Events & Calendars Chemistry Seminar - Student Thesis Talks

Kaeli Mathias '18 and John Strahan '18 are both working with Professor Sheila Jaswal.

Kaeli Mathias will discuss "Investigation of Acetonitrile as a Mass-Spectrometry Compatible Denaturant." Biological function is highly reliant on the structure, folding and stability of proteins in the appropriate cellular conditions. Traditional methods to study folding and stability map a protein’s free energy landscape using harsh denaturants to perturb the equilibrium between the native and unfolded states, enabling measurement of unfolding kinetics and thermodynamics. The Jaswal lab has developed a method, hydrogen exchange mass spectrometry (HXMS,) that can map protein landscapes under much more physiological conditions, requiring little to no denaturant. This project focuses on validating the organic solvent acetonitrile as a mild denaturant. Mathias '18 is monitoring the unfolding as a function of acetonitrile concentration of protein L, a well-characterized two state protein, using tryptophan fluorescence and HXMS. These experiments will help establish the scope of Acetonitrile in enabling HXMS mapping of protein folding landscapes.

John Strahan will discuss "Modeling Hydrogen Exchange with Gillespie Algorithm Simulations and Approximate Bayesian Computation." Hydrogen exchange mass spectrometry has recently emerged as a useful tool for studying protein folding mechanisms and free energy landscapes. The underlying kinetics of the exchange process are complicated and resistant to simple analytical treatment when the protein is under native conditions. In previous work, members of the Jaswal Lab have developed a stochastic simulation using the Gillespie algorithm to calculate the mass spectrum as a function of time, and then fit this simulation to experimental data using a least squares scheme. In the present work, Strahan '18 refines and extends this analysis by using Bayesian Computation, which allows for determination of distributions of kinetic parameters which are consistent with the observed experimental data. From this information, he can estimate parameter means and variances, as well as correlations between parameters.