Please use this identifier to cite or link to this item: http://dx.doi.org/10.14279/depositonce-11957
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dc.contributor.authorGiese, Sven H.-
dc.contributor.authorSinn, Ludwig R.-
dc.contributor.authorWegner, Fritz-
dc.contributor.authorRappsilber, Juri-
dc.date.accessioned2021-05-31T14:18:33Z-
dc.date.available2021-05-31T14:18:33Z-
dc.date.issued2021-05-28-
dc.identifier.urihttps://depositonce.tu-berlin.de/handle/11303/13163-
dc.identifier.urihttp://dx.doi.org/10.14279/depositonce-11957-
dc.description.abstractCrosslinking 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 mass spectra of crosslinked peptides limits the numbers of protein–protein interactions that can be confidently identified. Here, we leverage chromatographic retention time information to aid the identification of crosslinked peptides from mass 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.en
dc.description.sponsorshipDFG, 390540038, EXC 2008: UniSysCaten
dc.description.sponsorshipDFG, 392923329, GRK 2473: Bioaktive Peptide - Innovative Aspekte zur Synthese und Biosyntheseen
dc.description.sponsorshipTU Berlin, Open-Access-Mittel - 2021en
dc.language.isoenen
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en
dc.subject.ddc500 Naturwissenschaften und Mathematikde
dc.subject.ddc000 Informatik, Informationswissenschaft, allgemeine Werkede
dc.subject.ddc570 Biowissenschaften; Biologiede
dc.subject.otherdeep learningen
dc.subject.otherproteomicsen
dc.subject.othermass spectrometryen
dc.subject.otherliquid chromatographyen
dc.subject.othermachine learningen
dc.subject.otherprotein–protein interaction networksen
dc.titleRetention time prediction using neural networks increases identifications in crosslinking mass spectrometryen
dc.typeArticleen
tub.accessrights.dnbfreeen
tub.publisher.universityorinstitutionTechnische Universität Berlinen
dc.identifier.eissn2041-1723-
dc.title.translatedRetentionszeitvorhersage mittels Neuronaler Netze erhöht die Identifikationen in Quervernetzungsmassenspektrometriede
dc.type.versionpublishedVersionen
dcterms.bibliographicCitation.doi10.1038/s41467-021-23441-0en
dcterms.bibliographicCitation.journaltitleNature Communicationsen
dcterms.bibliographicCitation.originalpublisherplaceLondonen
dcterms.bibliographicCitation.volume12en
dcterms.bibliographicCitation.originalpublishernameSpringer Natureen
dcterms.bibliographicCitation.articlenumber3237en
dc.identifier.pmid34050149-
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