Abstract
Protein oxidative ‘footprinting’— where obtaining structural information of proteins using hydroxyl radical labeling is detected with bottom-up proteomics mass spectrometry analysis—has evolved from an emerging technology to an approach applied to a range of structural biology and biophysics questions. Whether the hydroxyl radicals are generated from hydrogen peroxide (via photolysis, Fenton chemistry, or electrochemistry) or directly from water (through X-ray irradiation, plasma, or gamma rays), the resulting structural information is irreversibly encoded within the protein side chains and can be detected and quantitated with up to single-residue resolution, using a commonly applied set of LC-MS data reduction and analysis protocols. Quantitative measurements of changes in the oxidative labeling of amino acid side chain reflect alterations in solvent accessibility, typically resulting from protein-protein interactions, ligand binding, protein folding/conformational changes, or applied stress. Measuring changes in oxidative labeling thus provides a detailed map of structural changes and interaction sites across the system under study. Oxidative footprinting technologies over the past decade have demonstrated their utility as a mapping method for protein structure analysis in solution and within living cells. The goal of this article is to harmonize and disseminate best practices for experimental design, data collection, data interpretation, and data presentation. This perspective intends to facilitate the application of this insightful approach to new biological systems and experimental applications. The information presented in this article coalesces best practices independently developed by teams who have decades of experience in advancing protein oxidative footprinting. We provide a consensus viewpoint on the use of this technology and the different methodologies for structural elucidation in both academic research and in biopharmaceutical drug discovery and development.
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More detailed descriptions of best practices in data analysis and statistical analysis
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