms to unknown protein from its neighborhood topology using a ranking methodology showing different levels of association. This work achieves precision of 0.74, recall of 0.67, and F-score of 0.73, respectively, on 19,247 human proteins having 8,548,002 interactions in between themselves.


Gene ontology (GO) Enrichment score Edge weight Shore protein Bridge protein Fjord protein Gene ontology similarity Protein–Protein interaction network (PPIN) 


  1. 1.
    Tiwari, A.K., Srivastava, R.: A survey of computational intelligence techniques in protein function prediction. Int. J. Proteomics. 2014, 845479 (2014)CrossRefGoogle Scholar
  2. 2.
    Schwikowski, B., Uetz, P., Fields, S.: A network of protein-protein interactions in yeast. Nat. Biotechnol. 18, 1257–1261 (2000)CrossRefGoogle Scholar
  3. 3.
    Hishigaki, H., Nakai, K., Ono, T., Tanigami, A., Takagi, T.: Assessment of prediction accuracy of protein function from protein–protein interaction data. Yeast 18, 523–531 (2001)CrossRefGoogle Scholar
  4. 4.
    Chen, J., Hsu, W., Lee, M.L., Ng, S.-K.: Labeling network motifs in protein interactomes for protein function prediction. In: 2007 IEEE 23rd International Conference on Data Engineering, pp. 546–555. IEEE (2007)Google Scholar
  5. 5.
    Vazquez, A., Flammini, A., Maritan, A., Vespignani, A.: Global protein function prediction from protein-protein interaction networks. Nat. Biotechnol. 21, 697–700 (2003)CrossRefGoogle Scholar
  6. 6.
    Karaoz, U., Murali, T.M., Letovsky, S., Zheng, Y., Ding, C., Cantor, C.R., Kasif, S.: Whole-genome annotation by using evidence integration in functional-linkage networks. Proc. Natl. Acad. Sci. USA 101, 2888–2893 (2004)CrossRefGoogle Scholar
  7. 7.
    Nabieva, E., Jim, K., Agarwal, A., Chazelle, B., Singh, M.: Whole-proteome prediction of protein function via graph-theoretic analysis of interaction maps. Bioinformatics 21(Suppl 1), i302–i310 (2005)CrossRefGoogle Scholar
  8. 8.
    Sharan, R., Ulitsky, I., Shamir, R.: Network-based prediction of protein function. Mol. Syst. Biol. 3 (2007)Google Scholar
  9. 9.
    King, A.D., Przulj, N., Jurisica, I.: Protein complex prediction via cost-based clustering. Bioinformatics 20, 3013–3020 (2004)CrossRefGoogle Scholar
  10. 10.
    Zhao, B., Wang, J., Li, M., Li, X., Li, Y., Wu, F.-X., Pan, Y.: A new method for predicting protein functions from dynamic weighted interactome networks. IEEE Trans. Nanobiosci. 15, 131–139 (2016)CrossRefGoogle Scholar
  11. 11.
    Piovesan, D., Giollo, M., Leonardi, E., Ferrari, C., Tosatto, S.C.E.: INGA: Protein function prediction combining interaction networks, domain assignments and sequence similarity. Nucleic Acids Res. 43, W134–W140 (2015)CrossRefGoogle Scholar
  12. 12.
    Peng, W., Wang, J., Wang, W., Liu, Q., Wu, F.-X., Pan, Y.: Iteration method for predicting essential proteins based on orthology and protein-protein interaction networks. BMC Syst. Biol. 6, 87 (2012)CrossRefGoogle Scholar
  13. 13.
    Saha, S., Chatterjee, P., Basu, S., Kundu, M., Nasipuri, M.: Improving prediction of protein function from protein interaction network using intelligent neighborhood approach. In: Proceedings of 2012 International Conference on Communications, Devices and Intelligent Systems, CODIS (2012)Google Scholar
  14. 14.
    Saha, S., Chatterjee, P., Basu, S., Kundu, M., Nasipuri, M.: FunPred-1: Protein function prediction from a protein interaction network using neighborhood analysis. Cell. Mol. Biol. Lett. 19 (2014)Google Scholar
  15. 15.
    Saha, S., Chatterjee, P., Basu, S., Nasipuri, M.: Gene Ontology Based Function Prediction of Human Protein Using Protein Sequence and Neighborhood Property of PPI Network. In: Proceedings of 5th International Conference on Frontiers in Intelligent Computing: Theory and Applications: FICTA 2016, vol. 2, pp. 109–118. Springer, Singapore (2017)Google Scholar
  16. 16.
    Szklarczyk, D., Franceschini, A., Wyder, S., Forslund, K., Heller, D., Huerta-Cepas, J., Simonovic, M., Roth, A., Santos, A., Tsafou, K.P., Kuhn, M., Bork, P., Jensen, L.J., von Mering, C.: STRING v10: protein-protein interaction networks, integrated over the tree of life. Nucleic Acids Res. 43, D447–D452 (2015)CrossRefGoogle Scholar
  17. 17.
    The UniProt Consortium: UniProt: a hub for protein information. Nucleic Acids Res. 43, D204–D212 (2014)CrossRefGoogle Scholar
  18. 18.
    Ashburner, M., Ball, C.A., Blake, J.A., Botstein, D., Butler, H., Cherry, J.M., Davis, A.P., Dolinski, K., Dwight, S.S., Eppig, J.T., Harris, M.A., Hill, D.P., Issel-Tarver, L., Kasarskis, A., Lewis, S., Matese, J.C., Richardson, J.E., Ringwald, M., Rubin, G.M., Sherlock, G.: Gene Ontology: tool for the unification of biology. Nat. Genet. 25, 25–29 (2000)CrossRefGoogle Scholar
  19. 19.
    Franceschini, A., Szklarczyk, D., Frankild, S., Kuhn, M., Simonovic, M., Roth, A., Lin, J., Minguez, P., Bork, P., Von Mering, C., Jensen, L.J.: STRING v9.1: Protein-protein interaction networks, with increased coverage and integration. Nucleic Acids Res. 41, 808–815 (2013)CrossRefGoogle Scholar
  20. 20.
    Hanna, E.M., Zaki, N.: Detecting protein complexes in protein interaction networks using a ranking algorithm with a refined merging procedure. 15, 1–11 (2014)Google Scholar
  21. 21.
    Wang, S., Wu, F.: Detecting overlapping protein complexes in PPI networks based on robustness. Proteome Sci. 11, S18 (2013)CrossRefGoogle Scholar
  22. 22.
    Du, Z., Li, L., Chen, C.F., Yu, P.S., Wang, J.Z.: G-SESAME: Web tools for GO-term-based gene similarity analysis and knowledge discovery. Nucleic Acids Res. 37, 345–349 (2009)CrossRefGoogle Scholar
  23. 23.
    Moosavi, S., Rahgozar, M., Rahimi, A.: Protein function prediction using neighbor relativity in protein-protein interaction network. Comput. Biol. Chem. 43, 11–16 (2013)CrossRefGoogle Scholar
  24. 24.
    Chua, H.N., Sung, W.-K., Wong, L.: Exploiting indirect neighbours and topological weight to predict protein function from protein-protein interactions. Bioinformatics 22, 1623–1630 (2006)CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  • Kaustav Sengupta
    • 1
  • Sovan Saha
    • 2
  • Piyali Chatterjee
    • 3
  • Mahantapas Kundu
    • 1
  • Mita Nasipuri
    • 1
  • Subhadip Basu
    • 1
  1. 1.Department of Computer Science and EngineeringJadavpur UniversityKolkataIndia
  2. 2.Department of Computer Science and EngineeringDr. Sudhir Chandra Sur Degree Engineering CollegeDum Dum, KolkataIndia
  3. 3.Department of Computer Science and EngineeringNetaji Subhash Engineering CollegeGaria, KolkataIndia

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