Projects per year
Abstract
Crosslinking mass spectrometry has developed into a robust technique that is increasingly used to investigate the interactomes of organelles and cells. However, the incomplete and noisy information in the spectra limits the numbers of protein-protein interactions that can be confidently identified. Here, we successfully leverage chromatographic retention time information to aid the identification of crosslinked peptides from spectra. Our Siamese machine learning model xiRT achieves highly accurate retention time predictions of crosslinked peptides in a multi-dimensional separation of crosslinked E. coli lysate. Importantly, supplementing the search engine score with retention time features leads to a substantial increase in protein-protein interactions without affecting confidence. This approach is not limited to cell lysates and multi-dimensional separation but also improves considerably the analysis of crosslinked multiprotein complexes with a single chromatographic dimension. Retention times are a powerful complement to mass spectrometric information to increase the sensitivity of crosslinking mass spectrometry analyses.
Original language | English |
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Article number | 3237 |
Number of pages | 34 |
Journal | Nature Communications |
Volume | 12 |
DOIs | |
Publication status | Published - 28 May 2021 |
Keywords / Materials (for Non-textual outputs)
- liquid chromatography
- machine learning
- mass spectrometry
- protein–protein interaction networks
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Dive into the research topics of 'Retention time prediction using neural networks increases identifications in crosslinking mass spectrometry'. Together they form a unique fingerprint.Projects
- 3 Finished
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`Core Funding for the Wellcome Trust Centre for Cell Biology¿, Research Enrichment, Public Engagement
Tollervey, D. (Principal Investigator)
1/12/18 → 1/06/22
Project: Research
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Wellcome Centre for Cell Biology
Tollervey, D. (Principal Investigator)
1/12/16 → 1/12/21
Project: Research
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Protein structures in the context of time and space by mass spectrometry
Rappsilber, J. (Principal Investigator)
1/06/14 → 31/05/21
Project: Research