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
Blue Gene: a vision for protein science using a petaflop supercomputer
IBM Systems Journal - Deep computing for the life sciences
An Efficient Algorithm for Computing the Fitness Function of a Hydrophobic-Hydrophilic Model
HIS '04 Proceedings of the Fourth International Conference on Hybrid Intelligent Systems
Partially Computed Fitness Function Based Genetic Algorithm for Hydrophobic-Hydrophilic Model
HIS '04 Proceedings of the Fourth International Conference on Hybrid Intelligent Systems
Twin Removal in Genetic Algorithms for Protein Structure Prediction Using Low-Resolution Model
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
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The use of Genetic Algorithms in a 2D Hydrophobic-Hydrophilic (HP) model in protein folding prediction application requires frequent fitness function computations. While the fitness computation is linear, the overhead incurred is significant with respect to the protein folding prediction problem. Any reduction in the computational cost will therefore assist in more efficiently searching the enormous solution space for protein folding prediction. This paper proposes a novel pruning strategy that exploits the inherent properties of the HP model and guarantee reduction of the computational complexity during an ordered traversal of the amino acid chain sequences for fitness computation, truncating the sequence by at least one residue.