Abstract
Protein structure prediction (PSP) remains one of the most challenging open problems in structural bioinformatics. Simplified models in terms of lattice structure and energy function have been proposed to ease the computational hardness of this combinatorial optimization problem. In this paper, we describe a clustered meme-based evolutionary approach for PSP using triangular lattice model. Under the framework of memetic algorithm, the proposed method extracts a pool of cultural information from different regions of the search space using data clustering technique. These highly observed local substructures, termed as meme, are then aggregated centrally for further refinements as second stage of evolution. The optimal utilization of ‘explore-and-exploit’ feature of evolutionary algorithms is ensured by the inherent parallel architecture of the algorithm and subsequent use of cultural information.
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References
Agarwala, R., et al.: Local rules for protein folding on a triangular lattice and generalized hydrophobicity in the HP model. In: Proc. SODA 1997, pp. 390–399 (1997)
Anfinsen, C.B.: Principles that govern the folding of protein chains. Science 181, 223–230 (1973)
Bazzoli, A., Tettamanzi, A.G.B.: A Memetic Algorithm for Protein Structure Prediction in a 3D-Lattice HP Model. In: Raidl, G.R., Cagnoni, S., Branke, J., Corne, D.W., Drechsler, R., Jin, Y., Johnson, C.G., Machado, P., Marchiori, E., Rothlauf, F., Smith, G.D., Squillero, G. (eds.) EvoWorkshops 2004. LNCS, vol. 3005, pp. 1–10. Springer, Heidelberg (2004)
Böckenhauer, H.-J., Dayem Ullah, A.Z.M., Kapsokalivas, L., Steinhöfel, K.: A Local Move Set for Protein Folding in Triangular Lattice Models. In: Crandall, K.A., Lagergren, J. (eds.) WABI 2008. LNCS (LNBI), vol. 5251, pp. 369–381. Springer, Heidelberg (2008)
Crescenzi, P., et al.: On the complexity of protein folding. Journal of Computational Biology 5, 423–465 (1998)
Dal Palù, A., et al.: A constraint solver for discrete lattices, its parallelization, and application to protein structure prediction. Software-Practice and Experience 37, 1405–1449 (2007)
Dawkins, R.: The Selfish Gene. Oxford University Press, New York (1976)
Dill, K.A., et al.: Principles of protein folding - A perspective from simple exact models. Protein Science 4, 561–602 (1995)
Hart, W.E., et al.: Fast protein folding in the hydrophobic-hydrophilic model within three-eights of optimal. In: ACM Symposium on Theory of Computing, pp. 157–168 (1995)
Hoque, M.T., Chetty, M., Dooley, L.S.: A Hybrid Genetic Algorithm for 2D FCC Hydrophobic-Hydrophilic Lattice Model to Predict Protein Folding. In: Sattar, A., Kang, B.-h. (eds.) AI 2006. LNCS (LNAI), vol. 4304, pp. 867–876. Springer, Heidelberg (2006)
Hoque, M.T., et al.: Protein folding prediction in 3d fcc hp lattice model using genetic algorithm. In: Proc. CEC 2007, pp. 4138–4145 (2007)
Islam, M. K., Chetty, M.: Novel Memetic Algorithm for Protein Structure Prediction. In: Nicholson, A., Li, X. (eds.) AI 2009. LNCS, vol. 5866, pp. 412–421. Springer, Heidelberg (2009)
Islam, M.K., et al.: Clustered Memetic Algorithm for Protein Structure Prediction. In: Proc. CEC 2010, pp. 1–8 (2010)
Jiang, T., et al.: Protein folding simulations of the hydrophobic–hydrophilic model by combining tabu search with genetic algorithms. The Journal of Chemical Physics 119(8), 4592–4596 (2003)
Kapsokalivas, L., et al.: Two Local Search Methods for Protein Folding Simulation in the HP and the MJ Lattice Models. In: Proc. BIRD 2008, pp. 167–179 (2008)
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)
Lesh, N., et al.: A complete and effective move set for simplified protein folding. In: Proc. ICCB 2003, pp. 188–195 (2003)
Liang, F., et al.: Evolutionary Monte Carlo for protein folding simulations. Journal of Chemical Physics 115(7), 3374–3380 (2001)
Lopes, H.S., et al.: An enhanced genetic algorithm for protein structure prediction using the 2D hydrophobic-polar mode. In: Proc. AE 2005, pp. 238–246 (2005)
Mann, M., et al.: CPSP-tools - Exact and Complete Algorithms for High-throughput 3D Lattice Protein Studies. BMC Bioinformatics 9, 230 (2008)
Martínez-estudillo, A., et al.: Hybridization of evolutionary algorithms and local search by means of a clustering method. IEEE Transactions on Systems, Man and Cybernetics, Part B 36, 534–545 (2006)
Ong, Y., et al.: Memetic Computation — Past, Present & Future [Research Frontier]. IEEE Computational Intelligence Magazine 5(2), 24–31 (2010)
Park, B.H., et al.: The complexity and accuracy of discrete state models of protein structure. Journal of Molecular Biology 249(2), 493–507 (1995)
Pelta, D.A., et al.: Multimeme algorithms using fuzzy logic based memes for protein structure prediction. In: Krasnogor, N., Smith, J.E. (eds.) Recent Advances in Memetic Algorithms. Springer, Heidelberg (2004)
Shmygelska, A., et al.: An ant colony optimisation algorithm for the 2D and 3D hydrophobic polar protein folding problem. BMC Bioinformatics 6(30) (2005)
Thachuk, C., et al.: A replica exchange Monte Carlo algorithm for protein folding in the HP model. BMC Bioinformatics 8, 342 (2007)
Unger, R., et al.: Genetic algorithms for protein folding simulations. Journal of Molecular Biology 231(1), 75–81 (1993)
Yue, K., et al.: Forces of tertiary structural organization in globular proteins. Proc. Natural Academy of Sciences USA 92, 146–150 (1995)
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Islam, M.K., Chetty, M., Ullah, A.D., Steinhöfel, K. (2011). A Memetic Approach to Protein Structure Prediction in Triangular Lattices. In: Lu, BL., Zhang, L., Kwok, J. (eds) Neural Information Processing. ICONIP 2011. Lecture Notes in Computer Science, vol 7062. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24955-6_74
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DOI: https://doi.org/10.1007/978-3-642-24955-6_74
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