Graduation Year
2021
Document Type
Thesis
Degree
M.S.
Degree Name
Master of Science (M.S.)
Degree Granting Department
Chemical Engineering
Major Professor
Lawrence A. Stern, Ph.D.
Committee Member
Ramon Gonzalez, Ph.D.
Committee Member
David Simmons, Ph.D.
Keywords
Computational Protein Engineering, Immunotherapy, Protein Engineering, TGF-β, Yeast Surface Display
Abstract
TGF-β is an important protein for regulation of the immune system, and has been linked to promotion of tumor progression and cancer growth. Inhibiting TGF-β has been shown to be an effective therapeutic technique for fighting multiple cancer types. Engineering TGF-β’s natural inhibitor, Latency Associated Peptide (LAP), to improve its biophysical properties has potential to increase therapeutic efficacy.
Rosetta and FoldX can be used in concert to engineer stabilizing mutations of proteins. In accordance with this, PyRosetta and FoldX were used to predict stabilizing mutations of LAP. The most stabilizing mutations from each program were combined with mutually predicted mutations. A library of 255 mutations on 47 residues was created. In addition, PyRosetta was used to predict amenable mutations for binding affinity to TGF-β. This was also converted into a mutational library for analysis.
LAP was able to be expressed on the surface of Sacchromyces cerevesiae in enough quantity to dimerize and be functional. Thermal denaturation was performed, and the melting point of yeast expressed LAP was found to be 62.3 ± 0.6 °C. Additionally, yeast expressed LAP was shown to be able to bind to TGF-β via flow cytometry.
Scholar Commons Citation
Stephens, Johnny Will, "Mutational Analysis of Latency Associated Peptide for Therapeutic Application" (2021). USF Tampa Graduate Theses and Dissertations.
https://digitalcommons.usf.edu/etd/9720
