Prosit: proteome-wide prediction of peptide tandem mass spectra by deep learning
Gessulat, S., Schmidt, T., Zolg, D. P., Samaras, P., Schnatbaum, K., Zerweck, J., Knaute, T., Rechenberger, J., Delanghe, B., Huhmer, A., Reimer, U., Ehrlich, H. C., Aiche, S., Kuster, B., and Wilhelm, M. (2019). Nat Methods 16, 509-518. doi: 10.1038/s41592-019-0426-7
Abstract:
In
mass-spectrometry-based proteomics, the identification and quantification of peptides and proteins heavily rely on sequence database searching or spectral library matching. The lack of accurate predictive models for fragment ion intensities impairs the realization of the full potential of these approaches. Here, we extended the ProteomeTools synthetic
peptide library to 550,000 tryptic peptides and 21 million high-quality
tandem mass spectra. We trained a
deep neural network, termed
Prosit, resulting in chromatographic retention time and fragment ion intensity predictions that exceed the quality of the experimental data. Integrating
Prosit into database search pipelines led to more identifications at >10× lower false discovery rates. We show the general applicability of
Prosit by predicting
spectra for proteases other than trypsin, generating spectral libraries for data-independent acquisition and improving the analysis of metaproteomes.
Prosit is integrated into ProteomicsDB, allowing search result re-scoring and custom spectral library generation for any organism on the basis of
peptide sequence alone.