DFS Based Partial Pathways in GA for Protein Structure Prediction

Authors:
Md Tamjidul Hoque;Madhu Chetty;Andrew Lewis;Abdul Sattar
Affiliations:
Institute for Integrated and Intelligent Systems (IIIS), Griffith University, Nathan, Australia QLD 4108;Gippsland School of Information Technology (GSIT), Monash University, Churchill, Australia VIC 3842;Institute for Integrated and Intelligent Systems (IIIS), Griffith University, Nathan, Australia QLD 4108;Institute for Integrated and Intelligent Systems (IIIS), Griffith University, Nathan, Australia QLD 4108
Venue:
PRIB '08 Proceedings of the Third IAPR International Conference on Pattern Recognition in Bioinformatics
Year:
2008

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Abstract

Nondeterministic conformational search techniques, such as Genetic Algorithms (GAs) are promising for solving protein structure prediction (PSP) problem. The crossover operator of a GA can underpin the formation of potential conformations by exchanging and sharing potential sub-conformations, which is promising for solving PSP. However, the usual nature of an optimum PSP conformation being compact can produce many invalid conformations (by having non-self-avoiding-walk) using crossover. While a crossover-based converging conformation suffers from limited pathways, combining it with depth-first search (DFS) can partially reveal potential pathways. DFS generates random conformations increasingly quickly with increasing length of the protein sequences compared to random-move-only-based conformation generation. Random conformations are frequently applied for maintaining diversity as well as for initialization in many GA variations.