Peroxisome proliferator-activated receptor γ (PPARγ) is associated with a wide range of diseases, including type 2 diabetes mellitus (T2D). Thiazolidinediones (TZDs) are agonists of PPARγ which have an insulin sensitizing effect, and are therefore used as a treatment for T2D. However, TZDs cause negative side effects in patients, such as weight gain, edema, and increased risk of bone fracture. Partial agonists could be an alternative to TZD-based drugs with fewer side effects. However, there is a lack of understanding of the types of PPARγ partial agonists and how they differ from full agonists. In silico techniques, like virtual screening, molecular docking, and pharmacophore modeling, allow us to determine and characterize markers of varying levels of agonism. An extensive search of the RCSB Protein Data Bank found 62 structures of PPARγ resolved with partial agonists. Cross-docking was performed and found that two PDB structures, 3TY0 and 5TWO, would be effective as receptor structures for virtual screening. By clustering known partial agonists by common pharmacophore features, we found several distinct groups of partial agonists. Interaction and pharmacophore models were created for each group of partial agonists. Virtual screening of FDA-approved compounds showed that the models were able to predict potential partial agonists of PPARγ. This study provides additional insight into the different binding modes of partial agonists of PPARγ and their characteristics. These models can be used to assist drug discovery efforts for intelligently designing novel therapeutics for T2D which have fewer negative side effects. / Master of Science in Life Sciences / The peroxisome proliferator-activated receptor γ (PPARγ) protein is associated with a wide range of diseases, including type 2 diabetes mellitus (T2D). Thiazolidinediones (TZDs) are compounds that activate PPARγ, and increase insulin sensitivity in patients with T2D. However, TZDs cause negative side effects in patients, such as weight gain, increased fluid retention, and increased risk of bone fracture. Partial agonists could be an alternative to TZD-based drugs with fewer side effects. However, there is a lack of understanding of the types of PPARγ partial agonists and how they differ from full agonists. Computational techniques allow us to investigate common features between known partial agonists. An extensive search of the RCSB Protein Data Bank found 62 structures of PPARγ which contained partial agonists. Each known partial agonist was docked into twelve complete PPARγ structures, and it was found that two structure models would be effective as receptor structures for virtual screening. A set of known partial agonists were grouped based on common chemical features, and three distinct groups of partial agonists were found. Binding criteria for each of these three groups were developed. A library of FDA-approved compounds was screened using the criteria for binding to identify potential novel partial agonists. Three potential novel partial agonists were found in the screening. This study provides additional insight into how different compounds activate PPARγ. These methods can be used to assist drug discovery efforts for intelligently designing novel therapeutics for T2D which have fewer negative side effects.
| Identifer | oai:union.ndltd.org:VTETD/oai:vtechworks.lib.vt.edu:10919/110873 |
| Date | 03 June 2022 |
| Creators | Collins, Erin Taylor |
| Contributors | Biochemistry, Brown, Anne M., Kennelly, Peter J., Lewis, Stephanie N. |
| Publisher | Virginia Tech |
| Source Sets | Virginia Tech Theses and Dissertation |
| Language | English |
| Detected Language | English |
| Type | Thesis |
| Format | ETD, application/pdf |
| Rights | Creative Commons Attribution 4.0 International, http://creativecommons.org/licenses/by/4.0/ |
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