DFS Based Partial Pathways in GA for Protein Structure Prediction

  • Md Tamjidul Hoque
  • Madhu Chetty
  • Andrew Lewis
  • Abdul Sattar
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5265)

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.

Keywords

Depth-first search protein structure prediction genetic algorithm lattice model 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Md Tamjidul Hoque
    • 1
  • Madhu Chetty
    • 2
  • Andrew Lewis
    • 1
  • Abdul Sattar
    • 1
  1. 1.Institute for Integrated and Intelligent Systems (IIIS)Griffith UniversityNathanAustralia
  2. 2.Gippsland School of Information Technology (GSIT)Monash UniversityChurchillAustralia

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