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Neighborhood Selection in Constraint-Based Local Search for Protein Structure Prediction

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AI 2013: Advances in Artificial Intelligence (AI 2013)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 8272))

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Abstract

Protein structure prediction (PSP) is a very challenging constraint optimization problem. Constraint-based local search approaches have obtained promising results in solving constraint models for PSP. However, the neighborhood exploration policies adopted in these approaches either remain exhaustive or are based on random decisions. In this paper, we propose heuristics to intelligently explore only the promising areas of the search neighborhood. On face centered cubic lattice using a realistic 20×20 energy model and standard benchmark proteins, we obtain structures with significantly lower energy and RMSD values than those obtained by the state-of-the-art algorithms.

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Author information

Authors and Affiliations

  1. Institute for Intelligent and Integrated Systems (IIIS), Griffith University, Australia

    Swakkhar Shatabda, M. A. Hakim Newton & Abdul Sattar

  2. Queensland Research laboratory, National ICT Australia(NICTA), Australia

    Swakkhar Shatabda & Abdul Sattar

Authors
  1. Swakkhar Shatabda
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  2. M. A. Hakim Newton
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  3. Abdul Sattar
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Editor information

Editors and Affiliations

  1. Department of Information Science, University of Otago, 9054, Dunedin, New Zealand

    Stephen Cranefield

  2. Macquarie University, 2109, Sydney, NSW, Australia

    Abhaya Nayak

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Shatabda, S., Newton, M.A.H., Sattar, A. (2013). Neighborhood Selection in Constraint-Based Local Search for Protein Structure Prediction. In: Cranefield, S., Nayak, A. (eds) AI 2013: Advances in Artificial Intelligence. AI 2013. Lecture Notes in Computer Science(), vol 8272. Springer, Cham. https://doi.org/10.1007/978-3-319-03680-9_5

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  • DOI: https://doi.org/10.1007/978-3-319-03680-9_5

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-03679-3

  • Online ISBN: 978-3-319-03680-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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