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  • C-iSUMO: A sumoylation site predictor that incorporates intrinsic characteristics of amino acid sequences

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    Sharma433150-Accepted.pdf (395.6Kb)
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    Accepted Manuscript (AM)
    Author(s)
    Lopez, Yosvany
    Dehzangi, Abdollah
    Reddy, Hamendra Manhar
    Sharma, Alok
    Griffith University Author(s)
    Sharma, Alok
    Year published
    2020
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    Abstract
    Post-translational modifications are considered important molecular interactions in protein science. One of these modifications is “sumoylation” whose computational detection has recently become a challenge. In this paper, we propose a new computational predictor which makes use of the sine and cosine of backbone torsion angles and the accessible surface area for predicting sumoylation sites. The aforementioned features were computed for all the proteins in our benchmark dataset, and a training matrix consisting of sumoylation and non-sumoylation sites was ultimately created. This training matrix was balanced by undersampling ...
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    Post-translational modifications are considered important molecular interactions in protein science. One of these modifications is “sumoylation” whose computational detection has recently become a challenge. In this paper, we propose a new computational predictor which makes use of the sine and cosine of backbone torsion angles and the accessible surface area for predicting sumoylation sites. The aforementioned features were computed for all the proteins in our benchmark dataset, and a training matrix consisting of sumoylation and non-sumoylation sites was ultimately created. This training matrix was balanced by undersampling the majority class (non-sumoylation sites) using the NearMiss method. Finally, an AdaBoost classifier was used for discriminating between sumoylation and non-sumoylation sites. Our predictor was called “C-iSumo” because of its effective use of circular functions. C-iSumo was compared with another predictor which was outperformed in statistical metrics such as sensitivity (0.734), accuracy (0.746) and Matthews correlation coefficient (0.494).
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    Journal Title
    Computational Biology and Chemistry
    Volume
    87
    DOI
    https://doi.org/10.1016/j.compbiolchem.2020.107235
    Copyright Statement
    © 2020 Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International Licence (http://creativecommons.org/licenses/by-nc-nd/4.0/) which permits unrestricted, non-commercial use, distribution and reproduction in any medium, providing that the work is properly cited.
    Subject
    Chemical sciences
    Biological sciences
    Science & Technology
    Life Sciences & Biomedicine
    Biology
    Interdisciplinary Applications
    Publication URI
    http://hdl.handle.net/10072/396834
    Collection
    • Journal articles

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