PL EN


Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
2014 | 19 | 4 |

Tytuł artykułu

FunPred-1: Protein function prediction from a protein interaction network using neighborhood analysis

Warianty tytułu

Języki publikacji

EN

Abstrakty

EN
Proteins are responsible for all biological activities in living organisms. Thanks to genome sequencing projects, large amounts of DNA and protein sequence data are now available, but the biological functions of many proteins are still not annotated in most cases. The unknown function of such non-annotated proteins may be inferred or deduced from their neighbors in a protein interaction network. In this paper, we propose two new methods to predict protein functions based on network neighborhood properties. FunPred 1.1 uses a combination of three simple-yet-effective scoring techniques: the neighborhood ratio, the protein path connectivity and the relative functional similarity. FunPred 1.2 applies a heuristic approach using the edge clustering coefficient to reduce the search space by identifying densely connected neighborhood regions. The overall accuracy achieved in FunPred 1.2 over 8 functional groups involving hetero-interactions in 650 yeast proteins is around 87%, which is higher than the accuracy with FunPred 1.1. It is also higher than the accuracy of many of the state-of-the-art protein function prediction methods described in the literature. The test datasets and the complete source code of the developed software are now freely available at http://code.google.com/p/cmaterbioinfo/.

Wydawca

-

Rocznik

Tom

19

Numer

4

Opis fizyczny

p.675-691,fig.,ref.

Twórcy

autor
  • Department of Computer Science and Engineering, Dr.Sudhir Chandra Sur Degree Engineering College, Dumdum, Kolkata, 700074, India
  • Department of Computer Science and Engineering, Netaji Subhash Engineering College, Garia, Kolkata, 700152, India
autor
  • Department of Computer Science and Engineering, Jadavpur University, Kolkata, 700032, India
autor
  • Department of Computer Science and Engineering, Jadavpur University, Kolkata, 700032, India
autor
  • Department of Computer Science and Engineering, Jadavpur University, Kolkata, 700032, India

Bibliografia

  • 1. Schwikowski, B., Uetz, P. and Fields, S. A network of protein-protein interactions in yeast. Nat. Biotechnol. 18 (2000) 1257–1261.
  • 2. Hishigaki, H., Nakai, K., Ono, T., Tanigami, A. and Takagi, T. Assessment of prediction accuracy of protein function from protein–protein interaction data. Yeast (Chichester, England) 18 (2001) 523–531.
  • 3. Chen, J., Hsu, W., Lee, M.L. and Ng. S.K. Labeling network motifs in protein interactomes for protein function prediction. IEEE 23rd International Conference on Data Engineering (2007) 546–555.
  • 4. Vazquez, A., Flammini, A., Maritan, A. and Vespignani, A. Global protein function prediction from protein-protein interaction networks. Nat. Biotechnol. 21 (2003) 697–700.
  • 5. Karaoz, U., Murali, T.M., Letovsky, S., Zheng, Y., Ding, C., Cantor, C.R. and Kasif, S. Whole-genome annotation by using evidence integration in functional-linkage networks. Proc. Natl. Acad. Sci. USA 101 (2004) 2888– 2893.
  • 6. Nabieva, E., Jim, K., Agarwal, A., Chazelle, B. and Singh, M. Wholeproteome prediction of protein function via graph-theoretic analysis of interaction maps. Bioinformatics 21 (2005) i302–i310.
  • 7. Deng, M., Mehta, S., Sun, F. and Chen, T. Inferring domain–domain interactions from protein–protein interactions. Genome Res. (2002) 1540–1548.
  • 8. Letovsky, S. and Kasif, S. Predicting protein function from protein/protein interaction data: a probabilistic approach. Bioinformatics 19 (2003) i197–i204.
  • 9. Wu, D.D. An efficient approach to detect a protein community from a seed. Proc. IEEE Symp. Comput. Intel. Bioinforma. Comput. Biol. (2005) 1–7.
  • 10. Samanta, M.P. and Liang, S. Predicting protein functions from redundancies in large-scale protein interaction networks. Proc. Natl. Acad. Sci. USA 100 (2003) 12579–12583.
  • 11. Arnau, V., Mars, S. and Marín, I. Iterative cluster analysis of protein interaction data. Bioinformatics 21 (2005) 364–378.
  • 12. Bader, G.D. and Hogue, C.W.V. An automated method for finding molecular complexes in large protein interaction networks. BMC Bioinformatics 27 (2003) 1–27.
  • 13. Altaf-Ul-Amin, M., Shinbo, Y., Mihara, K., Kurokawa, K. and Kanaya, S. Development and implementation of an algorithm for detection of protein complexes in large interaction networks. BMC Bioinformatics 7 (2006) DOI: 10.1186/1471-2105-7-207.
  • 14. Spirin, V. and Mirny, L.A. Protein complexes and functional modules in molecular networks. Proc. Natl. Acad. Sci. USA 100 (2003) 12123–12128.
  • 15. King, A.D., Przulj, N. and Jurisica, I. Protein complex prediction via costbased clustering. Bioinformatics 20 (2004) 3013–3020.
  • 16. Asthana, S., King, O.D., Gibbons, F.D. and Roth, F.P. Predicting protein complex membership using probabilistic network reliability. Genome Res. 14 (2004) 1170–1175.
  • 17. 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., AltafUl, 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. and Greenblatt, J.F. Global landscape of protein complexes in the yeast Saccharomyces cerevisiae. Nature 440 (2006) 637–643.
  • 18. Wang, H., Huang, H., Ding, C. and Nie, F. Predicting protein-protein interactions from multimodal biological data sources via nonnegative matrix tri-factorization. J. Comput. Biol. 20 (2013) 344–358.
  • 19. Chatterjee, P., Basu, S., Kundu, M., Nasipuri, M. and Plewczynski, D. PPI_SVM: prediction of protein-protein interactions using machine learning, domain-domain affinities and frequency tables. Cell. Mol. Biol. Lett. 16 (2011) 264–278.
  • 20. Wu, X., Zhu, L., Guo, J., Zhang, D.Y. and Lin, K. Prediction of yeast protein-protein interaction network: insights from the Gene Ontology and annotations. Nucleic Acids Res. 34 (2006) 2137–2150.
  • 21. Moosavi, S., Rahgozar, M. and Rahimi, A. Protein function prediction using neighbor relativity in protein-protein interaction network. Comput. Biol. Chem. 43 (2013) DOI: 10.1016/j.compbiolchem.2012.12.003.
  • 22. Peng, W., Wang, J., Wang, W., Liu, Q., Wu, F.X. and Pan, Y. Iteration method for predicting essential proteins based on orthology and proteinprotein interaction networks. BMC Syst. Biol. 6 (2012) DOI: 10.1186/1752- 0509-6-87.
  • 23. Chua, H.N., Sung, W.K. and Wong, L. Exploiting indirect neighbours and topological weight to predict protein function from protein–protein interactions. Bioinformatics 22 (2006) 1623–1630.
  • 24. Chatterjee, P., Basu, S., Kundu, M., Nasipuri, M., and Plewczynski, D. PSP_MCSVM: brainstorming consensus prediction of protein secondary structures using two-stage multiclass support vector machines. J. Mol. Model. 17 (2011) 2191–2201.

Typ dokumentu

Bibliografia

Identyfikatory

Identyfikator YADDA

bwmeta1.element.agro-b9a1adc4-4177-4154-a4a4-e94610aad2ce
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.