Advertisement

Optimizing HP Model Using Reinforcement Learning

  • Ru Yang
  • Hongjie Wu
  • Qiming Fu
  • Tao Ding
  • Cheng Chen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10955)

Abstract

Protein structure prediction has always been an important issue in bioinformatics field. This paper proposes an HP model optimization method based on reinforcement learning, which is a new attempt in the area of protein structure prediction. It does not require external supervision as the agent can find the optimal solution from the reward function in the training process. And the method also decreases computational complexity through making the time complexity of the algorithm has a linear relationship with the length of protein sequence.

Keywords

Reinforcement learning HP model Structure prediction 

Notes

Acknowledgement

This paper is supported by the National Natural Science Foundation of China (61772357, 61502329, 61672371), Jiangsu 333 talent project and top six talent peak project (DZXX-010), Suzhou Foresight Research Project (SYG201704, SNG201610) and the Postgraduate Research & Practice Innovation Program of Jiangsu Province (SJCX17_0680).

References

  1. 1.
    Yan, S.M., Wu, G.: Detailed folding structures of M-lycotoxin-Hc1a and its mutageneses using 2D HP model. Mol. Simul. 38(10), 809–822 (2012)CrossRefGoogle Scholar
  2. 2.
    Pan, J., Wang, X., Cheng, Y., et al.: Multi-source transfer ELM-based Q learning. Neurocomputing 137(11), 57–64 (2014)CrossRefGoogle Scholar
  3. 3.
    Mann, M., Backofen, R.: Exact methods for lattice protein models. Bio-Algorithms Med-Syst. 10(4), 213–225 (2014)Google Scholar
  4. 4.
    Tang, X., Wang, J., Zhong, J., et al.: Predicting essential proteins based on weighted degree centrality. IEEE/ACM Trans. Comput. Biol. Bioinform. 11(2), 407–418 (2014)CrossRefGoogle Scholar
  5. 5.
    Qin, Y.F., Zheng, X.Q., Wang, J., et al.: Prediction of protein structural class based on linear predictive coding of PSI-BLAST profiles. Open Life Sci. 1, 11–15 (2015)Google Scholar
  6. 6.
    Huang, J.T., Wang, T., Huang, S.R., et al.: Prediction of protein folding rates from simplified secondary structure alphabet. J. Theor. Biol. 383, 1–6 (2015)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Electronic and Information EngineeringSuzhou University of Science and TechnologySuzhouChina
  2. 2.Jiangsu Province Key Laboratory of Intelligent Building Energy EfficiencySuzhou University of Science and TechnologySuzhouChina

Personalised recommendations