Abstract
Post-translational modifications (PTMs) are essential for regulating conformational changes, activities and functions of proteins, and are involved in almost all cellular pathways and processes. Phosphorylation is one of the most important post-translational modifications of proteins, which is related to many activities of life. It can regulate signal transduction, gene expression and cell cycle regulation of many cellular processes by protein phosphorylation and dephosphorylation. With the development and application of proteomics technology, researchers pay close attention on protein phosphorylation research more and more widely. In this paper, we use PSO algorithm to optimize neural network weight coefficients and classify the data which has secondary encoding according to the physical and chemical properties of amino acids for feature extraction. The experimental results compared with the result of the support vector machine (SVM) and experimental results show that the prediction accuracy of PSO-ANNs 2.44 % higher than that of SVM. And this paper at the same time, this paper also analyzes the experimental results under different window values. The results of the experiment are best when the window value is 11.
Keywords
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Xue, Y., Liu, Z.X., Cao, J., Ren, J.: Computational prediction of post-translational modification sites in proteins. Syst. Comput. Biol.-Mol. Cell. Exp. Syst. 5772(6), 18559 (2011)
Huang, Z.Y., Yu, Y.L., Fang, C.Y., Yang, F.Y.: Progress in identification of protein phosphorylation by mass spectrometry. J. Chin. Mass Spectrom. Soc. 24(4), 490–500 (2003)
Kim, J.H., Lee, J., Oh, B., Kimm, K., Koh, I.: Prediction of phosphorylation sites using SVMs. Bioinformatics 20(17), 3179–3184 (2004)
Li, A., Wang, L.R., Shi, Y.Z., Wang, M.H., Jiang, Z.H., Feng, H.Q.: Phosphorylation site prediction with a modified k-nearest neighbor algorithm and BLOSUM62 matrix. Conf. Proc. IEEE Eng. Med. Biol. Soc. 6, 6075–6078 (2005)
Wu, Z., Lu, M., Li, T.T.: Prediction of substrate sites for protein phosphatases 1B, SHP-1, and SHP-2 based on sequence features. Amino Acids 46(8), 1919–1928 (2014)
Tang, Y.R., Chen, Y.Z., Canchaya, C.A., Zhang, Z.D.: GANNPhos: a new phosphorylation site predictor based on a genetic algorithm integrated neural network. Protein Eng. Des. Sel. 20(8), 405–412 (2007)
Fan, S.C., Zhang, X.G.: Characterizing the microenvironment surrounding phosphorylated protein sites. Genomics Proteomics Bioinf. 3, 213–217 (2005)
Wang, J.Y., Zhu, S.G., Xu, C.F.: Biochemistry, 3rd edn. Higher Education Press, Peking (2002)
Zhang, Z.H., Wang, Z.H., Zhang, Z.R., Wang, Y.X.: A novel method for apoptosis protein subcellular localization prediction combining encoding based on grouped weight and support vector machine. FEBS Lett. 580, 6169–6174 (2006)
Nanni, L., Lumini, A.: An ensemble of reduced alphabets with protein encoding based on grouped weight for predicting DNA-binding proteins. Amino Acids 36, 167–175 (2009)
Li, H., Xie, L.: Biological information method for prediction and identification of protein translation modification. Prog. Mod. Biomed. 8, 1729–1735 (2008)
Trost, B., Kusalik, A.: Computational prediction of eukaryotic phosphorylation sites. Bioinformatics 27, 2927–2935 (2011)
Huang, S.Y., Shi, S.P., Qiu, J.D., Liu, M.C.: Using support vector machines to identify protein phosphorylation sites in viruses. J. Mol. Graph. Model. 56, 84–90 (2015)
Liu, Q.F.: Protein sequence coding and function prediction. Hunan University, May 2011
Hornbeck, P.V., et al.: PhosphoSitePlus, 2014: mutations, PTMs and recalibrations. Nucleic Acidc Res. 43, D512–D520 (2015)
Acknowledgements
This research was partially supported by Program for Scientific research innovation team in Colleges and universities of Shandong Province 2012–2015, the Key Project of Natural Science Foundation of Shandong Province (ZR2011FZ001), the Natural Science Foundation of Shandong Province (ZR2011FL022, ZR2013FL002), the Youth Science and Technology Star Program of Jinan City (201406003), Shandong Provincial Natural Science Foundation, China, under Grant ZR2015JL025 and the Shandong Provincial Key Laboratory of Network Based Intelligent Computing. This work was also supported by the National Natural Science Foundation of China (Grant No. 61302128). The scientific research foundation of University of Jinan (XKY1410, XKY1411).
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Han, R., Wang, D., Chen, Y., Bao, W., Zhang, Q., Cong, H. (2016). Prediction of Phosphorylation Sites Using PSO-ANNs. In: Huang, DS., Bevilacqua, V., Premaratne, P. (eds) Intelligent Computing Theories and Application. ICIC 2016. Lecture Notes in Computer Science(), vol 9771. Springer, Cham. https://doi.org/10.1007/978-3-319-42291-6_34
Download citation
DOI: https://doi.org/10.1007/978-3-319-42291-6_34
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-42290-9
Online ISBN: 978-3-319-42291-6
eBook Packages: Computer ScienceComputer Science (R0)