Structural biology uncovers life's secrets by studying protein structures via techniques like X-ray crystallography. This knowledge drives advancements in protein engineering for the improvement of human lives. Yet, obtaining high-quality crystals in X-ray crystallography is challenging. To overcome this, we used Translocation ETS Leukemia protein Sterile Alpha Motif domain (TELSAM), a promising polymer-forming crystallization chaperone (PFCC), to enhance protein crystallization. Human thirty-eight-negative kinase-1 (TNK1), a key player in cancer progression, possess a ubiquitin association (UBA) domain that binds polyubiquitin and regulates TNK1 activity and stability. Although sequence analysis hints at an unconventional TNK1 UBA domain architecture, its molecular structure lacks experimental validation. To gain insight into TNK1 regulation, we fused the UBA domain to the 1TEL crystallization chaperone and obtained crystals diffracting as far as 1.53 Ã…. 1TEL enabled solution of the X-ray phases. GG and GSGG linkers allowed the UBA to reproducibly find a productive binding mode against its 1TEL polymer and to crystallize at protein concentrations as low as 0.1 mg/mL. Our findings support a TELSAM fusion crystallization mechanism, highlighting fewer crystal contacts compared to traditional crystals. Both modeling and experimental validation indicate that the UBA domain exhibits selectivity towards polyubiquitin chain length and linkages. Radical S-adenosylmethionine (SAM) enzymes catalyze various radical-mediated substrate transformations. Despite the growing interest of computational enzyme design in industrial small molecule synthesis, radical SAM enzymes remain relatively unexplored. We used PyRosetta to leverage hydrogen bonding design (hbDes) and hydrophobic interaction design (hpDes) to enable a radical cyclization reaction on our selected substrate. Although the purified enzymes demonstrated activation potential with a reducing agent, enzymatic assays failed to exhibit activity against the reactant. To obtain successful results, addressing additional questions and issues is required, which may involve the implementation of machine learning.
| Identifer | oai:union.ndltd.org:BGMYU2/oai:scholarsarchive.byu.edu:etd-11153 |
| Date | 10 August 2023 |
| Creators | Tseng, Yi-Jie |
| Publisher | BYU ScholarsArchive |
| Source Sets | Brigham Young University |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Theses and Dissertations |
| Rights | https://lib.byu.edu/about/copyright/ |
Page generated in 0.0019 seconds