Abstract
One of the most important types of posttranslational modification is phosphorylation which helps in the regulation of almost all activities of the cell. Phosphorylation is the process of addition of a phosphate group to a protein after the process of translation. In this paper, we have used evolutionary information extracted from position-specific scoring matrices (PSSM) to serve as features for prediction. Support vector machine (SVM) was used the machine learning tool. The system was tested with an independent set of 141 proteins for which our system achieved the highest AUC score of 0.7327. Additionally, our system attained best results for 34 proteins in terms of AUC.
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References
Altschul, S.F., Madden, T.L., Schäffer, A.A., Zhang, J., Zhang, Z., Miller, W., Lipman, D.J.: Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res. 25(17), 3389–3402 (1997)
Banerjee, S., Basu, S., Nasipuri, M.: Big data analytics and its prospects in computational proteomics. In: Information Systems Design and Intelligent Applications, pp. 591–598. Springer (2015)
Basu, S., Plewczynski, D.: AMS 3.0: prediction of post-translational modifications. BMC Bioinformatics 11(1), 210 (2010)
Biswas, A.K., Noman, N., Sikder, A.R.: Machine learning approach to predict protein phosphorylation sites by incorporating evolutionary information. BMC Bioinformatics 11(1), 273 (2010)
Blom, N., Gammeltoft, S., Brunak, S.: Sequence and structure-based prediction of eukaryotic protein phosphorylation sites. J. Mol. Biol. 294, 1351–1362 (1999). doi:10.1006/jmbi.1999.3310
Dou, Y., Yao, B., Zhang, C.: PhosphoSVM: prediction of phosphorylation sites by integrating various protein sequence attributes with a support vector machine. Amino Acids 46(6), 1459–1469 (2014)
Gao, J., Thelen, J.J., Dunker, A.K., Xu, D.: Musite, a tool for global prediction of general and kinase-specific phosphorylation sites. Mol. Cell. Proteomics 9(12), 2586–2600 (2010)
Hjerrild, M., Stensballe, A., Rasmussen, T.E., Kofoed, C.B., Blom, N., Sicheritz-Ponten, T., Larsen, M.R., Brunak, S., Jensen, O.N., Ganuneltoft, S.: (2004). Identification of phosphorylation sites in protein kinase A substrates using artificial neural networks and mass spectrometry. J. Proteome Res. 3, 426–433. doi:10.1021/pr0341033
Huang, Y., Niu, B., Gao, Y., Fu, L., Li, W.: CD-HIT Suite: a web server for clustering and comparing biological sequences. Bioinformatics 26(5), 680–682 (2010)
Plewczynski, D., Basu, S., Saha, I.: AMS 4.0: consensus prediction of post-translational modifications in protein sequences. Amino Acids, 43(2), 573–582 (2012)
Trost, B., Kusalik, A.: Computational phosphorylation site prediction in plants using random forests and organism-specific instance weights. Bioinformatics, btt031 (2013)
Xue, Y., Gao, X., Cao, J., Liu, Z., Jin, C., Wen, L., Yao, X., Ren, J.: A summary of computational resources for protein phosphorylation. Curr. Protein Pept. Sci. 11(6), 485–496 (2010)
Xue, Y., Liu, Z., Cao, J., Ma, Q., Gao, X., Wang, Q., Jin, C., Zhou, Y., Wen, L., Ren, J.: GPS 2.1: enhanced prediction of kinase-specific phosphorylation sites with an algorithm of motif length selection. Protein Eng. Des. Sel. 24(3), 255–260 (2011)
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Authors are indebted to CMATER, department of computer science and Engineering, Jadavpur University for providing the necessary support for carrying out this experiment.
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Sagnik Banerjee, Debjyoti Ghosh, Subhadip Basu, Mita Nasipuri (2016). JUPred_SVM: Prediction of Phosphorylation Sites Using a Consensus of SVM Classifiers. In: Pant, M., Deep, K., Bansal, J., Nagar, A., Das, K. (eds) Proceedings of Fifth International Conference on Soft Computing for Problem Solving. Advances in Intelligent Systems and Computing, vol 436. Springer, Singapore. https://doi.org/10.1007/978-981-10-0448-3_45
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DOI: https://doi.org/10.1007/978-981-10-0448-3_45
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