Redesigning error control in cross-linking mass spectrometry enables more robust and sensitive protein-protein interaction studies
Abstract Cross-linking mass spectrometry (XL-MS) allows characterizing protein-protein interactions (PPIs) in native biological systems by capturing cross-links between different proteins (inter-links). However, inter-link identification remains challenging, requiring dedicated data filtering scheme...
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| Format: | Article |
| Language: | English |
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Springer Nature
2024-12-01
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| Series: | Molecular Systems Biology |
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| Online Access: | https://doi.org/10.1038/s44320-024-00079-w |
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| author | Boris Bogdanow Max Ruwolt Julia Ruta Lars Mühlberg Cong Wang Wen-feng Zeng Arne Elofsson Fan Liu |
| author_facet | Boris Bogdanow Max Ruwolt Julia Ruta Lars Mühlberg Cong Wang Wen-feng Zeng Arne Elofsson Fan Liu |
| author_sort | Boris Bogdanow |
| collection | DOAJ |
| description | Abstract Cross-linking mass spectrometry (XL-MS) allows characterizing protein-protein interactions (PPIs) in native biological systems by capturing cross-links between different proteins (inter-links). However, inter-link identification remains challenging, requiring dedicated data filtering schemes and thorough error control. Here, we benchmark existing data filtering schemes combined with error rate estimation strategies utilizing concatenated target-decoy protein sequence databases. These workflows show shortcomings either in sensitivity (many false negatives) or specificity (many false positives). To ameliorate the limited sensitivity without compromising specificity, we develop an alternative target-decoy search strategy using fused target-decoy databases. Furthermore, we devise a different data filtering scheme that takes the inter-link context of the XL-MS dataset into account. Combining both approaches maintains low error rates and minimizes false negatives, as we show by mathematical simulations, analysis of experimental ground-truth data, and application to various biological datasets. In human cells, inter-link identifications increase by 75% and we confirm their structural accuracy through proteome-wide comparisons to AlphaFold2-derived models. Taken together, target-decoy fusion and context-sensitive data filtering deepen and fine-tune XL-MS-based interactomics. |
| format | Article |
| id | doaj-art-daf4c3e09c454cda82ee29eb7daa0a13 |
| institution | Kabale University |
| issn | 1744-4292 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Springer Nature |
| record_format | Article |
| series | Molecular Systems Biology |
| spelling | doaj-art-daf4c3e09c454cda82ee29eb7daa0a132025-01-05T12:50:44ZengSpringer NatureMolecular Systems Biology1744-42922024-12-012119010610.1038/s44320-024-00079-wRedesigning error control in cross-linking mass spectrometry enables more robust and sensitive protein-protein interaction studiesBoris Bogdanow0Max Ruwolt1Julia Ruta2Lars Mühlberg3Cong Wang4Wen-feng Zeng5Arne Elofsson6Fan Liu7Research group “Structural Interactomics”, Leibniz Forschungsinstitut für Molekulare PharmakologieResearch group “Structural Interactomics”, Leibniz Forschungsinstitut für Molekulare PharmakologieResearch group “Structural Interactomics”, Leibniz Forschungsinstitut für Molekulare PharmakologieResearch group “Structural Interactomics”, Leibniz Forschungsinstitut für Molekulare PharmakologieResearch group “Structural Interactomics”, Leibniz Forschungsinstitut für Molekulare PharmakologieDepartment of Proteomics and Signal Transduction, Max Planck Institute of BiochemistryStockholm Bioinformatics Center, Stockholm UniversityResearch group “Structural Interactomics”, Leibniz Forschungsinstitut für Molekulare PharmakologieAbstract Cross-linking mass spectrometry (XL-MS) allows characterizing protein-protein interactions (PPIs) in native biological systems by capturing cross-links between different proteins (inter-links). However, inter-link identification remains challenging, requiring dedicated data filtering schemes and thorough error control. Here, we benchmark existing data filtering schemes combined with error rate estimation strategies utilizing concatenated target-decoy protein sequence databases. These workflows show shortcomings either in sensitivity (many false negatives) or specificity (many false positives). To ameliorate the limited sensitivity without compromising specificity, we develop an alternative target-decoy search strategy using fused target-decoy databases. Furthermore, we devise a different data filtering scheme that takes the inter-link context of the XL-MS dataset into account. Combining both approaches maintains low error rates and minimizes false negatives, as we show by mathematical simulations, analysis of experimental ground-truth data, and application to various biological datasets. In human cells, inter-link identifications increase by 75% and we confirm their structural accuracy through proteome-wide comparisons to AlphaFold2-derived models. Taken together, target-decoy fusion and context-sensitive data filtering deepen and fine-tune XL-MS-based interactomics.https://doi.org/10.1038/s44320-024-00079-wCross-linking Mass SpectrometryFalse-Discovery RateProteomicsStructure ModelingError Control |
| spellingShingle | Boris Bogdanow Max Ruwolt Julia Ruta Lars Mühlberg Cong Wang Wen-feng Zeng Arne Elofsson Fan Liu Redesigning error control in cross-linking mass spectrometry enables more robust and sensitive protein-protein interaction studies Molecular Systems Biology Cross-linking Mass Spectrometry False-Discovery Rate Proteomics Structure Modeling Error Control |
| title | Redesigning error control in cross-linking mass spectrometry enables more robust and sensitive protein-protein interaction studies |
| title_full | Redesigning error control in cross-linking mass spectrometry enables more robust and sensitive protein-protein interaction studies |
| title_fullStr | Redesigning error control in cross-linking mass spectrometry enables more robust and sensitive protein-protein interaction studies |
| title_full_unstemmed | Redesigning error control in cross-linking mass spectrometry enables more robust and sensitive protein-protein interaction studies |
| title_short | Redesigning error control in cross-linking mass spectrometry enables more robust and sensitive protein-protein interaction studies |
| title_sort | redesigning error control in cross linking mass spectrometry enables more robust and sensitive protein protein interaction studies |
| topic | Cross-linking Mass Spectrometry False-Discovery Rate Proteomics Structure Modeling Error Control |
| url | https://doi.org/10.1038/s44320-024-00079-w |
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