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

  • Author / Creator
    Chen, Ke
  • 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.

  • Subjects / Keywords
  • Graduation date
  • Type of Item
  • Degree
    Doctor of Philosophy
  • DOI
  • 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
  • Institution
    University of Alberta
  • Degree level
  • Department
    • Department of Electrical and Computer Engineering
  • Supervisor / co-supervisor and their department(s)
    • Lukasz, Kurgan (Department of Electrical and Computer Engineering)
  • Examining committee members and their departments
    • Jack, Tuszynski (Department of Oncology)
    • Daisuke, Kihara (Department of Biological and Computer Science, Purdue University)
    • Petr, Musilek (Department of Electrical and Computer Engineering)
    • Marek, Reformat (Department of Electrical and Computer Engineering)