Protein Structure Prediction Using Bee Colony Optimization Metaheuristic
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Predicting the native structure of proteins is one of the most challenging problems in molecular biology. The goal is to determine the three-dimensional structure from the one-dimensional amino acid sequence. De novo prediction algorithms seek to do this by developing a representation of the proteins structure, an energy potential and some optimization algorithm that finds the structure with minimal energy. Bee Colony Optimization (BCO) is a relatively new approach to solving optimization problems based on the foraging behaviour of bees. Several variants of BCO have been suggested in the literature. We have devised a new variant that unifies the existing and is much more flexible with respect to replacing the various elements of the BCO. In particular, this applies to the choice of the local search as well as the method for generating scout locations and performing the waggle dance. We apply our BCO method to generate good solutions to the protein structure prediction problem. The results show that BCO generally finds better solutions than simulated annealing which so far has been the metaheuristic of choice for this problem.
KeywordsProtein structure prediction Bee Colony Optimization Metaheuristic
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- 1.Abbass, H.A.: MBO: marriage in honey bees optimization—a haplometrosis polygynous swarming approach. In: Proceedings of the 2001 Congress on Evolutionary Computation CEC2001, pp. 207–214 (2001)Google Scholar
- 2.Bahamish, H.A.A., Abdullah, R., Salam, R.A.: Protein conformational search using Bees Algorithm. In: Asia International Conference on Modelling and Simulation, pp. 911–916 (2008)Google Scholar
- 4.Chothia, C., Lesk, A.M.: The relation between the divergence of sequence and structure in proteins. EMBO J. 5, 823–826 (1986)Google Scholar
- 8.Karaboga, D.: An idea based on honey bee swarm for numerical optimization. Technical Report TR06, Erciyes Univ., Engineering Faculty, Computer Engineering Department (2005)Google Scholar
- 14.Paluszewski, M., Hamelryck, T., Winter, P.: Reconstructing protein structure from solvent exposure using tabu search. In: Algorithms for Molecular Biology (ALMOB) (2006)Google Scholar
- 15.Paluszewski, M., Winter, P.: EBBA: efficient branch and bound algorithm for protein decoy generation. Technical report. Department of Computer Science, Univ. of Copenhagen, vol. 08(08) (2008)Google Scholar
- 16.Paluszewski, M., Winter, P.: Protein decoy generation using branch and bound with efficient bounding. In: Proc. of the 8th Int. Workshop, WABI 2008, LNBI 5251, pp. 382–393 (2008)Google Scholar
- 17.Pham, D., Koc, E., Ghanbarzadeh, A., Otri, S., Rahim, S., Zaidi, M., Phrueksanant, J., Lee, J., Sahran, S., Sholedolu, M., Ridley, M., Mahmuddin, M., Al-Jabbouli, H., Darwish, A.H., Soroka, A., Packianather, M., Castellani, M.: The Bees Algorithm—a novel tool for optimisation problems. In: Proceedings of IPROMS 2006 Conference, pp. 454–461 (2006)Google Scholar
- 18.Pham, D.T., Ghanbarzadeh, A., Koc, E., Otri, S., Rahim, S., Zaidi, M.: The Bees Algorithm. Technical report, MEC, Cardiff University, UK (2005)Google Scholar
- 20.Sayle, R.: RasMol v2.5 a molecular visualisation program. Biomol. Struc. Glaxo Research and Development Greenford. Roger Sayle and Biomol. Struct. (1994)Google Scholar
- 24.Vilhjalmsson, B., Hamelryck, T.: Predicting a new type of solvent exposure. In: ECCB, Computational Biology Madrid 05, P-C35, Poster (2005)Google Scholar