(7aj) Engineering Ligands to Control Protein Conformational Changes

Induced conformational changes within proteins are an integral part of signal cascades and proper cellular function; however, mutations to certain cell receptors and other functional proteins that cause irregular conformations can result in a disease state. To improve our ability to understand the mechanisms by which conformational changes influence the healthy and disease state of cells, I propose a balanced approach that utilizes engineered binding proteins, high structural resolution NMR experiments, and molecular dynamic simulations. These complementary techniques provide an efficient route for the discovery of technologically useful binding ligands as well as a deeper fundamental understanding of free energy landscapes and protein dynamics.

This research vision balances laboratory experiments and computational simulations and uniquely leverages my high-throughput protein engineering expertise (PhD in ChemE, University of Minnesota, Advisor: Ben Hackel) with the structural and biophysical techniques secured during postdoctoral training with Professor Dorothee Kern (Howard Hughes Medical Institute investigator) at Brandeis University.

An essential feature of this work will include eukaryotic surface expression of small protein libraries for the discovery of binding interactions to mammalian cell-bound target receptors. Conformational changes upon binding will be detected using GFP fused signal cascade products. Positive hits will be isolated using flow cytometry, thus enabling high-throughput selection. Deep sequencing of binding populations will elucidate the most effective binding ligand attributes necessary for modulating receptor conformation. Dynamics of lead ligands will be measured by NMR spectroscopy as well as rendered using molecular dynamic simulations to provide extensive free energy landscape information for the target receptor. Importantly, the experimentally derived results will act as a feedback loop to guide the molecular dynamic system configurations for improved prediction of conformations.

This work demonstrates an innovative approach to couple high-throughput protein discovery and evolution to protein function rather than merely binding. Merging this innovation with experimental and computational structural studies allows a uniquely broad and deep perspective – and level of control – on protein conformational impacts.

Teaching Interests:

I am eager to work with undergraduate and graduate students as well as community outreach programs to promote a better understanding of science and technology. My previous teaching experiences include tutoring general and organic chemistry and teaching assistant roles for chemical engineering unit operations senior lab, diffusion and transport phenomena, and thermodynamics. In the future, I am particularly interested in leading courses that focus on biomolecular engineering and statistical mechanics.