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  • Improving protein fold recognition and template-based modeling by employing probabilistic-based matching between predicted one-dimensional structural properties of query and corresponding native properties of templates

    Author(s)
    Yang, Yuedong
    Faraggi, Eshel
    Zhao, Huiying
    Zhou, Yaoqi
    Griffith University Author(s)
    Zhou, Yaoqi
    Yang, Yuedong
    Year published
    2011
    Metadata
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    Abstract
    Motivation: In recent years, development of a single-method fold-recognition server lags behind consensus and multiple template techniques. However, a good consensus prediction relies on the accuracy of individual methods. This article reports our efforts to further improve a single-method fold recognition technique called SPARKS by changing the alignment scoring function and incorporating the SPINE-X techniques that make improved prediction of secondary structure, backbone torsion angle and solvent accessible surface area. Results: The new method called SPARKS-X was tested with the SALIGN benchmark for alignment accuracy, ...
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    Motivation: In recent years, development of a single-method fold-recognition server lags behind consensus and multiple template techniques. However, a good consensus prediction relies on the accuracy of individual methods. This article reports our efforts to further improve a single-method fold recognition technique called SPARKS by changing the alignment scoring function and incorporating the SPINE-X techniques that make improved prediction of secondary structure, backbone torsion angle and solvent accessible surface area. Results: The new method called SPARKS-X was tested with the SALIGN benchmark for alignment accuracy, Lindahl and SCOP benchmarks for fold recognition, and CASP 9 blind test for structure prediction. The method is compared to several state-of-the-art techniques such as HHPRED and BoostThreader. Results show that SPARKS-X is one of the best single-method fold recognition techniques. We further note that incorporating multiple templates and refinement in model building will likely further improve SPARKS-X.
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    Journal Title
    Bioinformatics
    Volume
    27
    Issue
    15
    DOI
    https://doi.org/10.1093/bioinformatics/btr350
    Subject
    Bioinformatics
    Mathematical Sciences
    Biological Sciences
    Information and Computing Sciences
    Publication URI
    http://hdl.handle.net/10072/56124
    Collection
    • Journal articles

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