Prosit-TMT: Deep Learning Boosts Identification of TMT-Labeled Peptides

Wassim Gabriel, Matthew The, Daniel P. Zolg, Florian P. Bayer, Omar Shouman, Ludwig Lautenbacher, Karsten Schnatbaum, Johannes Zerweck, Tobias Knaute, Bernard Delanghe, Andreas Huhmer, Holger Wenschuh, Ulf Reimer, Guillaume Médard, Bernhard Kuster, Mathias Wilhelm

Research output: Contribution to journalArticlepeer-review

12 Scopus citations

Abstract

The prediction of fragment ion intensities and retention time of peptides has gained significant attention over the past few years. However, the progress shown in the accurate prediction of such properties focused primarily on unlabeled peptides. Tandem mass tags (TMT) are chemical peptide labels that are coupled to free amine groups usually after protein digestion to enable the multiplexed analysis of multiple samples in bottom-up mass spectrometry. It is a standard workflow in proteomics ranging from single-cell to high-throughput proteomics. Particularly for TMT, increasing the number of confidently identified spectra is highly desirable as it provides identification and quantification information with every spectrum. Here, we report on the generation of an extensive resource of synthetic TMT-labeled peptides as part of the ProteomeTools project and present the extension of the deep learning model Prosit to accurately predict the retention time and fragment ion intensities of TMT-labeled peptides with high accuracy. Prosit-TMT supports CID and HCD fragmentation and ion trap and Orbitrap mass analyzers in a single model. Reanalysis of published TMT data sets show that this single model extracts substantial additional information. Applying Prosit-TMT, we discovered that the expression of many proteins in human breast milk follows a distinct daily cycle which may prime the newborn for nutritional or environmental cues.(Figure Presented).

Original languageEnglish
Pages (from-to)7181-7190
Number of pages10
JournalAnalytical Chemistry
Volume94
Issue number20
DOIs
StatePublished - 2022

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