Prediction of Phosphorylation Sites Using PSO-ANNs

  • Ruizhi Han
  • Dong WangEmail author
  • Yuehui ChenEmail author
  • Wenzheng Bao
  • Qianqian Zhang
  • Hanhan Cong
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9771)


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.


Phosphorylation sites prediction Particle swarm optimization (PSO) Artificial neural network 



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).


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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.School of Information Science and EngineeringUniversity of JinanJinanChina
  2. 2.Shandong Provincial Key Laboratory of Network Based Intelligent ComputingJinanChina
  3. 3.College of Electronics and Information EngineeringTongji UniversityShanghaiChina
  4. 4.University of JinanJinanChina

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