Usage
  • 21 views
  • 43 downloads

Computational Prediction of Strand Residues from Protein Sequences

  • Author / Creator
    Kedarisetti, Kanaka Durga
  • Accurately identifying strand residues (beta-residues) from protein sequences aids prediction and analysis of numerous structural and functional aspects of proteins. This thesis is focused on improving sequence-based prediction of strand residues and strands, which in turn would lead to better recognition of β-sheets (arrangements of multiple strands). We developed a novel ensemble-based predictor, BETArPRED, achieving a statistically significant performance improvement over existing, state-of-the-art secondary structure predictors. Our method improves prediction of strand residues and strands, and it also finds strands that were missed by the other methods. When compared with the top-performing three dimensional structure predictor, our BETArPRED improves predictions of strands and provides more correct predictions of strand residues, while the other predictor achieves higher rate of correct strand residue predictions when under-predicting strands. Next, we investigate strand residue-residue pair propensities incorporating long-range interactions, and a scoring function that uses these propensities. This scoring function is empirically shown to differentiate between strand and non-strand residues. We study the effect of residue conservation and directionality of strands in beta-sheets on these propensities, and conclude that they provide little to no further improvement. We also compare our pair propensities with other recently proposed relative frequency-based pair propensities, and find that our pair propensities provide better discriminatory power in judging a residue from a strand to a non-strand. These proposed pair propensities could be used to further improve the sequence-based beta-residue predictors.

  • Subjects / Keywords
  • Graduation date
    2012-06
  • Type of Item
    Thesis
  • Degree
    Doctor of Philosophy
  • DOI
    https://doi.org/10.7939/R30S3X
  • 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
    Doctoral
  • Department
    • Department of Electrical and Computer Engineering
  • Specialization
    • Software Engineering and Intelligent Systems
  • Supervisor / co-supervisor and their department(s)
    • Dick, Scott (Department of Electrical and Computer Engineering)
    • Kurgan, Lukasz (Department of Electrical and Computer Engineering)
  • Examining committee members and their departments
    • Gromiha, M Michael (IIT Madras)
    • Reformat, Marek (Department of Electrical and Computer Engineering)
    • Tuszynski, Jack (Physics/Oncology)