Neighborhood Selection in Constraint-Based Local Search for Protein Structure Prediction
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Newton, MAH
Sattar, A
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Stephen Cranefield, Abhaya Nayak
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Dunedin, New Zealand
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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 20ײ0 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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Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
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8272 LNAI
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© 2013 Springer Berlin/Heidelberg. This is the author-manuscript version of this paper. Reproduced in accordance with the copyright policy of the publisher.The original publication is available at www.springerlink.com
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Artificial intelligence not elsewhere classified