ArticleAnalytical chemistry2022
Mono- and Intralink Filter (Mi-Filter) To Reduce False Identifications in Cross-Linking Mass Spectrometry Data.
Article in Analytical chemistry, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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5 citing papers in PubMed, 9 citations in OpenAlex.
- Human RNA ligase 1 as a novel regulator of ribosome function and translation under oxidative stress.Nucleic acids research · 2026Article
- Mapping amBio · 2025Article
- Rational Modification of a Cross-Linker for Improved Flexible Protein Structure Modeling.Analytical chemistry · 2025Article
- Rescuing error control in crosslinking mass spectrometry.Molecular systems biology · 2024Article
- Mass Spectrometry Structural Proteomics Enabled by Limited Proteolysis and Cross-Linking.Mass spectrometry reviewsReview
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Authors and funding
8 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
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.
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