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  • Mixing Energy Models in Genetic Algorithms for On-Lattice Protein Structure Prediction

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    Author
    Rashid, Mahmood
    Newton, MAHakim
    Tamjidul Hoque, Md.
    Sattar, Abdul
    Year published
    2013
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    Abstract
    Protein structure prediction (PSP) is computationally a very challenging problem. The challenge largely comes from the fact that the energy function that needs to be minimised in order to obtain the native structure of a given protein is not clearly known. A high resolution 20x20 energy model could better capture the behaviour of the actual energy function than a low resolution energy model such as hydrophobic polar. However, the fine grained details of the high resolution interaction energy matrix are often not very informative for guiding the search. In contrast, a low resolution energy model could effectively bias the ...
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    Protein structure prediction (PSP) is computationally a very challenging problem. The challenge largely comes from the fact that the energy function that needs to be minimised in order to obtain the native structure of a given protein is not clearly known. A high resolution 20x20 energy model could better capture the behaviour of the actual energy function than a low resolution energy model such as hydrophobic polar. However, the fine grained details of the high resolution interaction energy matrix are often not very informative for guiding the search. In contrast, a low resolution energy model could effectively bias the search towards certain promising directions. In this paper, we develop a genetic algorithm that mainly uses a high resolution energy model for protein structure evaluation but uses a low resolution HP energy model in focussing the search towards exploring structures that have hydrophobic cores. We experimentally show that this mixing of energy models leads to significant lower energy structures compared to the state-of-the-art results.
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    Journal Title
    BioMed Research International
    Volume
    2013
    DOI
    https://doi.org/10.1155/2013/924137
    Copyright Statement
    © The Author(s) 2013. The attached file is posted here with permission of the copyright owners 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 and Image Processing not elsewhere classified
    Publication URI
    http://hdl.handle.net/10072/57694
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    • Journal articles

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