A Structure based Approach for Accurate Prediction of Protein Interactions Networks

Hafeez Ur Rehman, Usman Zafar, Alfredo Benso, Naveed Islam

Abstract

In the recent days, extraordinary revolution in genome sequencing technologies have produced an overwhelming amount of genes that code for proteins, resulting in deluge of proteomics data. Since proteins are involved in almost every biological activity, therefore due to this rapid uncovering of biological “facts”, the field of System Biology now stands on the doorstep of considerable theoretical and practical advancements. Precise understanding of proteins, specially their functional associations or interactions are inevitable to explicate how complex biological processes occur at molecular level, as well as to understand how these processes are controlled and modified in different disease states. In this paper, we present a novel protein structure based method to precisely predict the interactions of two putative protein pairs. We also utilize the interspecies relationship of proteins i.e., the sequence homology, which is crucial in cases of limited information from other sources of biological data. We further enhance our model to account for protein binding sites by linking individual residues in structural templates which bind to other residues. Finally, we evaluate our model by combining different sources of information using Naive Bayes classification. The proposed model provides substantial improvements in terms of accuracy, precision, recall when compared with previous approaches. We report an accuracy of 90% when tested for a protein interaction network of yeast proteome.

References

  1. A. Shoemaker, B. and R. Panchenko, A. (2007a). Deciphering protein-protein interactions. part i. experimental techniques and databases. PLOS Comput. Biol., 3(3):e42.
  2. A. Shoemaker, B. and R. Panchenko, A. (2007b). Deciphering protein-protein interactions. part ii. computational methods to predict protein and domain interaction partners. PLOS Comput. Biol., 3(3):e43.
  3. Altschul, S., Gish, W., Miller, Myers, E., and J. Lipman, D. (1990). Basic local alignment search tool. Molecular Biology, 215:403-410.
  4. Benso, A., Di Carlo, S., Ur Rehman, H., Politano, G., Savino, A., and Suravajhala, P. (2012). Using gnome wide data for protein function prediction by exploiting gene ontology relationships. pages 497-502. IEEE International Conference on Automation Quality and Testing Robotics (AQTR)., IEEE.
  5. Benso, A., Di Carlo, S., Ur Rehman, H., Politano, G., Savino, A., and Suravajhala, P. (2013). A combined approach for genome wide protein function annotation/prediction. PROTEOME SCIENCE, 11(S1):1- 12. ISSN: 1477-5956.
  6. Braun, P. and et al. (2009). An experimentally derived confidence score for binary protein-protein interactions. Nature Methods, 6:91 to 97.
  7. Burger, L. and V. Nimwegen, E. (2008). Accurate prediction of protein protein interactions from sequence alignments using a bayesian method. Mol Syst Biol, 4:165.
  8. C. Zhang, Q., Petrey, D., Norel, R., and Honig, B. (2010). Protein interface conservation across structure space. Proc. Natl Acad. Sci. USA, 107:10896-10901.
  9. Deane, C. M., Salwinski, L., Xenarios, I., and Eisenberg, D. (2002). Protein interactions: two methods for assessment of the reliability of high throughput observations. . Mol. Cell. Proteomics, 1:349 to 356.
  10. Espadaler, J., Romero, O., M. Jackson, R., and et al. (2005). Prediction of protein-protein interactions using distant conservation of sequence patterns and structure relationships. Oxford Journals, Volume 21, Issue 16:3360 -3368.
  11. F. Xia, J., Han, K., and S. Huang, D. (2010). Sequencebased prediction of protein-protein interactions by means of rotation forest and autocorrelation descriptor. Protein Pept Lett, 17(1):137-45.
  12. Golovin, A. and Henrick, K. (2008). Msdmotif: exploring protein sites and motifs. BMC Bioinformatics, 9:1-11. Springer-Verlag Berlin Heidelberg.
  13. Ito, T., Chiba, T., Ozawa, R., and et al. (2001). A comprehensive analysis of protein protein interactions in saccharomyces cerevisiae. Proc Natl Acad Sci USA, 98:4569-74.
  14. M. Berman, H., Westbrook, J., Feng, Z., Gilliland, G., N. Bhat, T., Weissig, H., N. Shindyalov, I., and E. Bourne, P. (2000). The protein data bank. Nucleic Acids Research, 28:235-242.
  15. (2013). Mmdb and vast+: tracking structural similarities between macromolecular complexes. Nucleic Acids Res., 42:(D1): D297-D303. [PubMed PMID: 24319143].
  16. Mitrofanova, A., Pavlovic, V., and Mishra, B. (2011). Prediction of protein functions with gene ontology and interspecies protein homology data. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 8 no. 3:775-784.
  17. N. Pelletier, J., Arndt, K., Pluckthun, A., and et al. (1999). An in vivo library versus library selection of optimized protein protein interactions. Nat Biotechnol, 17:683-90.
  18. R. Rhodes, D., A. Tomlins, S., and Varambally, S. (2005). Probabilistic model of the human protein-protein interaction network. Nature Biotechnology, 23:951 - 959.
  19. Rigaut, G., Shevchenko, A., Rutz, B., and et al. (1999). A generic protein purification method for protein complex characterization and proteome exploration. Nat Biotechnol, 17:1030-32.
  20. Salwinski, L. and Eisenberg, D. (2003). Computational methods of analysis of protein protein interactions. Curr. Opin. Struct. Biol., 13:377 to 382.
  21. Schweiger, R., Linial, M., and Linial, N. (2011). Generative probabilistic models for protein-protein interaction network the biclique perspective. Oxford Journals, Volume 27.
  22. Shatsky, M., Nussinov, R., and J. Wolfson, H. (2004). A method for simultaneous alignment of multiple protein structures. PROTEINS: Structure, Function, and Bioinformatics, 56:143-156.
  23. Shen, J., Zhang, J., Luo, X., Zhu, W., Yu, K., and et al. (2006). Predicting protein-protein interactions based only on sequences information. Proceedings of the National Academy of Sciences, vol. 104:4337-4341.
  24. The UniProt Consortium (2015). Uniprot: a hub for protein information. Nucleic Acids Res. 43: D204-D212.
  25. Tuncbag, N., Gursoy, A., Nussinov, R., and Keskin, O. (2011). Predicting protein-protein interactions on a proteome scale by matching evolutionary and structural similarities at interfaces using prism. Nature Protocols, 06 NO.09:1341-1354.
  26. Valencia, A. and Pazos, F. (2003). Prediction of proteinprotein interactions from evolutionary information. Methods Biochem Anal, 44:411-26.
  27. Wass, M., Fuentes, G., Pons, C., Pazos, F., and Valencia, A. (2011). Towards the prediction of protein interaction partners using physical docking. Mol. Syst. Biol., 7:469.
  28. You, Z. H., Chan, K. C. C., and Hu, P. (2015). Predicting protein-protein interactions from primary protein sequences using a novel multi-scale local feature representation scheme and the random forest. PLoS ONE, 10(5).
  29. Zhang, Q. C., Petrey, D., and et al. (2012). Structure based prediction of protein-protein interactions on a genome wide scale. Nature, 490(7421):556 to 60.
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Paper Citation


in Harvard Style

Rehman H., Zafar U., Benso A. and Islam N. (2016). A Structure based Approach for Accurate Prediction of Protein Interactions Networks . In Proceedings of the 9th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 3: BIOINFORMATICS, (BIOSTEC 2016) ISBN 978-989-758-170-0, pages 237-244. DOI: 10.5220/0005705002370244


in Bibtex Style

@conference{bioinformatics16,
author={Hafeez Ur Rehman and Usman Zafar and Alfredo Benso and Naveed Islam},
title={A Structure based Approach for Accurate Prediction of Protein Interactions Networks},
booktitle={Proceedings of the 9th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 3: BIOINFORMATICS, (BIOSTEC 2016)},
year={2016},
pages={237-244},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005705002370244},
isbn={978-989-758-170-0},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 9th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 3: BIOINFORMATICS, (BIOSTEC 2016)
TI - A Structure based Approach for Accurate Prediction of Protein Interactions Networks
SN - 978-989-758-170-0
AU - Rehman H.
AU - Zafar U.
AU - Benso A.
AU - Islam N.
PY - 2016
SP - 237
EP - 244
DO - 10.5220/0005705002370244