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  • Improving succinylation prediction accuracy by incorporating the secondary structure via helix, strand and coil, and evolutionary information from profile bigrams

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    Author(s)
    Dehzangi, Abdollah
    Lopez, Yosvany
    Lal, Sunil Pranit
    Taherzadeh, Ghazaleh
    Sattar, Abdul
    Tsunoda, Tatsuhiko
    Sharma, Alok
    Griffith University Author(s)
    Sattar, Abdul
    Year published
    2018
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    Abstract
    Post-translational modification refers to the biological mechanism involved in the enzymatic modification of proteins after being translated in the ribosome. This mechanism comprises a wide range of structural modifications, which bring dramatic variations to the biological function of proteins. One of the recently discovered modifications is succinylation. Although succinylation can be detected through mass spectrometry, its current experimental detection turns out to be a timely process unable to meet the exponential growth of sequenced proteins. Therefore, the implementation of fast and accurate computational methods has ...
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    Post-translational modification refers to the biological mechanism involved in the enzymatic modification of proteins after being translated in the ribosome. This mechanism comprises a wide range of structural modifications, which bring dramatic variations to the biological function of proteins. One of the recently discovered modifications is succinylation. Although succinylation can be detected through mass spectrometry, its current experimental detection turns out to be a timely process unable to meet the exponential growth of sequenced proteins. Therefore, the implementation of fast and accurate computational methods has emerged as a feasible solution. This paper proposes a novel classification approach, which effectively incorporates the secondary structure and evolutionary information of proteins through profile bigrams for succinylation prediction. The proposed predictor, abbreviated as SSEvol-Suc, made use of the above features for training an AdaBoost classifier and consequently predicting succinylated lysine residues. When SSEvol-Suc was compared with four benchmark predictors, it outperformed them in metrics such as sensitivity (0.909), accuracy (0.875) and Matthews correlation coefficient (0.75).
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    Journal Title
    PLoS One
    Volume
    13
    Issue
    2
    DOI
    https://doi.org/10.1371/journal.pone.0191900
    Copyright Statement
    © 2018 Dehzangi et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
    Subject
    Microbiology not elsewhere classified
    Publication URI
    http://hdl.handle.net/10072/381309
    Collection
    • Journal articles

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