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Sequence-based prediction and characterization of disorder-to-order transitioning binding sites in proteins

  • Author / Creator
    Miri Disfani, Fatemeh
  • Molecular Recognition Feature (MoRF) regions are disordered binding sites that become structured upon binding. MoRFs are implicated in important biological processes, including signaling and regulation. However, only a limited number of experimentally validated MoRFs is known, which motivates development of computational methods that predict MoRFs from protein chains. We introduce a new MoRF predictor, MoRFpred, which identifies all MoRF types (, , coil, and complex). We develop a comprehensive dataset of annotated MoRFs and use it to build and empirically compare our method. Empirical evaluation shows that MoRFpred statistically significantly outperforms existing predictors by 0.07 in AUC and 10% in success rate. We show that our predicted MoRF regions have non-random sequence similarity with native MoRFs. We use this observation along with the fact that predictions with higher probability are more accurate to identify putative MoRF regions. We present case studies to analyze these putative MoRFs.

  • Subjects / Keywords
  • Graduation date
    2012-06
  • Type of Item
    Thesis
  • Degree
    Master of Science
  • DOI
    https://doi.org/10.7939/R34126
  • License
    This thesis is made available by the University of Alberta Libraries with permission of the copyright owner solely for non-commercial purposes. This thesis, or any portion thereof, may not otherwise be copied or reproduced without the written consent of the copyright owner, except to the extent permitted by Canadian copyright law.
  • Language
    English
  • Institution
    University of Alberta
  • Degree level
    Master's
  • Department
    • Department of Electrical and Computer Engineering
  • Specialization
    • Software Engineering and Intelligent Systems
  • Supervisor / co-supervisor and their department(s)
    • Kurgan, Lukasz (Electrical and Computer Engineering)
  • Examining committee members and their departments
    • Reformat, Marek (Electrical and Computer Engineering)
    • Tuszynski, Jack A.(Physics)