TY - GEN
T1 - An efficient genetic algorithm for predicting protein tertiary structures in the 2D HP model
AU - Bui, Thang N.
AU - Sundarraj, Gnanasekaran
PY - 2005
Y1 - 2005
N2 - 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.
AB - 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.
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U2 - 10.1145/1068009.1068072
DO - 10.1145/1068009.1068072
M3 - Conference contribution
AN - SCOPUS:32444434801
SN - 1595930108
SN - 9781595930101
T3 - GECCO 2005 - Genetic and Evolutionary Computation Conference
SP - 385
EP - 392
BT - GECCO 2005 - Genetic and Evolutionary Computation Conference
A2 - Beyer, H.G.
A2 - O'Reilly, U.M.
A2 - Arnold, D.
A2 - Banzhaf, W.
A2 - Blum, C.
A2 - Bonabeau, E.W.
A2 - Cantu-Paz, E.
A2 - Dasgupta, D.
A2 - Deb, K.
A2 - et al, al
T2 - GECCO 2005 - Genetic and Evolutionary Computation Conference
Y2 - 25 June 2005 through 29 June 2005
ER -