Mono- and Intralink Filter (Mi-Filter) To Reduce False Identifications in 
Cross-Linking Mass Spectrometry Data

Chen, Xingyu; Sailer, Carolin; Kammer, Kai Michael; Fursch, Julius; Eisele, Markus R.; Sakata, Eri; Pellarin, Riccardo; Stengel, Florian

doi:10.1021/acs.analchem.2c00494

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Mono- and Intralink Filter (Mi-Filter) To Reduce False Identifications in Cross-Linking Mass Spectrometry Data

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Eisele, Markus R.
Baumeister, Wolfgang / Molecular Structural Biology, Max Planck Institute of Biochemistry, Max Planck Society;

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Sakata, Eri
Baumeister, Wolfgang / Molecular Structural Biology, Max Planck Institute of Biochemistry, Max Planck Society;

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Citation

Chen, X., Sailer, C., Kammer, K. M., Fursch, J., Eisele, M. R., Sakata, E., et al. (2022). Mono- and Intralink Filter (Mi-Filter) To Reduce False Identifications in Cross-Linking Mass Spectrometry Data. Analytical Chemistry, 94, 17751-17756. doi:10.1021/acs.analchem.2c00494.

Cite as: https://hdl.handle.net/21.11116/0000-000C-3B82-F

Abstract

Cross-linking mass spectrometry (XL-MS) has become an indispensable tool for the emerging field of systems structural biology over the recent years. However, the confidence in individual protein-protein interactions (PPIs) depends on the correct assessment of individual inter-protein cross-links. In this article, we describe a mono-and intralink filter (mi-filter) that is applicable to any kind of cross-linking data and workflow. It stipulates that only proteins for which at least one monolink or intra-protein cross-link has been identified within a given data set are considered for an inter-protein cross-link and therefore participate in a PPI. We show that this simple and intuitive filter has a dramatic effect on different types of cross-linking data ranging from individual protein complexes over medium-complexity affinity enrichments to proteome-wide cell lysates and significantly reduces the number of false-positive identifications for inter-protein links in all these types of XL-MS data.