Refining Spin–Spin Distance Distributions in Complex Biological Systems Using Multi-Gaussian Monte Carlo Analysis

Abstract

Pulse dipolar electron paramagnetic resonance spectroscopy provides means of distance measurements in the range of ~ 1.5–10 nm between two spin labels tethered to a biological system. However, the extraction of distance distribution between spin labels is an ill-posed mathematical problem. The most common approach for obtaining distance distribution employs Tikhonov regularization method, where a regularization parameter characterizing the smoothness of distribution is introduced. However, in case of multi-modal distance distributions with peaks of different widths, the use of a single regularization parameter might lead to certain distortions of actual distribution shapes. Recently, a multi-Gaussian Monte Carlo approach was proposed for eliminating this drawback and verified for model biradicals [1]. In the present work, we for the first time test this approach on complicated biological systems exhibiting multi-modal distance distributions. We apply multi-Gaussian analysis to pulsed electron–electron double resonance data of supramolecular ribosomal complexes, where the 11-mer oligoribonucleotide (MR) bearing two nitroxide labels at its termini is used as a reporter. Calculated distance distributions reveal the same conformations of MR as those obtained by Tikhonov regularization, but feature the peaks having different widths, which leads to a better resolution in several cases. The advantages, complications, and further perspectives of application of Monte-Carlo-based multi-Gaussian approach to real biological systems are discussed.