Supercharged Models of Intrinsically Disordered Proteins and Their Utility in Sensing

Author

Peter J. Schnatz, The Graduate Center, City University of New YorkFollow

Date of Degree

9-2018

Document Type

Dissertation

Degree Name

Ph.D.

Program

Physics

Advisor

Ronald L. Koder

Committee Members

Scott Banta

Shana Elbaum-Garfinkle

Marilyn Gunner

Sharon Loverde

Raymond Tu

Subject Categories

Biochemistry | Biological and Chemical Physics | Biophysics | Biotechnology | Other Biochemistry, Biophysics, and Structural Biology

Keywords

Intrinsically disordered proteins, protein design, supercharging, allostery, ligand-induced folding, biosensing

Abstract

In this thesis I show that greatly increasing the magnitude of a protein’s net charge using surface supercharging transforms that protein into a ligand-gated or counterion-gated conformational molecular switch. To demonstrate this I first modified the designed helical bundle hemoprotein H4 using simple molecular modeling, creating a highly charged protein which both unfolds reversibly at low ionic strength and undergoes the ligand-induced folding transition commonly observed in signal transduction by intrinsically disordered proteins in biology. Due to the high surface charge density, ligand binding to this protein is allosterically activated by low concentrations of divalent cations and the polyamine spermine. To demonstrate this process further using a natural protein, I similarly modified E. coli cytochrome b₅₆₂ and the resulting protein behaves in a like manner. These simple model systems allow us to derive and then experimentally validate a mass-action model for coupled folding and binding behavior of ligand-gated conformational switches, establishing a set of general engineering principles which can be used to convert natural and designed proteins into allosteric molecular switches useful in biodesign, synthetic biology, and sensing.

Recommended Citation

Schnatz, Peter J., "Supercharged Models of Intrinsically Disordered Proteins and Their Utility in Sensing" (2018). CUNY Academic Works.
https://academicworks.cuny.edu/gc_etds/2811