Physics Colloquium – Justin A. Trujillo

Speaker: Justin A. Trujillo, PhD Candidate – UWM Physics

Implementing Novel Data Analysis Methods to Enhance Biophysical Studies

Studying the structure, motions, and interactions of proteins as they perform their functions is greatly important in biophysics research. Time-resolved serial femtosecond crystallography (TRSFX) has been highly successful at resolving protein structures and motions. However, TR-SFX data can be challenging to analyze. To address this, we used a machine learning algorithm and applied it to simulated diffraction data for photoactive yellow protein (PYP). The results show that the machine learning method successfully recovers structural and dynamical information, demonstrating that this method can be trusted for crystallography and other complex datasets.

Another method for studying proteins is fluorescence microscopy paired with Förster resonance energy transfer (FRET). Being highly sensitive to separation distance between fluorescent molecules, FRET is a precise way to study protein interactions in cells. The quantity of interest in these studies is the FRET efficiency, which measures energy transfer via FRET between fluorescent molecules. FRET efficiencies calculated from spectrally resolved data allow for the quaternary structure of proteins to be studied using a method known as FRET spectrometry. However, due to constraints with interpreting data, this capability has not been used with time-resolved data. In the second part of this work, we show the results of implementing a novel method of extracting FRET efficiencies from time-resolved data. This new approach may expand FRET spectrometry to be compatible with time-resolved data.

Location: Kenwood IRC 2175

Time: 1:00PM – 2:30PM

Date: June 25th, 2025