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  • Prediction of RNA binding proteins comes of age from low resolution to high resolution

    Author(s)
    Zhao, Huiying
    Yang, Yuedong
    Zhou, Yaoqi
    Griffith University Author(s)
    Zhou, Yaoqi
    Yang, Yuedong
    Year published
    2013
    Metadata
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    Abstract
    Networks of protein-RNA interactions is likely to be larger than protein-protein and protein-DNA inter- action networks because RNA transcripts are encoded tens of times more than proteins (e.g. only 3% of human genome coded for proteins), have diverse function and localization, and are controlled by pro- teins from birth (transcription) to death (degradation). This massive network is evidenced by several recent experimental discoveries of large numbers of previously unknown RNA-binding proteins (RBPs). Meanwhile, more than 400 non-redundant protein-RNA complex structures (at 25% sequence identity or less) have been deposited ...
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    Networks of protein-RNA interactions is likely to be larger than protein-protein and protein-DNA inter- action networks because RNA transcripts are encoded tens of times more than proteins (e.g. only 3% of human genome coded for proteins), have diverse function and localization, and are controlled by pro- teins from birth (transcription) to death (degradation). This massive network is evidenced by several recent experimental discoveries of large numbers of previously unknown RNA-binding proteins (RBPs). Meanwhile, more than 400 non-redundant protein-RNA complex structures (at 25% sequence identity or less) have been deposited into the protein databank. These sequences and structural resources for RBPs provide ample data for the development of computational techniques dedicated to RBP predic- tion, as experimentally determining RNA-binding functions is time-consuming and expensive. This review compares traditional machine-learning based approaches with emerging template-based meth- ods at several levels of prediction resolution ranging from two-state binding/non-binding prediction, to binding residue prediction and protein-RNA complex structure prediction. The analysis indicates that the two approaches are complementary and their combinations may lead to further improvements.
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    Journal Title
    Molecular BioSystems
    Volume
    9
    Issue
    10
    DOI
    https://doi.org/10.1039/c3mb70167k
    Subject
    Bioinformatics
    Biochemistry and Cell Biology
    Publication URI
    http://hdl.handle.net/10072/170436
    Collection
    • Journal articles

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