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PLoS One. 2016 Dec 20;11(12):e0167547. doi: 10.1371/journal.pone.0167547. eCollection 2016.

A General Method for Targeted Quantitative Cross-Linking Mass Spectrometry.

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Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, United States of America.
Boyce Thompson Institute for Plant Research, Ithaca, NY, United States of America.
USDA-Agricultural Research Service, Ithaca, NY, United States of America.
Department of Plant Pathology and Plant-Microbe Biology, Cornell University, Ithaca, NY, United States of America.
Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, United States of America.
Cold Spring Harbor Laboratory Proteomics Course 2016, Cold Spring Harbor, NY, United States of America.


Chemical cross-linking mass spectrometry (XL-MS) provides protein structural information by identifying covalently linked proximal amino acid residues on protein surfaces. The information gained by this technique is complementary to other structural biology methods such as x-ray crystallography, NMR and cryo-electron microscopy[1]. The extension of traditional quantitative proteomics methods with chemical cross-linking can provide information on the structural dynamics of protein structures and protein complexes. The identification and quantitation of cross-linked peptides remains challenging for the general community, requiring specialized expertise ultimately limiting more widespread adoption of the technique. We describe a general method for targeted quantitative mass spectrometric analysis of cross-linked peptide pairs. We report the adaptation of the widely used, open source software package Skyline, for the analysis of quantitative XL-MS data as a means for data analysis and sharing of methods. We demonstrate the utility and robustness of the method with a cross-laboratory study and present data that is supported by and validates previously published data on quantified cross-linked peptide pairs. This advance provides an easy to use resource so that any lab with access to a LC-MS system capable of performing targeted quantitative analysis can quickly and accurately measure dynamic changes in protein structure and protein interactions.

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