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  • A Parallel Framework for Multipoint Spiral Search in ab Initio Protein Structure Prediction

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    Author(s)
    Rashid, MA
    Shatabda, S
    Newton, MAH
    Hoque, MT
    Sattar, A
    Griffith University Author(s)
    Sattar, Abdul
    Rashid, Mahmood A.
    Newton, MAHakim A.
    Shatabda, Swakkhar
    Hoque, Md T.
    Year published
    2014
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    Abstract
    Protein structure prediction is computationally a very challenging problem. A large number of existing search algorithms attempt to solve the problem by exploring possible structures and finding the one with the minimum free energy. However, these algorithms perform poorly on large sized proteins due to an astronomically wide search space. In this paper, we present a multipoint spiral search framework that uses parallel processing techniques to expedite exploration by starting from different points. In our approach, a set of random initial solutions are generated and distributed to different threads. We allow each thread to ...
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    Protein structure prediction is computationally a very challenging problem. A large number of existing search algorithms attempt to solve the problem by exploring possible structures and finding the one with the minimum free energy. However, these algorithms perform poorly on large sized proteins due to an astronomically wide search space. In this paper, we present a multipoint spiral search framework that uses parallel processing techniques to expedite exploration by starting from different points. In our approach, a set of random initial solutions are generated and distributed to different threads. We allow each thread to run for a predefined period of time. The improved solutions are stored threadwise. When the threads finish, the solutions are merged together and the duplicates are removed. A selected distinct set of solutions are then split to different threads again. In our ab initio protein structure prediction method, we use the three-dimensional face-centred-cubic lattice for structure-backbone mapping. We use both the low resolution hydrophobic-polar energy model and the high-resolution 20x20 energy model for search guiding. The experimental results show that our new parallel framework significantly improves the results obtained by the state-of-the-art single-point search approaches for both energy models on three-dimensional face-centred-cubic lattice. We also experimentally show the effectiveness of mixing energy models within parallel threads.
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    Journal Title
    Advances in Bioinformatics
    Volume
    2014
    DOI
    https://doi.org/10.1155/2014/985968
    Copyright Statement
    © The Author(s) 2014. The attached file is posted here with permission of the copyright owner[s] for your personal use only. No further distribution permitted.For information about this journal please refer to the journal’s website. The online version of this work is licensed under a Creative Commons License, available at http://creativecommons.org/licenses/by-nc-sa/2.1/au/
    Subject
    Artificial intelligence not elsewhere classified
    Publication URI
    http://hdl.handle.net/10072/66464
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    • Journal articles

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