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

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.

Predicting fragment intensities and retention time of iTRAQ‐ and TMTPro‐labeled peptides with Prosit‐TMT

“…The re-scoring was performed as described in Gabriel et al [13]. Briefly, to utilize the RT predictions, the re-scoring pipeline described in Gessulat et al [10] was extended.…”

“…Re‐scoring has been shown to hold the potential to substantially increase the confidence and number of peptide identifications [10–12]. While initial research focused on the application on unlabeled peptides, the concept was recently shown to drastically increase the sensitivity and specificity also for data utilizing tandem mass tag (TMT) labeled peptides [13]. This type of multiplexing received a lot of attention over the last years because it parallelizes the analysis of multiple samples and thus increases the scalability of bottom‐up proteomics [14, 15].…”

“…All commonly used isobaric labels [16–19] used to date utilize a similar mechanism and the tags share similar characteristics [20]. However, intensity prediction by machine learning models is only available for TMT 6/10/11‐plex and isobaric tags for relative and absolute quantitation (iTRAQ) 4‐plex accurate [4, 13].…”

“…However, intensity prediction by machine learning models is only available for TMT 6/10/11-plex and isobaric tags for relative and absolute quantitation (iTRAQ) 4-plex accurate [4,13].…”

The critical role that spectral libraries play in capturing the metabolomics community knowledge

“…On the one hand, ProteomeTools followed the traditional approach to generate a spectral library by synthesizing unique tryptic peptides from the human proteome and acquiring MS/MS data on multiple instrument platforms (Zolg et al, 2017). This was subsequently expanded to include additional tryptic peptides and modified peptides (Zolg et al, 2018), non-tryptic peptides (Wilhelm et al, 2021), and isobarically labeled peptides (Gabriel et al, 2022) to currently consist of more than one million unique synthetic peptides and over 14 million MS/MS spectra. In contrast, MassIVE-KB employed a data-driven approach towards spectral library creation by re-analyzing hundreds of millions to billions of public MS/MS spectra on the MassIVE data repository using sequence database searching (M. .…”