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In-silico characterization and prediction of protein-small ligand interactions Open Access


Other title
protein function annotation
binding site
Type of item
Degree grantor
University of Alberta
Author or creator
Chen, Ke
Supervisor and department
Lukasz, Kurgan (Department of Electrical and Computer Engineering)
Examining committee member and department
Marek, Reformat (Department of Electrical and Computer Engineering)
Daisuke, Kihara (Department of Biological and Computer Science, Purdue University)
Petr, Musilek (Department of Electrical and Computer Engineering)
Jack, Tuszynski (Department of Oncology)
Department of Electrical and Computer Engineering

Date accepted
Graduation date
Doctor of Philosophy
Degree level
Proteins, which participate in virtually every process within cells, implement many of their functions through interactions with various ligands. Although a substantial effort in characterization and prediction of protein-ligand interactions was observed in the past two decades, these subjects remain far from completion. This dissertation focuses on computational (in-silico) analysis and prediction of the protein-small ligand interactions, with particular emphasis on the protein-nucleotide interactions. We start by analyzing regularities, referred to as the interaction patterns, in the atomic-level protein-small ligand interactions which lead to the discovery of ten interaction patterns that cover majority of the known interactions. The discovery of these interaction patterns demonstrates that protein-ligand interactions can be predicted for a given protein. Next, we performed an extensive comparative analysis of the predictive performance of ten representative methods that predict binding residues and binding sites for small organic ligands. Our results reveal that although the predictive quality of these methods was significantly improved during the past decade, there is still a large room for further improvements, particularly when predicting for certain types of the organic compounds. We also found a few limitations of the existing methods which motivate the development of new predictors of the protein-small organic ligand interactions. Consequently, we proposed two methods that address prediction of the protein-nucleotide interactions. We selected nucleotides from among the organic compounds because they are highly abundant and ubiquitous (they are involved in a wide range of biological processes), and thus they constitute an important and challenging problem. The first method predicts nucleotide binding residues from protein sequences, and the second method identifies the binding sites from protein structures. We empirically demonstrate that both, the sequence-based and the structure-based, methods significantly improve predictions over the existing state-of-the-art solutions. Our study aims to help with the characterization and annotation of biological functions of proteins and elucidation of the molecular-level mechanisms of cellular activities, and it provides tools that can be used to implement improved molecular-docking based rational drug discovery protocols.
License granted by Ke Chen ( on 2011-09-27T17:46:50Z (GMT): 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 the above terms. The author reserves all other publication and other rights in association with the copyright in the thesis, and except as herein 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.
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