Revealing protein structural evolution is essential for determining its function and interaction with the environment, for understanding disease mechanisms. However, it is challenging for biophysical methods to observe structure evolution, especially at longer distances, limiting their application to few biological systems. Pulsed Dipolar Electron Spin Resonance Spectroscopy (PDS) is powerful method for obtaining such information between the distance ranges of 1 to 10 nm. In PDS, a dipolar signal is acquired from the interaction between a pair of spin labels, from which the distance distribution between them, P(r) may be obtained. Pseudo two-dimensional PDS experiments, such as Tm-filtered DEER, allow one to differentiate distance populations in inter-exchanging biomolecular systems. The reconstruction, however, of 2D distributions of distance populations using existing methods (such as Tikhonov regularization or Gaussian modeling) is challenging since relating the results of successive traces requires a priori information, which may not be readily available. To overcome this problem, we introduce the 2D Srivastava-Freed Singular Value Decomposition (2D SF-SVD) method that enables reconstruction of 2D distance distributions in a straightforward fashion, thereby permitting the accurate determination of measurable population changes for each distance. That is, the distance surface one obtains contains both the distance components transformed from the dimension of the dipolar signal and the dimension of traces, revealing changes in each distance population, and hence the structure evolution. We demonstrate this approach for the analysis of Tm-filtered DEER traces of protein A labeled with either R1 or R1p nitroxide spin labels.

Localized approach to ill-posed inversion problem to study structure evolution of proteins using pulsed dipolar spectroscopy

ACS Spring 2022

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