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Mono- and Intralink Filter (Mi-Filter) To Reduce False Identifications in Cross-Linking Mass Spectrometry Data
[Image: see text] 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-prote...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
American Chemical Society
2022
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9798375/ https://www.ncbi.nlm.nih.gov/pubmed/36510358 http://dx.doi.org/10.1021/acs.analchem.2c00494 |
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author | Chen, Xingyu Sailer, Carolin Kammer, Kai Michael Fürsch, Julius Eisele, Markus R. Sakata, Eri Pellarin, Riccardo Stengel, Florian |
author_facet | Chen, Xingyu Sailer, Carolin Kammer, Kai Michael Fürsch, Julius Eisele, Markus R. Sakata, Eri Pellarin, Riccardo Stengel, Florian |
author_sort | Chen, Xingyu |
collection | PubMed |
description | [Image: see text] 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. |
format | Online Article Text |
id | pubmed-9798375 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | American Chemical Society |
record_format | MEDLINE/PubMed |
spelling | pubmed-97983752022-12-30 Mono- and Intralink Filter (Mi-Filter) To Reduce False Identifications in Cross-Linking Mass Spectrometry Data Chen, Xingyu Sailer, Carolin Kammer, Kai Michael Fürsch, Julius Eisele, Markus R. Sakata, Eri Pellarin, Riccardo Stengel, Florian Anal Chem [Image: see text] 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. American Chemical Society 2022-12-12 2022-12-27 /pmc/articles/PMC9798375/ /pubmed/36510358 http://dx.doi.org/10.1021/acs.analchem.2c00494 Text en © 2022 The Authors. Published by American Chemical Society https://creativecommons.org/licenses/by/4.0/Permits the broadest form of re-use including for commercial purposes, provided that author attribution and integrity are maintained (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Chen, Xingyu Sailer, Carolin Kammer, Kai Michael Fürsch, Julius Eisele, Markus R. Sakata, Eri Pellarin, Riccardo Stengel, Florian Mono- and Intralink Filter (Mi-Filter) To Reduce False Identifications in Cross-Linking Mass Spectrometry Data |
title | Mono- and Intralink
Filter (Mi-Filter) To Reduce False
Identifications in Cross-Linking Mass Spectrometry Data |
title_full | Mono- and Intralink
Filter (Mi-Filter) To Reduce False
Identifications in Cross-Linking Mass Spectrometry Data |
title_fullStr | Mono- and Intralink
Filter (Mi-Filter) To Reduce False
Identifications in Cross-Linking Mass Spectrometry Data |
title_full_unstemmed | Mono- and Intralink
Filter (Mi-Filter) To Reduce False
Identifications in Cross-Linking Mass Spectrometry Data |
title_short | Mono- and Intralink
Filter (Mi-Filter) To Reduce False
Identifications in Cross-Linking Mass Spectrometry Data |
title_sort | mono- and intralink
filter (mi-filter) to reduce false
identifications in cross-linking mass spectrometry data |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9798375/ https://www.ncbi.nlm.nih.gov/pubmed/36510358 http://dx.doi.org/10.1021/acs.analchem.2c00494 |
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