Genetic algorithms for protein folding simulations
Genetic algorithms for protein folding simulations
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 complete and effective move set for simplified protein folding
RECOMB '03 Proceedings of the seventh annual international conference on Research in computational molecular biology
Genetic Algorithm for 3D Protein Folding Simulations
Proceedings of the 5th International Conference on Genetic Algorithms
Optimally Compact Finite Sphere Packings - Hydrophobic Cores in the FCC
CPM '01 Proceedings of the 12th Annual Symposium on Combinatorial Pattern Matching
Blue Gene: a vision for protein science using a petaflop supercomputer
IBM Systems Journal - Deep computing for the life sciences
Search for folding nuclei in native protein structures
Bioinformatics
A Local Move Set for Protein Folding in Triangular Lattice Models
WABI '08 Proceedings of the 8th international workshop on Algorithms in Bioinformatics
DFS Based Partial Pathways in GA for Protein Structure Prediction
PRIB '08 Proceedings of the Third IAPR International Conference on Pattern Recognition in Bioinformatics
Novel Memetic Algorithm for Protein Structure Prediction
AI '09 Proceedings of the 22nd Australasian Joint Conference on Advances in Artificial Intelligence
A memetic approach to protein structure prediction in triangular lattices
ICONIP'11 Proceedings of the 18th international conference on Neural Information Processing - Volume Part I
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This paper presents a Hybrid Genetic Algorithm (HGA) for the protein folding prediction (PFP) applications using the 2D face-centred-cube (FCC) Hydrophobic-Hydrophilic (HP) lattice model. This approach enhances the optimal core formation concept and develops effective and efficient strategies to implement generalized short pull moves to embed highly probable short motifs or building blocks and hence forms the hybridized GA for FCC model. Building blocks containing Hydrophobic (H) – Hydrophilic (P or Polar) covalent bonds are utilized such a way as to help form a core that maximizes the |fitness|. The HGA helps overcome the ineffective crossover and mutation operations that traditionally lead to the stuck condition, especially when the core becomes compact. PFP has been strategically translated into a multi-objective optimization problem and implemented using a swing function, with the HGA providing improved performance in the 2D FCC model compared with the Simple GA.