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Structure-based Computer-Aided Drug Discovery: Applications for Polypharmacology and Characterizing Non-globular Regions of Proteins

Kim, Stephanie S

Abstract Details

2020, Doctor of Philosophy, Ohio State University, Biophysics.
This dissertation explored different topics of current computer-aided drug development techniques, especially focusing on one of the more widely applied structure-based drug discovery approaches. The rapid growth in computational infrastructure accompanied by continuous method development of molecular docking algorithms enabled a more efficient rational drug development method. Despite its efficiency and enhanced sampling of potential drug compounds, there still remain challenges in reducing the failure rates in drug development. In an effort to improve current computer-aided drug development techniques, the chapters in this dissertation proposed alternative methods for target identification, multi-target drug discovery, and target’s natural flexibility prediction. Chapter 2 introduced a prediction method that addresses the question raised in computational drug discovery regarding the identification of binding targets of the selected small molecules. One of the methods that detect potential off-target bindings of a compound is known as inverse docking, which applies molecular docking by virtually screening a large protein library to a query compound. Guided by our finding that the absolute docking score was a poor indication of a ligand’s protein target, we developed a novel “combined Z-score” method that used a weighted fraction of ligand and receptor-based Z-scores to identify the potential binding target of a compound. Not only did our combined Z-score method improve the prediction accuracy for identifying the true binding target, but it also allowed users to identify targets from a user-defined list of compounds and targets. Chapter 3 explored an area of computational drug discovery where limited information on the binding site was given. In this study, we aimed to simultaneously target cyclin A2 and CDK2 to increase the chance of finding modulators of DNA damage response mechanism. To have a better understanding on the target protein and also to account for protein flexibility when docking ligands, a relaxed complex scheme was implemented for sampling potential binding ligands. Additionally, chapter 3 studied a case of polypharmacology, identifying ligands that could bind to multiple binding sites by using a weighted consensus ranking method. The top selections were experimentally verified with a biochemical luminescence assay, resulting in two nanomolar and two micromolar inhibitors. The four cyclin A2-CDK2 complex inhibitors were the first reported inhibitors that were specifically designed not to target the cyclin A2-CDK2 protein-protein interface. Chapter 4 investigated a rather special type of proteins, the intrinsically disordered proteins. After recognizing the biological importance of naturally flexible disordered proteins, chapter 4 proposed an easier and more reliable approach by using Rosetta for the prediction of disordered regions in proteins. This study addressed one of the major limitations of the previous sequence-based disorder prediction methods and accounted for long-range residue interactions with Rosetta models. As a result, our RosettaResidue method outperformed other established disorder prediction tools and did not exhibit a biased prediction toward either ordered or disordered regions
Steffen Lindert (Advisor)
Jose Otero (Committee Member)
William Ray (Committee Member)
Sherwin Singer (Committee Member)
177 p.

Recommended Citations

Citations

  • Kim, S. S. (2020). Structure-based Computer-Aided Drug Discovery: Applications for Polypharmacology and Characterizing Non-globular Regions of Proteins [Doctoral dissertation, Ohio State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=osu1586984837053744

    APA Style (7th edition)

  • Kim, Stephanie. Structure-based Computer-Aided Drug Discovery: Applications for Polypharmacology and Characterizing Non-globular Regions of Proteins. 2020. Ohio State University, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=osu1586984837053744.

    MLA Style (8th edition)

  • Kim, Stephanie. "Structure-based Computer-Aided Drug Discovery: Applications for Polypharmacology and Characterizing Non-globular Regions of Proteins." Doctoral dissertation, Ohio State University, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=osu1586984837053744

    Chicago Manual of Style (17th edition)