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KSIBW: Predicting Kinase-Substrate Interactions Based on Bi-random Walk

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Bioinformatics Research and Applications (ISBRA 2018)

Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 10847))

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Abstract

Protein phosphorylation is an important chemical modification in the organism that regulates many cellular processes. In recent years, many algorithms for predicting kinase-substrate interactions have been proposed. However, most of those methods are mainly focused on utilizing protein sequence information. In this paper, we propose a computational framework, KSIBW, to predict kinase-substrate interactions based on bi-random walk. Unlike traditional methods, the protein-protein interaction (PPI) information are used to measure the similarities of kinase-kinase and substrate-substrate, respectively. Then, the bi-random walk is employed to identify potential kinase-substrate interactions. The experiment results show that our method outperforms other state-of-the-art algorithms in performance.

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References

  1. Mann, M., Jensen, O.N.: Proteomic analysis of post-translational modifications. Nat. Biotechnol. 21(3), 255–261 (2003)

    Article  Google Scholar 

  2. Lan, W., Wang, J., Li, M., Peng, W., Wu, F.: Computational approaches for prioritizing candidate disease genes based on PPI networks. Tsinghua Sci. Technol. 20(5), 500–512 (2015)

    Article  MathSciNet  Google Scholar 

  3. Grabiec, A.M., Korchynskyi, O., Tak, P.P., Reedquist, K.A.: Histone deacetylase inhibitors suppress rheumatoid arthritis fibroblast-like synoviocyte and macrophage IL-6 production by accelerating mRNA decay. Ann. Rheum. Dis. 71(3), 424–431 (2012)

    Article  Google Scholar 

  4. Cohen, P.: The role of protein phosphorylation in human health and disease. FEBS J. 268(19), 5001–5010 (2001)

    Google Scholar 

  5. Aponte, A.M., Phillips, D., Harris, R.A., Blinova, K., French, S., Johnson, D.T., Balaban, R.S.: 32P labeling of protein phosphorylation and metabolite association in the mitochondria matrix. Methods Enzymol. 457, 63–80 (2009)

    Article  Google Scholar 

  6. Lin, J., Xie, Z., Zhu, H., Qian, J.: Understanding protein phosphorylation on a systems level. Brief. Funct. Genomics 9(1), 32–42 (2010)

    Article  Google Scholar 

  7. Dinkel, H., Chica, C., Via, A., Gould, C.M., Jensen, L.J., Gibson, T.J., Diella, F.: Phospho.ELM: a database of phosphorylation sites—update 2011. Nucleic Acids Res. 39(Suppl\(\_\)1), D261–D267 (2010)

    Google Scholar 

  8. Hornbeck, P.V., Kornhauser, J.M., Tkachev, S., Zhang, B., Skrzypek, E., Murray, B., Latham, V., Sullivan, M.: PhosphoSitePlus: a comprehensive resource for investigating the structure and function of experimentally determined post-translational modifications in man and mouse. Nucleic Acids Res. 40(D1), D261–D270 (2011)

    Article  Google Scholar 

  9. Chen, Q., Wang, Y., Chen, B., Zhang, C., Wang, L., Li, J.: Using propensity scores to predict the kinases of unannotated phosphopeptides. Knowl.-Based Syst. 135, 60–76 (2017)

    Article  Google Scholar 

  10. Linding, R., Jensen, L.J., Ostheimer, G.J., van Vugt, M.A., Jørgensen, C., Miron, I.M., Diella, F., Colwill, K., Taylor, L., Elder, K., et al.: Systematic discovery of in vivo phosphorylation networks. Cell 129(7), 1415–1426 (2007)

    Article  Google Scholar 

  11. Dang, T.H., Van Leemput, K., Verschoren, A., Laukens, K.: Prediction of kinase-specific phosphorylation sites using conditional random fields. Bioinformatics 24(24), 2857–2864 (2008)

    Article  Google Scholar 

  12. Zhou, F.F., Xue, Y., Chen, G.L., Yao, X.: GPS: a novel group-based phosphorylation predicting and scoring method. Biochem. Biophys. Res. Commun. 325(4), 1443–1448 (2004)

    Article  Google Scholar 

  13. Song, C., Ye, M., Liu, Z., Cheng, H., Jiang, X., Han, G., Songyang, Z., Tan, Y., Wang, H., Ren, J., et al.: Systematic analysis of protein phosphorylation networks from phosphoproteomic data. Mol. Cell. Proteomics 11(10), 1070–1083 (2012)

    Article  Google Scholar 

  14. Zou, L., Wang, M., Shen, Y., Liao, J., Li, A., Wang, M.: PKIS: computational identification of protein kinases for experimentally discovered protein phosphorylation sites. BMC Bioinform. 14(1), 247 (2013)

    Article  Google Scholar 

  15. Patrick, R., Lê Cao, K.A., Kobe, B., Bodén, M.: PhosphoPICK: modelling cellular context to map kinase-substrate phosphorylation events. Bioinformatics 31(3), 382–389 (2014)

    Article  Google Scholar 

  16. Fan, W., Xu, X., Shen, Y., Feng, H., Li, A., Wang, M.: Prediction of protein kinase-specific phosphorylation sites in hierarchical structure using functional information and random forest. Amino Acids 46(4), 1069–1078 (2014)

    Article  Google Scholar 

  17. Damle, N.P., Mohanty, D.: Deciphering kinase-substrate relationships by analysis of domain-specific phosphorylation network. Bioinformatics 30(12), 1730–1738 (2014)

    Article  Google Scholar 

  18. Li, A., Xu, X., Zhang, H., Wang, M.: Kinase identification with supervised Laplacian regularized least squares. PLoS ONE 10(10), e0139676 (2015)

    Article  Google Scholar 

  19. Song, J., Wang, H., Wang, J., Leier, A., Marquez-Lago, T., Yang, B., Zhang, Z., Akutsu, T., Webb, G.I., Daly, R.J.: PhosphoPredict: a bioinformatics tool for prediction of human kinase-specific phosphorylation substrates and sites by integrating heterogeneous feature selection. Sci. Rep. 7(1), 6862 (2017)

    Article  Google Scholar 

  20. Li, H., Wang, M., Xu, X.: Prediction of kinase-substrate relations based on heterogeneous networks. J. Bioinform. Comput. Biol. 13(06), 1542003 (2015)

    Article  Google Scholar 

  21. Qin, G.M., Li, R.Y., Zhao, X.M.: PhosD: inferring kinase-substrate interactions based on protein domains. Bioinformatics 33(8), 1197–1204 (2016)

    Google Scholar 

  22. Lan, W., Wang, J., Li, M., Liu, J., Wu, F.X., Pan, Y.: Predicting microRNA-disease associations based on improved microRNA and disease similarities. IEEE/ACM Trans. Comput. Biol. Bioinform. 99, 1 (2016)

    Google Scholar 

  23. Liu, J., Li, M., Lan, W., Wu, F.X., Pan, Y., Wang, J.: Classification of Alzheimer’s disease using whole brain hierarchical network. IEEE/ACM Trans. Comput. Biol. Bioinform. 15(2), 624–632 (2018)

    Article  Google Scholar 

  24. Lan, W., Wang, J., Li, M., Liu, J., Pan, Y.: Predicting microRNA-disease associations by integrating multiple biological information. In: 2015 IEEE International Conference on Bioinformatics and Biomedicine, BIBM, pp. 183–188. IEEE (2015)

    Google Scholar 

  25. Li, T., Wernersson, R., Hansen, R.B., Horn, H., Mercer, J., Slodkowicz, G., Workman, C.T., Rigina, O., Rapacki, K., Stærfeldt, H.H., et al.: A scored human protein-protein interaction network to catalyze genomic interpretation. Nat. Methods 14(1), 61–64 (2017)

    Article  Google Scholar 

  26. Luo, H., Wang, J., Li, M., Luo, J., Peng, X., Wu, F.X., Pan, Y.: Drug repositioning based on comprehensive similarity measures and Bi-Random walk algorithm. Bioinformatics 32(17), 2664 (2016)

    Article  Google Scholar 

  27. Peng, W., Lan, W., Yu, Z., Wang, J., Pan, Y.: A framework for integrating multiple biological networks to predict microRNA-disease associations. IEEE Trans. Nanobiosci. 16(2), 100–107 (2017)

    Article  Google Scholar 

  28. Peng, W., Lan, W., Zhong, J., Wang, J., Pan, Y.: A novel method of predicting microRNA-disease associations based on microRNA, disease, gene and environment factor networks. Methods 124, 69–77 (2017)

    Article  Google Scholar 

  29. Irish, J.M., Ånensen, N., Hovland, R., Skavland, J., Børresen-Dale, A.L., Bruserud, Ø., Nolan, G.P., Gjertsen, B.T.: Flt3 Y591 duplication and Bcl-2 overexpression are detected in acute myeloid leukemia cells with high levels of phosphorylated wild-type p53. Blood 109(6), 2589–2596 (2007)

    Article  Google Scholar 

  30. Ball, H.L., Myers, J.S., Cortez, D.: ATRIP binding to replication protein A-single-stranded DNA promotes ATR-ATRIP localization but is dispensable for Chk1 phosphorylation. Mol. Biol. Cell 16(5), 2372–2381 (2005)

    Article  Google Scholar 

  31. Ando, K., Ozaki, T., Yamamoto, H., Furuya, K., Hosoda, M., Hayashi, S., Fukuzawa, M., Nakagawara, A.: Polo-like kinase 1 (Plk1) inhibits p53 function by physical interaction and phosphorylation. J. Biol. Chem. 279(24), 25549–25561 (2004)

    Article  Google Scholar 

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Acknowledgement

This work is supported in part by the National Natural Science Foundation of China under Grant No. 61702122, 61751314, 31560317, 61702555, 61662028 and 61762087; Key project of Natural Science Foundation of Guangxi 2017GXNSFDA198033; Key research and development plan of Guangxi AB17195055 and Director Open Fund of Qinzhou City Key Laboratory of Advanced Technology of Internet of Things IOT2017A04.

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Authors and Affiliations

  1. School of Computer, Electronics and Information, Guangxi University, Nanning, People’s Republic of China

    Canshang Deng, Qingfeng Chen & Wei Lan

  2. School of Information Science and Engineering, Central South University, Changsha, People’s Republic of China

    Ruiqing Zheng, Jin Liu & Jianxin Wang

  3. School of Electronic and Information Engineering, Qinzhou University, Qingzhou, People’s Republic of China

    Zhixian Liu

  4. State Key Laboratory for Conservation and Utilization of Subtropical Agro-Bioresources, Guangxi University, Nanning, People’s Republic of China

    Qingfeng Chen & Zhixian Liu

Authors
  1. Canshang Deng
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  2. Qingfeng Chen
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  3. Zhixian Liu
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  4. Ruiqing Zheng
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  5. Jin Liu
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  6. Jianxin Wang
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  7. Wei Lan
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Corresponding author

Correspondence to Wei Lan .

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Editors and Affiliations

  1. Chinese Academy of Sciences, Beijing, China

    Fa Zhang

  2. Georgia State University, Atlanta, GA, USA

    Zhipeng Cai

  3. Georgia State University, Atlanta, GA, USA

    Pavel Skums

  4. Chinese Academy of Sciences, Beijing, China

    Shihua Zhang

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Deng, C. et al. (2018). KSIBW: Predicting Kinase-Substrate Interactions Based on Bi-random Walk. In: Zhang, F., Cai, Z., Skums, P., Zhang, S. (eds) Bioinformatics Research and Applications. ISBRA 2018. Lecture Notes in Computer Science(), vol 10847. Springer, Cham. https://doi.org/10.1007/978-3-319-94968-0_13

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  • DOI: https://doi.org/10.1007/978-3-319-94968-0_13

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-94967-3

  • Online ISBN: 978-3-319-94968-0

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