Statistical analysis of ENDOR spectra
2021 | journal article; research paper. A publication with affiliation to the University of Göttingen.
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- Authors
- Pokern, Yvo; Eltzner, Benjamin ; Huckemann, Stephan F. ; Beeken, Clemens; Stubbe, JoAnne; Tkach, Igor; Bennati, Marina ; Hiller, Markus
- Abstract
- Electron–nuclear double resonance (ENDOR) measures the hyperfine interaction of magnetic nuclei with paramagnetic centers and is hence a powerful tool for spectroscopic investigations extending from biophysics to material science. Progress in microwave technology and the recent availability of commercial electron paramagnetic resonance (EPR) spectrometers up to an electron Larmor frequency of 263 GHz now open the opportunity for a more quantitative spectral analysis. Using representative spectra of a prototype amino acid radical in a biologically relevant enzyme, the Y 122 • in Escherichia coli ribonucleotide reductase, we developed a statistical model for ENDOR data and conducted statistical inference on the spectra including uncertainty estimation and hypothesis testing. Our approach in conjunction with 1 H/ 2 H isotopic labeling of Y 122 • in the protein unambiguously established new unexpected spectral contributions. Density functional theory (DFT) calculations and ENDOR spectral simulations indicated that these features result from the beta-methylene hyperfine coupling and are caused by a distribution of molecular conformations, likely important for the biological function of this essential radical. The results demonstrate that model-based statistical analysis in combination with state-of-the-art spectroscopy accesses information hitherto beyond standard approaches.
- Issue Date
- 2021
- Journal
- Proceedings of the National Academy of Sciences
- Project
- SFB 1456 | Cluster A | A01: Geometric and Bayesian statistics to reconstruct protein radical structures from ENDOR spectroscopy
SFB 1456 | Cluster A: Data with Geometric Nonlinearities
SFB 1456: Mathematik des Experiments: Die Herausforderung indirekter Messungen in den Naturwissenschaften - ISSN
- 0027-8424
- eISSN
- 1091-6490
- Language
- English