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  • Discovering MoRFs by trisecting intrinsically disordered protein sequence into terminals and middle regions

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    Author(s)
    Sharma, Ronesh
    Sharma, Alok
    Patil, Ashwini
    Tsunoda, Tatsuhiko
    Griffith University Author(s)
    Sharma, Alok
    Year published
    2019
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    Abstract
    Background: Molecular Recognition Features (MoRFs) are short protein regions present in intrinsically disordered protein (IDPs) sequences. MoRFs interact with structured partner protein and upon interaction, they undergo a disorder-to-order transition to perform various biological functions. Analyses of MoRFs are important towards understanding their function. Results: Performance is reported using the MoRF dataset that has been previously used to compare the other existing MoRF predictors. The performance obtained in this study is equivalent to the benchmarked OPAL predictor, i.e., OPAL achieved AUC of 0.815, whereas the ...
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    Background: Molecular Recognition Features (MoRFs) are short protein regions present in intrinsically disordered protein (IDPs) sequences. MoRFs interact with structured partner protein and upon interaction, they undergo a disorder-to-order transition to perform various biological functions. Analyses of MoRFs are important towards understanding their function. Results: Performance is reported using the MoRF dataset that has been previously used to compare the other existing MoRF predictors. The performance obtained in this study is equivalent to the benchmarked OPAL predictor, i.e., OPAL achieved AUC of 0.815, whereas the model in this study achieved AUC of 0.819 using TEST set. Conclusion: Achieving comparable performance, the proposed method can be used as an alternative approach for MoRF prediction.
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    Journal Title
    BMC BIOINFORMATICS
    Volume
    19
    DOI
    https://doi.org/10.1186/s12859-018-2396-7
    Copyright Statement
    © The Author(s). 2019 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
    Subject
    Mathematical sciences
    Biological sciences
    Publication URI
    http://hdl.handle.net/10072/385825
    Collection
    • Journal articles

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