In recent decades, X-ray crystallography has evolved into a routine technology for solving the structures of biological macromolecules. Consistent investment by the international scientific community has ensured access to powerful synchrotron X-ray sources and high quality data analysis software supporting an exponential increase in the number of deposited structures. Such structures are essential for understanding the mechanisms of the molecules inside cells. However, they typically only capture a very limited subset of the conformations adopted by the inherently dynamic proteins and nucleic acids that power life on Earth.
To address the relative scarcity of dynamic information about macromolecules, new X-ray sources and experimental designs are rapidly developing at a number of facilities across the globe. These new technologies use short X-ray pulses to explore the dynamics of macromolecules at room temperature and atomic resolution. In the Hekstra lab, I focus on using these new X-ray sources to uncover dynamic information about enzymes. I am actively involved in preparing biological samples and helping conduct these experiments, but my primary role is algorithm development for data analysis. I am applying cutting edge concepts from Bayesian statistics and deep learning to improve data analysis for these new types of experiments. My work is supported by a faculty transition award from the Burroughs Wellcome Fund (CASI).