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

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.