Abstract
We present an Immune Algorithm (IA) based on clonal selection principle and which uses memory B cells, to face the protein structure prediction problem (PSP) a particular example of the String Folding Problem in 2D and 3D lattice. Memory B cells with a longer life span are used to partition the funnel landscape of PSP, so to properly explore the search space. The designed IA shows its ability to tackle standard benchmarks instances substantially better than other IA’s. In particular, for the 3D HP model the IA allowed us to find energy minima not found by other evolutionary algorithms described in literature.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Cutello, V., Nicosia, G.: The clonal selection principle for in silico and in vitro computing. In: De Castro, L.N., Von Zuben, F.J. (eds.) Recent Developments in Biologically Inspired Computing. Idea Group Publishing, Hershey (2004)
De Castro, L.N., Von Zuben, F.J.: Learning and optimization using the clonal selection principle. IEEE Trans. Evol. Comput. 6(3), 239–251 (2002)
Plotkin, S.S., Onuchic, J.N.: Understanding protein folding with energy landscape theory. Quarterly Reviews of Biophysics 35(2), 111–167 (2002)
Cutello, V., Nicosia, G., Pavone, M.: An immune algorithm with hyper-macromutations for the 2D hydrophilic-hydrophobic model. In: CEC 2004, vol. 1, pp. 1074–1080. IEEE Press, Los Alamitos (2004)
Cutello, V., Nicosia, G., Pavone, M.: A hybrid immune algorithm with information gain for the graph coloring problem. In: Cantú-Paz, E., Foster, J.A., Deb, K., Davis, L., Roy, R., O’Reilly, U.-M., Beyer, H.-G., Kendall, G., Wilson, S.W., Harman, M., Wegener, J., Dasgupta, D., Potter, M.A., Schultz, A., Dowsland, K.A., Jonoska, N., Miller, J., Standish, R.K. (eds.) GECCO 2003. LNCS, vol. 2723, pp. 171–182. Springer, Heidelberg (2003)
Dill, K.A.: Theory for the folding and stability of globular proteins. Biochemistry 24(6), 1501–1509 (1985)
Hirst, J.D.: The evolutionary landscape of functional model proteins. Protein Engineering 12(9), 721–726 (1999)
Unger, R., Moult, J.: Genetic algorithms for protein folding simulations. J. Molecular Biology 231(1), 75–81 (1993)
Cotta, C.: Protein Structure Prediction using Evolutionary Algorithms Hybridized with Backtracking. In: Mira, J., Álvarez, J.R. (eds.) IWANN 2003. LNCS, vol. 2686, pp. 321–328. Springer, Heidelberg (2003)
Krasnogor, N., Blackburne, B.P., Burke, E.K., Hirst, J.D.: Multimeme algorithms for protein structure prediction. In: Guervós, J.J.M., Adamidis, P.A., Beyer, H.-G., Fernández-Villacañas, J.-L., Schwefel, H.-P. (eds.) PPSN 2002. LNCS, vol. 2439, pp. 769–778. Springer, Heidelberg (2002)
Krasnogor, N., Hart, W.E., Smith, J., Pelta, D.A.: Protein structure prediction with evolutionary algorithms. In: GECCO 1999, pp. 1596–1601 (1999)
Crescenzi, P., Goldman, D., Papadimitriou, C., Piccolboni, A., Yannakakis, M.: On the complexity of protein folding. Journal of Computational Biology 5(3), 423–466 (1998)
Berger, B., Leighton, T.: Protein folding in the hydrophobic-hydrophilic model is np complete. J. Comput. Biol. 5, 27–40 (1998)
Cutello, V., Nicosia, G., Pavone, M.: Exploring the capability of immune algorithms: A characterization of hypermutation operators. In: Nicosia, G., Cutello, V., Bentley, P.J., Timmis, J. (eds.) ICARIS 2004. LNCS, vol. 3239, pp. 263–276. Springer, Heidelberg (2004)
Seiden, P.E., Celada, F.: A model for simulating cognate recognition and response in the immune system. J. Theor. Biology 158, 329–357 (1992)
Shmygelska, A., Hoos, H.H.: An Improved Ant Colony Optimization Algorithm for the 2D HP Protein Folding Problem. In: Xiang, Y., Chaib-draa, B. (eds.) Canadian AI 2003. LNCS (LNAI), vol. 2671, pp. 400–417. Springer, Heidelberg (2003)
Blackburne, B.P., Hirst, J.D.: Evolution of functional model proteins. J. Chemical Physics 115(4), 1935–1942 (2001)
Chan, H.S., Dill, K.A.: Comparing folding codes for proteins and polymers. Proteins: Struct., Funct., Genet. 24, 335–344 (1996)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Cutello, V., Morelli, G., Nicosia, G., Pavone, M. (2005). Immune Algorithms with Aging Operators for the String Folding Problem and the Protein Folding Problem. In: Raidl, G.R., Gottlieb, J. (eds) Evolutionary Computation in Combinatorial Optimization. EvoCOP 2005. Lecture Notes in Computer Science, vol 3448. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-31996-2_8
Download citation
DOI: https://doi.org/10.1007/978-3-540-31996-2_8
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-25337-2
Online ISBN: 978-3-540-31996-2
eBook Packages: Computer ScienceComputer Science (R0)