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Predicting the structures of a protein, i.e. finding low-energy conformations of a protein is one of the most prominent problems in bioinformatics. A simplified two-dimensional (2D) hydrophobic-hydrophilic (HP) lattice model is studied. Despite the simplicity of the model, the protein structure prediction problem on the HP lattice model has been proven to be NP-hard. The ant colony optimization (ACO)...
The protein structural class plays an important role in protein science. In this paper, a new method for predicting protein structural class based Flexible Neural Tree Ensemble (FNTE) is introduced. The structure of Flexible Neural Tree (FNT) is developed by Probabilistic Incremental Program Evolution (PIPE) and the parameters are optimized by Particle Swarm Optimization (PSO) algorithm. The one thousand...
Predicting the structure of protein through its sequence of amino acids is a complex and challenging problem in computational biology. Though toy model is one of the simplest and effective models, it is still extremely difficult to predict its structure as the increase of amino acids. Particle swarm optimization (PSO) is a swarm intelligence algorithm, has been successfully applied to many optimization...
Tabu search is a meta-heuristic approach that is found to be useful in solving combinatorial optimization problems. This paper employs the adaptive memory features of tabu search to deal with protein folding problem. A kind of optimization of the neighborhood scale is presented, where a annealing mechanism is also used to enhance the searching ability for optimum solutions of the AB off-lattice model...
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