A Study of the Protein Folding Problem by a Simulation Model

  • Omar Gaci
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 68)


In this paper, we propose a simulation model to study the protein folding problem. We describe the main properties of proteins and describe the protein folding problem according to the existing approaches. Then, we propose to simulate the folding process when a protein is represented by an amino acid interaction network. This is a graph whose vertices are the proteins amino acids and whose edges are the interactions between them. We propose a genetic algorithm of reconstructing the graph of interactions between secondary structure elements which describe the structural motifs. The performance of our algorithms is validated experimentally.


Genetic Algorithm Interaction Network Protein Data Bank Adjacency Matrix Secondary Structure Element 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    A.R. Atilgan, P. Akan, C. Baysal. Small-world communication of residues and significance for protein dynamics. Biophys. J. 86(1 Pt 1), 85–91 (2004)CrossRefGoogle Scholar
  2. 2.
    K.A. Dill, S. Bromberg, K.Z. Yue, K.M. Fiebig, D.P. Yee, P.D. Thomas, H.S. Chan, Principles of protein folding: a perspective from simple exact models. Protein Sci. 4(4), 561–602 (1995)CrossRefGoogle Scholar
  3. 3.
    C.B. Anfinsen, Principles that govern the folding of protein chains. Science 181, 223–230 (1973)CrossRefGoogle Scholar
  4. 4.
    C. Levinthal. Are there pathways for protein folding? J. Chim. Phys. 65, 44–45 (1968)Google Scholar
  5. 5.
    H.M. Berman, J. Westbrook, Z. Feng, G. Gilliland, T.N. Bhat, H. Weissig, P.E. Bourne, I.N. Shindyalov, The protein data bank. Nucleic Acids Res. 28, 235–242 (2000)CrossRefGoogle Scholar
  6. 6.
    M. Kataoka, Y. Goto, X-ray solution scattering studies of protein folding. Folding Des. 1, 107–114 (1996)CrossRefGoogle Scholar
  7. 7.
    K.W. Plaxco, C.M. Dobson, Time-relaxed biophysical methods in the study of protein folding. Curr. Opin. Struct. Biol. 6, 630–636 (1996)CrossRefGoogle Scholar
  8. 8.
    A. Bairoch, R. Apweiler, The swiss-prot protein sequence database and its supplement trembl. Nucleic Acids Res. 28, 45–48 (2000)CrossRefGoogle Scholar
  9. 9.
    R.L. Baldwin, Why is protein folding so fast? Proc. Natl. Acad. Sci. USA 93, 2627–2628 (1996)CrossRefGoogle Scholar
  10. 10.
    A. Broder, R. Kumar, F. Maghoul, P. Raghavan, S. Rajagopalan, R. Stata, A. Tomkins, J. Wiener. Graph structure in the Web. Comput. Netw. 33(1–6), 309–320, (2000)CrossRefGoogle Scholar
  11. 11.
    S. Wasserman, K. Faust, Social network analysis: methods and applications. Structural Analysis in the Social Sciences, vol. 8 (Cambridge University Press, Cambridge, 1994)Google Scholar
  12. 12.
    H. Jeong, B. Tombor, R. Albert, Z.N. Oltvai, A.-L. Barabàsi, The large-scale organization of metabolic networks. Nature 407(6804), 651–654 (2000)CrossRefGoogle Scholar
  13. 13.
    N.V. Dokholyan, L. Li, F. Ding, E.I. Shakhnovich, Topological determinants of protein folding. Proc. Natl. Acad. Sci. USA 99(13), 8637–8641 (2002)CrossRefGoogle Scholar
  14. 14.
    A. Ghosh, K.V. Brinda, S. Vishveshwara, Dynamics of lysozyme structure network: probing the process of unfolding. Biophys. J. 92(7), 2523–2535, (2007)CrossRefGoogle Scholar
  15. 15.
    U.K. Muppirala, Z. Li, A simple approach for protein structure discrimination based on the network pattern of conserved hydrophobic residues. Protein Eng. Des. Sel 19(6), 265–275 (2006)CrossRefGoogle Scholar
  16. 16.
    A. Dutot, F. Guinand, D. Olivier, Y. Pigné, GraphStream: A Tool for bridging the gap between Complex Systems and Dynamic Graphs, Proc of EPNACS: Emergent Properties in Natural and Artificial Complex Systems, Dresden, Germany, 137–143 (2007)Google Scholar
  17. 17.
    J.H. Holland, Adaptation in Natural and Artificial System (MIT Press, Cambridge, MA, 1992)Google Scholar
  18. 18.
    O. Gaci, Building a parallel between structural and topological properties. In Advances in Computational Biology (Springer, 2010)Google Scholar
  19. 19.
    O. Gaci, Building a topological inference exploiting qualitative criteria. Evol. Boinformatics (2010)Google Scholar

Copyright information

© Springer Science+Business Media B.V. 2010

Authors and Affiliations

  1. 1.Le Havre UniversityLe HavreFrance

Personalised recommendations