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  • Effective protein conformational sampling based on predicted torsion angles

    Author(s)
    Yang, Yuedong
    Zhou, Yaoqi
    Griffith University Author(s)
    Zhou, Yaoqi
    Yang, Yuedong
    Year published
    2016
    Metadata
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    Abstract
    Protein structure prediction is a long‐standing problem in molecular biology. Due to lack of an accurate energy function, it is often difficult to know whether the sampling algorithm or the energy function is the most important factor for failure of locating near‐native conformations of proteins. This article examines the size dependence of sampling effectiveness by using a perfect “energy function”: the root‐mean‐squared distance from the target native structure. Using protein targets up to 460 residues from critical assessment of structure prediction techniques (CASP11, 2014), we show that the accuracy of near native ...
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    Protein structure prediction is a long‐standing problem in molecular biology. Due to lack of an accurate energy function, it is often difficult to know whether the sampling algorithm or the energy function is the most important factor for failure of locating near‐native conformations of proteins. This article examines the size dependence of sampling effectiveness by using a perfect “energy function”: the root‐mean‐squared distance from the target native structure. Using protein targets up to 460 residues from critical assessment of structure prediction techniques (CASP11, 2014), we show that the accuracy of near native structures sampled is relatively independent of protein sizes but strongly depends on the errors of predicted torsion angles. Even with 40% out‐of‐range angle prediction, 2 Å or less near‐native conformation can be sampled. The result supports that the poor energy function is one of the bottlenecks of structure prediction and predicted torsion angles are useful for overcoming the bottleneck by restricting the sampling space in the absence of a perfect energy function.
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    Journal Title
    Journal of Computational Chemistry
    DOI
    https://doi.org/10.1002/jcc.24285
    Note
    This publication has been entered into Griffith Research Online as an Advanced Online Version.
    Subject
    Physical chemistry
    Physical chemistry not elsewhere classified
    Theoretical and computational chemistry
    Nanotechnology
    Publication URI
    http://hdl.handle.net/10072/101925
    Collection
    • Journal articles

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