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    Protein Function Prediction Based on Sequence and Structure Information

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    Thesis Report - Fatima Zohra Smaili copy.pdf
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    1.240Mb
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    Type
    Thesis
    Authors
    Smaili, Fatima Z. cc
    Advisors
    Gao, Xin cc
    Committee members
    Arold, Stefan T. cc
    Bajic, Vladimir B. cc
    Program
    Computer Science
    KAUST Department
    Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division
    Date
    2016-05-25
    Embargo End Date
    2017-05-25
    Permanent link to this record
    http://hdl.handle.net/10754/610703
    
    Metadata
    Show full item record
    Access Restrictions
    At the time of archiving, the student author of this thesis opted to temporarily restrict access to it. The full text of this thesis became available to the public after the expiration of the embargo on 2017-05-25.
    Abstract
    The number of available protein sequences in public databases is increasing exponentially. However, a significant fraction of these sequences lack functional annotation which is essential to our understanding of how biological systems and processes operate. In this master thesis project, we worked on inferring protein functions based on the primary protein sequence. In the approach we follow, 3D models are first constructed using I-TASSER. Functions are then deduced by structurally matching these predicted models, using global and local similarities, through three independent enzyme commission (EC) and gene ontology (GO) function libraries. The method was tested on 250 “hard” proteins, which lack homologous templates in both structure and function libraries. The results show that this method outperforms the conventional prediction methods based on sequence similarity or threading. Additionally, our method could be improved even further by incorporating protein-protein interaction information. Overall, the method we use provides an efficient approach for automated functional annotation of non-homologous proteins, starting from their sequence.
    Citation
    Smaili, F. Z. (2016). Protein Function Prediction Based on Sequence and Structure Information. KAUST Research Repository. https://doi.org/10.25781/KAUST-Z5074
    DOI
    10.25781/KAUST-Z5074
    ae974a485f413a2113503eed53cd6c53
    10.25781/KAUST-Z5074
    Scopus Count
    Collections
    MS Theses; Computer Science Program; Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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