Rank Based Ant Algorithm for 2D-HP Protein Folding

  • N. Thilagavathi
  • T. Amudha
Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 33)


Bio-inspired computing is the field of studying emergence and social behaviors of social insects. Bio-inspired algorithms are a branch of nature inspired algorithms which are termed as swarm intelligence, focused on insect behavior in order to develop some meta-heuristics which can mimic insect’s problem solving abilities. In this Paper Rank Based Ant Colony Optimization (RBACO) was applied to solve 2D-HP protein folding problem. The objective is minimizing the energy to obtain the best 2D structure of protein. The RBACO is one of the bio-inspired algorithms, which could obtain better solutions in 2D-HP protein folding process when compared with other algorithms. The problem instances were taken from the HP benchmarks. The 2D-HP lattice model was used for implementation with HP benchmark instances for 2D-HP protein folding.


Bio-inspired algorithms 2D-HP protein folding Rank based ACO Misfolding of proteins 


  1. 1.
    Alazzam, A., Lewis, H.W. III: A new optimization algorithm for combinatorial problems. (IJARAI) Int. J. Adv. Res. Artif. Intell. 2(5) (2013)Google Scholar
  2. 2.
    Soto, C.: Protein misfolding and disease; protein refolding and therapy. Elsevier Sci. 498, 204–207 (2001)Google Scholar
  3. 3.
    Liang, F., Wong, W.H.: Evolutionary Monte Carlo for protein folding simulations. J. Chem. Phy. 115(7), 3374–3380 (2001)CrossRefGoogle Scholar
  4. 4.
    Forbes, N.: Biologically inspired computing. Comput. Sci. Eng. 2(6), 83–87 (2000)CrossRefGoogle Scholar
  5. 5.
    Chakravarthy, H., Proch, P.B., Rajan, R., Chandrasekharan, K.: Bio inspired approach as a problem solving technique. Netw. Complex Syst. 2(2) (2012)Google Scholar
  6. 6.
    Yue, K., Fiebig, K.M., Thomast, P.D., Chan, H.S., Shakhnovicht, E.I., Dill, K.A.: A test of lattice protein folding algorithms. Pharm. Chem. 92, 325–329 (1995)Google Scholar
  7. 7.
    Lau, K., Dill, K.A.: A lattice statistical mechanics model of the conformation and sequence spaces of proteins. Macromolecules 22, 3986–3997 (1989)CrossRefGoogle Scholar
  8. 8.
    Bianchi, L., Dorigo, M., Gambardella, L.M., Gutjahr, W.J.: A survey on metaheuristics for stochastic combinatorial optimization. Nat. Comput. 8, 239–287 (2009)CrossRefMathSciNetMATHGoogle Scholar
  9. 9.
    Toma, L., Toma, S.: Contact interactions method: a new algorithm for protein folding simulations. Protein Sci. 5, 147–153 (1996)CrossRefGoogle Scholar
  10. 10.
    Milostan, M., Lukasiak, P., Dill, K.A., Blazewicz J.: A tabu search strategy for finding low energy structures of proteins in HP-model. In: Proceedings of the Annual Internatinal Conference on Computational Molecular Biology. Berlin. Poster No. 5-108. (2003)Google Scholar
  11. 11.
    Chen, M., Huang, W.Q.: A branch and bound algorithm for the protein folding problem in the HP lattice model. Geno. Prot. Bioinfo. 3(4) (2005)Google Scholar
  12. 12.
    Unger, R., Moult, J.: Genetic algorithms for protein folding simulations. J. Mol. Biol. 231, 75–81 (1993)CrossRefGoogle Scholar
  13. 13.
    Shmygelska, A., Hoos, H.H.: An improved ant colony optimization algorithm for the 2D HP protein folding problem. In: Proceedings of the 16th Canadian Conference on Artificial Intelligence. LNCS 2671. Springer (2003) 400–417Google Scholar
  14. 14.
    Fidanova, S., Lirkov, I.: Ant colony system approach for protein folding. In: Proceedings of the International Multiconference on Computer Science and Information Technology. (2008) pp. 887–891, ISSN:1896-7094Google Scholar
  15. 15.
    Thalheim, T., Merkle, D., Middendorf, M.: Protein folding in the HP-model solved with a hybrid population based ACO algorithm. IJCS. (2008)Google Scholar
  16. 16.
    Mohan, U.: Bio Inspired Computing. Division of computer science SOE. CUSAT. (2008)Google Scholar
  17. 17.
    Cutello, V., Nicosia, G., Pavone, M., Timmis, J.: An immune algorithm for protein structure prediction on lattice models. IEEE Trans. Evol. Comput. 11(1) (2007)Google Scholar
  18. 18.
    Zhang, Y., Wu, L.: Artificial bee colony for two dimensional protein folding. Adv. Electr. Eng. Syst. 1(1) (2012)Google Scholar
  19. 19.
    Zhang, Y., Skolnick, J.: Tertiary structure predictions on a comprehensive benchmark of medium to large size proteins. Biophys. J. 87, 2647–2655 (2004)CrossRefGoogle Scholar
  20. 20.
  21. 21.
  22. 22.
  23. 23.

Copyright information

© Springer India 2015

Authors and Affiliations

  1. 1.Department of Computer ApplicationsBharathiar UniversityCoimbatoreIndia

Personalised recommendations