Spin Labeling of RNA Using "Click" Chemistry for Coarse-grained Structure Determination via Pulsed Electron-electron Double Resonance Spectroscopy

利用“点击”化学对 RNA 进行自旋标记,通过脉冲电子-电子双共振光谱法测定粗粒结构

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作者:Maria F Vicino, Tobias Hett, Olav Schiemann

Abstract

Understanding the function of oligonucleotides on a molecular level requires methods for studying their structure, conformational changes, and internal dynamics. Various biophysical methods exist to achieve this, including the whole toolbox of Electron Paramagnetic Resonance (EPR or ESR) spectroscopy. An EPR method widely used in this regard is Pulsed Electron-Electron Double Resonance (PELDOR or DEER), which provides distances in the nanometer range between electron spins in biomolecules with Angstrom precision, without restriction to the size of the biomolecule, and in solution. Since oligonucleotides inherently do not contain unpaired electrons, these have to be introduced in the form of so-called spin labels. Firstly, this protocol describes how nitroxide spin labels can be site-specifically attached to oligonucleotides using "Click" chemistry. The reaction provides little byproducts, high yields, and is conveniently performed in aqueous solution. Secondly, the protocol details how to run the PELDOR experiment, analyze the data, and derive a coarse-grained structure. Here, emphasis is placed on the pitfalls, requirements for a good dataset, and limits of interpretation; thus, the protocol gives the user a guideline for the whole experiment i.e., from spin labeling, via the PELDOR measurement and data analysis, to the final coarse-grained structure. Graphical abstract: Schematic overview of the workflow described in this protocol: First, the spin-labeling of RNA is described, which is performed as a "Click"-reaction between the alkyne-functionalized RNA strand and the azide group of the spin label. Next, step-by-step instructions are given for setting up PELDOR/DEER distance measurements on the labeled RNA, and for data analysis. Finally, guidelines are provided for building a structural model from the previously analyzed data.

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