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Permanent link (DOI): https://doi.org/10.7939/R30S3X

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Computational Prediction of Strand Residues from Protein Sequences Open Access

Descriptions

Other title
Subject/Keyword
beta strand
Protein Secondary Structure
strand residues
Prediction
Type of item
Thesis
Degree grantor
University of Alberta
Author or creator
Kedarisetti, Kanaka Durga
Supervisor and department
Dick, Scott (Department of Electrical and Computer Engineering)
Kurgan, Lukasz (Department of Electrical and Computer Engineering)
Examining committee member and department
Reformat, Marek (Department of Electrical and Computer Engineering)
Gromiha, M Michael (IIT Madras)
Tuszynski, Jack (Physics/Oncology)
Department
Department of Electrical and Computer Engineering
Specialization
Software Engineering and Intelligent Systems
Date accepted
2012-03-19T12:10:00Z
Graduation date
2012-06
Degree
Doctor of Philosophy
Degree level
Doctoral
Abstract
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.
Language
English
DOI
doi:10.7939/R30S3X
Rights
Permission is hereby granted to the University of Alberta Libraries to reproduce single copies of this thesis and to lend or sell such copies for private, scholarly or scientific research purposes only. Where the thesis is converted to, or otherwise made available in digital form, the University of Alberta will advise potential users of the thesis of these terms. The author reserves all other publication and other rights in association with the copyright in the thesis and, except as herein before provided, neither the thesis nor any substantial portion thereof may be printed or otherwise reproduced in any material form whatsoever without the author's prior written permission.
Citation for previous publication
Kedarisetti KD, Mizianty M, Dick S and Kurgan L, 2011, Improved sequence-based prediction of strand residues, Journal of Bioinformatics and Computational Biology, 9, Issue 1, Pages 67-89.

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