Small molecules are powerful tools to probe biological systems and cure diseases. In the scope of this dissertation, small molecules were applied to study three distinct disease models: cancer, Sedaghatian-type spondylometaphyseal dysplasia (SSMD), and COVID-19. First, encouraged by the recently reported vulnerability of drug-resistant, metastatic cancers to GPX4 (Glutathione Peroxidase 4) inhibition, we examined the basis for nanomolar potency of proof-of-concept GPX4 inhibitors, which revealed an unexpected allosteric binding site. Through hierarchical screening of a lead-optimized compound library, we identified novel small molecules binding to this allosteric site. Second, a homozygous point mutation in the GPX4 gene was identified in three living patients with SSMD. With a structure-based analysis and cell models of the patient-derived variant, we found that the missense variant significantly changed the protein structure and caused substantial loss of enzymatic function. Proposed proof-of-concept treatments were subsequentially validated in patient fibroblasts. Our further structural investigation into the origin of the reduced enzymatic activity revealed a key residue modulating GPX4 enzymatic function. We also found that the variant alters the degradation of GPX4, unveiling the native degradation mechanism of GPX4 protein. Third, driven by the recent urgent need for COVID-19 antiviral therapeutics, we utilized the conservation of 3CL protease substrate-binding pockets across coronaviruses to identify four structurally divergent lead compounds that inhibit SARS-CoV-2 3CL protease. With structure-based optimization, we ultimately identified drug-like compounds with < 10 nM potency for inhibiting the SARS-CoV-2 3CL protease and blocking SARS-CoV-2 replication in human cells.
| Identifer | oai:union.ndltd.org:columbia.edu/oai:academiccommons.columbia.edu:10.7916/d8-26na-1487 |
| Date | January 2021 |
| Creators | Liu, Hengrui |
| Source Sets | Columbia University |
| Language | English |
| Detected Language | English |
| Type | Theses |
Page generated in 0.0018 seconds