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Main Title: Retention time prediction using neural networks increases identifications in crosslinking mass spectrometry
Translated Title: Retentionszeitvorhersage mittels Neuronaler Netze erhöht die Identifikationen in Quervernetzungsmassenspektrometrie
Author(s): Giese, Sven H.
Sinn, Ludwig R.
Wegner, Fritz
Rappsilber, Juri
Type: Article
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 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.
Subject(s): deep learning
mass spectrometry
liquid chromatography
machine learning
protein–protein interaction networks
Issue Date: 28-May-2021
Date Available: 31-May-2021
Is Part Of: 10.14279/depositonce-12031
Language Code: en
DDC Class: 500 Naturwissenschaften und Mathematik
000 Informatik, Informationswissenschaft, allgemeine Werke
570 Biowissenschaften; Biologie
Sponsor/Funder: DFG, 390540038, EXC 2008: UniSysCat
DFG, 392923329, GRK 2473: Bioaktive Peptide - Innovative Aspekte zur Synthese und Biosynthese
TU Berlin, Open-Access-Mittel - 2021
Journal Title: Nature Communications
Publisher: Springer Nature
Volume: 12
Article Number: 3237
Publisher DOI: 10.1038/s41467-021-23441-0
EISSN: 2041-1723
TU Affiliation(s): Fak. 3 Prozesswissenschaften » Inst. Biotechnologie » FG Bioanalytik
Appears in Collections:Technische Universität Berlin » Publications

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