Skip to main content
SpringerLink
Log in

Protein Structure Prediction in Lattice Models with Particle Swarm Optimization

  • Conference paper
Book cover Swarm Intelligence (ANTS 2010)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 6234))

Included in the following conference series:

Abstract

The protein structure prediction problem consists in finding good computational algorithms for prediction of protein native states. This paper applies the Particle Swarm Optimization (PSO) algorithm to predict the tertiary structure of proteins in lattice models. We propose a novel discrete PSO variant designed for lattice-based protein folding models. We present three lattice based models and two folding encodings, which are tested in different combinations on six proteins. The results indicate that the new algorithm performs very efficient and finds very good proteins conformations.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Băutu, A., Băutu, E.: Particle Swarms in Statistical Physics, pp. 77–88. Intech Publishing (2009)

    Google Scholar 

  2. Băutu, A., Luchian, H.: Protein structure prediction in the 2D HP model using Particle Swarm Optimization. In: Proc. of 7th Int. Conf. on Numerical Methods and Applications (to appear)

    Google Scholar 

  3. Benítez, C.M.V., Lopes, H.S.: A parallel genetic algorithm for protein folding prediction using the 3D-HP side chain model. In: Proc. of CEC 2009, pp. 1297–1304. IEEE Press, Piscataway (2009)

    Google Scholar 

  4. Bennett, A.J., Johnston, R.L., Turpin, E., He, J.Q.: Analysis of an immune algorithm for protein structure prediction. Informatica (2008)

    Google Scholar 

  5. Berenboym, I., Avigal, M.: Genetic algorithms with local search optimization for protein structure prediction problem. In: Proc. of the 10th annual Conf. on Genetic and evolutionary computation, pp. 1097–1098. ACM, New York (2008)

    Chapter  Google Scholar 

  6. Berger, B., Leighton, T.: Protein folding in the hydrophobic-hydrophilic (HP) is NP-complete. In: Proc. of RECOMB 1998, pp. 30–39. ACM, New York (1998)

    Chapter  Google Scholar 

  7. van den Bergh, F., Engelbrecht, A.: A study of particle swarm optimization particle trajectories. Information Sciences 176(8), 937–971 (2006)

    Article  MATH  MathSciNet  Google Scholar 

  8. Clerc, M.: Particle Swarm Optimization. Hermes Science, London (2006)

    Book  MATH  Google Scholar 

  9. Dill, K.A.: Dominant forces in protein folding. Biochemistry 29(31), 7133–7155 (1990)

    Article  Google Scholar 

  10. Dill, K.A., Ozkan, S.B., Shell, M.S., Weikl, T.R.: The protein folding problem. Annual Review of Biophysics 37(1), 289–316 (2008)

    Article  Google Scholar 

  11. Dill, K.A., Ozkan, S.B., Weikl, T.R., Chodera, J.D., Voelz, V.A.: The protein folding problem: when will it be solved? Current Opinion in Structural Biology 17(3), 342–346 (2007)

    Article  Google Scholar 

  12. Eberhart, R., Kennedy, J.: A new optimizer using particle swarm theory. In: Proc. of the Sixth Int. Symp. on Micro Machine and Human Science, pp. 39–43 (1995)

    Google Scholar 

  13. Kapsokalivas, L., Gan, X., Albrecht, A., Steinhöfel, K.: Population-based local search for protein folding simulation in the MJ energy model and cubic lattices. Computational Biology and Chemistry 33(4), 283–294 (2009)

    Article  Google Scholar 

  14. Kennedy, J., Eberhart, R.C.: A discrete binary version of the particle swarm algorithm. In: Proc. of the World Multiconf. on Systemics, Cybernetics and Informatics, vol. 5, pp. 4104–4109. IEEE Press, Piscataway (1997)

    Google Scholar 

  15. Krasnogor, N., Hart, W.E., Smith, J., Pelta, D.A.: Protein structure prediction with evolutionary algorithms. In: Proc. of the Genetic and Evo. Comp. Conf., vol. 2, pp. 1596–1601. Morgan Kaufmann, Orlando (1999)

    Google Scholar 

  16. Lin, C.J., Hsieh, M.H.: An efficient hybrid Taguchi-genetic algorithm for protein folding simulation. Expert Systems with Applications 36(10), 12446–12453 (2009)

    Article  Google Scholar 

  17. Liu, J., Wang, L., He, L., Shi, F.: Analysis of Toy Model for Protein Folding Based on PSO Algorithm. In: Wang, L., Chen, K., S. Ong, Y. (eds.) ICNC 2005. LNCS, vol. 3612, pp. 636–645. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  18. Pérez-Hernández, L.G., Rodríguez-Vázquez, K., Garduno-Juárez, R.: Parallel PSO applied to the protein folding problem. In: Proc. of the 11th Annual Conf. on Genetic and evolutionary computation, pp. 1791–1792. ACM, New York (2009)

    Google Scholar 

  19. Poli, R., Kennedy, J., Blackwell, T.: Particle swarm optimization. Swarm Intelligence 1, 33–57 (2007)

    Article  Google Scholar 

  20. Santana, R., Larranaga, P., Lozano, J.A.: Protein folding in simplified models with EDA. IEEE Transactions on Evolutionary Computation 12(4), 418–438 (2008)

    Article  Google Scholar 

  21. Thachuk, C., Shmygelska, A., Hoos, H.: A replica exchange Monte Carlo algorithm for protein folding in the HP model. BMC Bioinformatics 2008(1), 342 (2007)

    Article  Google Scholar 

  22. Zhu, H., Pu, C., Lin, X., Gu, J., Zhang, S., Su, M.: Protein structure prediction with EPSO in Toy model. In: Proc. of ICINIS 2009, pp. 673–676. IEEE Computer Society, Washington (2009)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Faculty of Computer Science, “Al. I. Cuza” University, Iasi, Romania

    Andrei Băutu & Henri Luchian

Authors
  1. Andrei Băutu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Henri Luchian
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. IRIDIA, CoDE, Université Libre de Bruxelles, Brussels, Belgium

    Marco Dorigo

  2. IRIDIA, CoDE, Université Libre de Bruxelles, Av. F. Roosevelt 50, CP 194/6, 1050, Brussels, Belgium

    Mauro Birattari

  3. ’Dalla Molle’ Institute for Artificial Intelligence (IDSIA), Galleria 2, 6928, Manno-Lugano, Switzerland

    Gianni A. Di Caro

  4. Complex Systems Institute, Paris-Ile-de-France, CREA, Ecole Polytechnique & CNRS, 57-59, rue Lhomond, 75005, Paris, France

    René Doursat

  5. Department of Computer Science, University of Pretoria, 0002, P.O. Box, Pretoria, South Africa

    Andries P. Engelbrecht

  6. EPFL, Laboratory of Intelligent Systems, Lausanne, Switzerland

    Dario Floreano

  7. Istituto Dalle Molle di Studi sull’Intelligenza Artificiale (IDSIA), Lugano, Switzerland

    Luca Maria Gambardella

  8. Department of Automatic Control and Systems Engineering, The University of Sheffield, Mappin Street, S1 3JD, Sheffield, UK

    Roderich Groß

  9. KOVAN Research Lab., Department of Computer Engineering, Middle East Technical University, Inonu Bulvari, 06531, Ankara, Turkey

    Erol Şahin

  10. Department of Bioengineering, Binghamton University, State University of New York, 13902-6000, Binghamton NY, USA

    Hiroki Sayama

  11. IRIDIA, CoDE, Université Libre de Bruxelles, Avenue F. Roosevelt 50, CP 194/6, 1050, Brussels, Belgium

    Thomas Stützle

Rights and permissions

Reprints and permissions

Copyright information

© 2010 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Băutu, A., Luchian, H. (2010). Protein Structure Prediction in Lattice Models with Particle Swarm Optimization. In: Dorigo, M., et al. Swarm Intelligence. ANTS 2010. Lecture Notes in Computer Science, vol 6234. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15461-4_51

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-15461-4_51

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-15460-7

  • Online ISBN: 978-3-642-15461-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation