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Predict Two-Dimensional Protein Folding Based on Hydrophobic-Polar Lattice Model and Chaotic Clonal Genetic Algorithm

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Intelligent Data Engineering and Automated Learning – IDEAL 2016 (IDEAL 2016)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9937))

Abstract

In order to improve the performance of prediction of protein folding problem, we introduce a relatively new chaotic clonal genetic algorithm (abbreviated as CCGA) to solve the 2D hydrophobic-polar lattice model. Our algorithm combines three successful components—(i) standard genetic algorithm (SGA), (ii) clonal selection algorithm (CSA), and (iii) chaotic operator. We compared this proposed CCGA with SGA, artificial immune system (AIS), and immune genetic algorithm (IGA) for various chain lengths. It demonstrated that CCGA had better performance than other methods over large-sized protein chains.

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Acknowledgment

This paper was supported by Natural Science Foundation of Jiangsu Province (BK20150983), Program of Natural Science Research of Jiangsu Higher Education Institutions (15KJB470010), Nanjing Normal University Research Foundation for Talented Scholars (2013119XGQ0061, 2014119XGQ0080), Open Project Program of the State Key Lab of CAD&CG, Zhejiang University (A1616).

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Authors and Affiliations

  1. School of Computer Science and Technology, Nanjing Normal University, Nanjing, 210023, Jiangsu, China

    Shuihua Wang, Yuankai Huo, Xueyan Wu, Hainan Wang & Yudong Zhang

  2. School of Information Science and Engineering, Southeast University, Nanjing, China

    Lenan Wu

Authors
  1. Shuihua Wang
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  2. Lenan Wu
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  3. Yuankai Huo
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  4. Xueyan Wu
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  5. Hainan Wang
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  6. Yudong Zhang
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Corresponding author

Correspondence to Yudong Zhang .

Editor information

Editors and Affiliations

  1. University of Manchester, Manchester, United Kingdom

    Hujun Yin

  2. Nanjing University, Nanjing, China

    Yang Gao

  3. Yangzhou University, Yangzhou, Jiangsu, China

    Bin Li

  4. Aeronautics and Astronautics, Nanjing University Aeronautics and Astronautics, Nanjing, China

    Daoqiang Zhang

  5. Nanjing Normal University, Nanjing, China

    Ming Yang

  6. Yangzhou University, Yangzhou, Jiangsu, China

    Yun Li

  7. Ostfalia University of Applied Sciences, Wolfenbüttel, Germany

    Frank Klawonn

  8. University of Seville, Seville, Spain

    Antonio J. Tallón-Ballesteros

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Wang, S., Wu, L., Huo, Y., Wu, X., Wang, H., Zhang, Y. (2016). Predict Two-Dimensional Protein Folding Based on Hydrophobic-Polar Lattice Model and Chaotic Clonal Genetic Algorithm. In: Yin, H., et al. Intelligent Data Engineering and Automated Learning – IDEAL 2016. IDEAL 2016. Lecture Notes in Computer Science(), vol 9937. Springer, Cham. https://doi.org/10.1007/978-3-319-46257-8_2

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  • DOI: https://doi.org/10.1007/978-3-319-46257-8_2

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-46256-1

  • Online ISBN: 978-3-319-46257-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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