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  • SPOT-1D-Single: Improving the Single-Sequence-Based Prediction of Protein Secondary Structure, Backbone Angles, Solvent Accessibility and Half-Sphere Exposures using a Large Training Set and Ensembled Deep Learning

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    Accepted Manuscript (AM)
    Author(s)
    Singh, Jaspreet
    Litfin, Thomas
    Paliwal, Kuldip
    Singh, Jaswinder
    Hanumanthappa, Anil Kumar
    Zhou, Yaoqi
    Griffith University Author(s)
    Litfin, Tom
    Paliwal, Kuldip K.
    Year published
    2021
    Metadata
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    Abstract
    MOTIVATION: Knowing protein secondary and other one-dimensional structural properties are essential for accurate protein structure and function prediction. As a result, many methods have been developed for predicting these one-dimensional structural properties. However, most methods relied on evolutionary information that may not exist for many proteins due to a lack of sequence homologs. Moreover, it is computationally intensive for obtaining evolutionary information as the library of protein sequences continues to expand exponentially. Here we developed a new single-sequence method called SPOT-1D-Single based on a large ...
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    MOTIVATION: Knowing protein secondary and other one-dimensional structural properties are essential for accurate protein structure and function prediction. As a result, many methods have been developed for predicting these one-dimensional structural properties. However, most methods relied on evolutionary information that may not exist for many proteins due to a lack of sequence homologs. Moreover, it is computationally intensive for obtaining evolutionary information as the library of protein sequences continues to expand exponentially. Here we developed a new single-sequence method called SPOT-1D-Single based on a large training dataset of 39120 proteins deposited prior to 2016 and an ensemble of hybrid Long-Short-Term-Memory bidirectional neural network and convolutional neural network. RESULTS: We showed that SPOT-1D-Single consistently improves over SPIDER3-Single and ProteinUnet for secondary structure, solvent accessibility, contact number, and backbone angles prediction for all seven independent test sets (TEST2018, SPOT-2016, SPOT-2016-HQ, SPOT-2018, SPOT-2018-HQ, CASP12, and CASP13 free-modeling targets). For example, the predicted three-state secondary structure's accuracy ranges from 72.12-74.28% by SPOT-1D-Single, compared to 69.1-72.6% by SPIDER3-Single and 70.6-73% by ProteinUnet. SPOT-1D-Single also predicts SS3 and SS8 with 6.24% and 6.98% better accuracy than SPOT-1D on SPOT-2018 proteins with no homologs (Neff=1), respectively. The new method's improvement over existing techniques is due to a larger training set combined with ensembled learning. AVAILABILITY: Standalone-version of SPOT-1D-Single is available at https://github.com/jas-preet/SPOT-1D-Single. Direct prediction can also be made at https://sparks-lab.org/server/spot-1d-single. The datasets used in this research can also be downloaded from GitHub.
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    Journal Title
    Bioinformatics
    DOI
    https://doi.org/10.1093/bioinformatics/btab316
    Funder(s)
    ARC
    Grant identifier(s)
    DP210101875
    Copyright Statement
    © 2021 Oxford University Press. This is a pre-copy-editing, author-produced PDF of an article accepted for publication in Bioinformatics following peer review. The definitive publisher-authenticated version SPOT-1D-Single: Improving the Single-Sequence-Based Prediction of Protein Secondary Structure, Backbone Angles, Solvent Accessibility and Half-Sphere Exposures using a Large Training Set and Ensembled Deep Learning, Bioinformatics, 2021 is available online at: https://doi.org/10.1093/bioinformatics/btab316.
    Note
    This publication has been entered as an advanced online version in Griffith Research Online.
    Subject
    Mathematical sciences
    Biological sciences
    Publication URI
    http://hdl.handle.net/10072/404530
    Collection
    • Journal articles

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