Scientific Talk – Making the most of sparse data

Scientific Talk – Making the most of sparse data

We cordially invite you to an online seminar by:

Diego del Alamo, Vanderbilt University, Nashville, USA, on September 22nd, 4 p.m. CEST (16:00 Uhr Leipzig).

“Making the most of sparse data: Modeling the glutamate-GABA antiporter using double electron-electron resonance spectroscopy data”

The glutamate-GABA antiporter GadC is a 52 kDa membrane transporter protein that contributes to acid resistance in pathogenic bacteria such as Listeria monocytogenes and Shigella flexneri. Because the only published structure was determined crystallographically under conditions where it is inactive, the low-pH active conformation remains unknown. The study of this protein is challenged by the fact that its size precludes either traditional solution-state NMR or cryo-EM. I therefore used double electron-electron resonance (DEER) spectroscopy to monitor the protein at both high and low pH. These distance distributions, measured in lipid nanodiscs, suggest that GadC does not sample the conformation that was crystallographically resolved. Although I wanted to generate a structural model of GadC at both high and low pH, the sparseness of the data (approximately one restraint per twenty residues) presented a challenge that could only be addressed with novel sampling and scoring methods. In this presentation I will discuss these methods and how they could allow researchers to more effectively leverage sparse distance data collected using DEER to model conformational changes in integral membrane proteins. The sampling strategy I developed combines six-dimensional rigid-body transformations of secondary structural elements with fragment insertion, while the improvements in scoring focused on developing more realistic and statistically sound scoring functions. Both methods have the potential to widen the range and improve the accuracy of conformational changes that can be modeled using DEER spectroscopy.

public, for interested parties

via Zoom

Meeting-ID: 707 142 249 // Kenncode: 439414