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.

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