The number of publications in the field of chemical cross-linking/mass spectrometry (MS) for deriving protein 3D structures and for probing protein/protein interactions has largely increased during the last years. MS analysis of the large cross-linking data sets requires an automated data analysis by dedicated software tools, but applying scoring procedures with statistical methods does not eliminate the fundamental problems of a misassignment of cross-linked products. In fact, we have observed a significant rate of misassigned cross-links in a number of publications, mainly due to the presence of isobaric cross-linked species, an incomplete fragmentation of cross-linked products, and low-mass accuracy fragment ion data. These false assignments will eventually lead to wrong conclusions on the structural information derived from chemical cross-linking/MS experiments. In this contribution, we examine the most common sources for misassigning cross-linked products. We propose and discuss rational criteria and suggest five guidelines that might be followed for a reliable and unambiguous identification of cross-links, independent of the software used for data analysis. In the interest of the cross-linking/MS approach, it should be ensured that only high-quality data enter the structural biology literature. Clearly, there is an urgent need to define common standards for data analysis and reporting formats of cross-linked products.

To Be or Not to Be? Five Guidelines to Avoid Misassignments in Cross-Linking/Mass Spectrometry

Iacobucci C.;
2017

Abstract

The number of publications in the field of chemical cross-linking/mass spectrometry (MS) for deriving protein 3D structures and for probing protein/protein interactions has largely increased during the last years. MS analysis of the large cross-linking data sets requires an automated data analysis by dedicated software tools, but applying scoring procedures with statistical methods does not eliminate the fundamental problems of a misassignment of cross-linked products. In fact, we have observed a significant rate of misassigned cross-links in a number of publications, mainly due to the presence of isobaric cross-linked species, an incomplete fragmentation of cross-linked products, and low-mass accuracy fragment ion data. These false assignments will eventually lead to wrong conclusions on the structural information derived from chemical cross-linking/MS experiments. In this contribution, we examine the most common sources for misassigning cross-linked products. We propose and discuss rational criteria and suggest five guidelines that might be followed for a reliable and unambiguous identification of cross-links, independent of the software used for data analysis. In the interest of the cross-linking/MS approach, it should be ensured that only high-quality data enter the structural biology literature. Clearly, there is an urgent need to define common standards for data analysis and reporting formats of cross-linked products.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11697/182232
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