| Literature DB >> 27997545 |
Juan D Chavez1, Jimmy K Eng1, Devin K Schweppe1, Michelle Cilia2,3,4, Keith Rivera5, Xuefei Zhong1, Xia Wu1, Terrence Allen6, Moshe Khurgel6, Akhilesh Kumar6, Athanasios Lampropoulos6, Mårten Larsson6, Shuvadeep Maity6, Yaroslav Morozov6, Wimal Pathmasiri6, Mathew Perez-Neut6, Coriness Pineyro-Ruiz6, Elizabeth Polina6, Stephanie Post6, Mark Rider6, Dorota Tokmina-Roszyk6, Katherine Tyson6, Debora Vieira Parrine Sant'Ana6, James E Bruce1.
Abstract
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.Entities:
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Year: 2016 PMID: 27997545 PMCID: PMC5172568 DOI: 10.1371/journal.pone.0167547
Source DB: PubMed Journal: PLoS One ISSN: 1932-6203 Impact factor: 3.240