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  • Capturing non-local interactions by long short-term memory bidirectional recurrent neural networks for improving prediction of protein secondary structure, backbone angles, contact numbers and solvent accessibility

    Author(s)
    Heffernan, Rhys
    Yang, Yuedong
    Paliwal, Kuldip
    Zhou, Yaoqi
    Griffith University Author(s)
    Paliwal, Kuldip K.
    Heffernan, Rhys
    Zhou, Yaoqi
    Yang, Yuedong
    Year published
    2017
    Metadata
    Show full item record
    Abstract
    Motivation: The accuracy of predicting protein local and global structural properties such as secondary structure and solvent accessible surface area has been stagnant for many years because of the challenge of accounting for non-local interactions between amino acid residues that are close in three-dimensional structural space but far from each other in their sequence positions. All existing machine-learning techniques relied on a sliding window of 10–20 amino acid residues to capture some ‘short to intermediate’ non-local interactions. Here, we employed Long Short-Term Memory (LSTM) Bidirectional Recurrent Neural Networks ...
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    Motivation: The accuracy of predicting protein local and global structural properties such as secondary structure and solvent accessible surface area has been stagnant for many years because of the challenge of accounting for non-local interactions between amino acid residues that are close in three-dimensional structural space but far from each other in their sequence positions. All existing machine-learning techniques relied on a sliding window of 10–20 amino acid residues to capture some ‘short to intermediate’ non-local interactions. Here, we employed Long Short-Term Memory (LSTM) Bidirectional Recurrent Neural Networks (BRNNs) which are capable of capturing long range interactions without using a window. Results: We showed that the application of LSTM-BRNN to the prediction of protein structural properties makes the most significant improvement for residues with the most long-range contacts (|i-j| >19) over a previous window-based, deep-learning method SPIDER2. Capturing long-range interactions allows the accuracy of three-state secondary structure prediction to reach 84% and the correlation coefficient between predicted and actual solvent accessible surface areas to reach 0.80, plus a reduction of 5%, 10%, 5% and 10% in the mean absolute error for backbone ϕϕ , ψ, θ and τ angles, respectively, from SPIDER2. More significantly, 27% of 182724 40-residue models directly constructed from predicted Cα atom-based θ and τ have similar structures to their corresponding native structures (6Å RMSD or less), which is 3% better than models built by ϕϕ and ψ angles. We expect the method to be useful for assisting protein structure and function prediction.
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    Journal Title
    Bioinformatics
    Volume
    33
    Issue
    18
    DOI
    https://doi.org/10.1093/bioinformatics/btx218
    Subject
    Biological Sciences not elsewhere classified
    Mathematical Sciences
    Biological Sciences
    Information and Computing Sciences
    Publication URI
    http://hdl.handle.net/10072/368329
    Collection
    • Journal articles

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