Fast protein folding in the hydrophobic-hydrophilic model within three-eights of optimal
STOC '95 Proceedings of the twenty-seventh annual ACM symposium on Theory of computing
On the complexity of protein folding (extended abstract)
STOC '98 Proceedings of the thirtieth annual ACM symposium on Theory of computing
Protein folding in the hydrophobic-hydrophilic (HP) is NP-complete
RECOMB '98 Proceedings of the second annual international conference on Computational molecular biology
A new algorithm for protein folding in the HP model
SODA '02 Proceedings of the thirteenth annual ACM-SIAM symposium on Discrete algorithms
Genetic Algorithm for 3D Protein Folding Simulations
Proceedings of the 5th International Conference on Genetic Algorithms
A Standard GA Approach to Native Protein Conformation Prediction
Proceedings of the 6th International Conference on Genetic Algorithms
An improved ant colony optimisation algorithm for the 2D HP protein folding problem
AI'03 Proceedings of the 16th Canadian society for computational studies of intelligence conference on Advances in artificial intelligence
An efficient hybrid Taguchi-genetic algorithm for protein folding simulation
Expert Systems with Applications: An International Journal
Scatter Search algorithm for Protein Structure Prediction
International Journal of Bioinformatics Research and Applications
Applied Bionics and Biomechanics
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Given the amino acid sequence of a protein, predicting its tertiary structure is known as the protein folding problem. This problem has been widely studied under the HP model in which each amino acid is classified, based on its hydrophobicity, as an H (hydrophobic or non-polar) or a P (hydrophilic or polar). Conformation of a protein in the HP model is embedded as a self-avoiding walk in either a two-dimensional or a three-dimensional lattice. The protein folding problem in the HP model is to find a lowest energy conformation. This problem is known to be NP-hard in both two-dimensional and three-dimensional square lattices. In this paper, we present an efficient genetic algorithm for the protein folding problem under the HP model in the two-dimensional square lattice. A special feature of this algorithm is its usage of secondary structures, that the algorithm evolves, as building blocks for the conformation. Experimental results on benchmark sequences show that the algorithm performs very well against existing evolutionary algorithms and Monte Carlo algorithms.