Format

Send to

Choose Destination
Nat Methods. 2019 Jun;16(6):509-518. doi: 10.1038/s41592-019-0426-7. Epub 2019 May 27.

Prosit: proteome-wide prediction of peptide tandem mass spectra by deep learning.

Author information

1
Chair of Proteomics and Bioanalytics, Technical University of Munich, Freising, Germany.
2
SAP SE, Potsdam, Germany.
3
JPT Peptide Technologies GmbH, Berlin, Germany.
4
Thermo Fisher Scientific, Bremen, Germany.
5
Thermo Fisher Scientific, San Jose, CA, USA.
6
Chair of Proteomics and Bioanalytics, Technical University of Munich, Freising, Germany. kuster@tum.de.
7
Bavarian Center for Biomolecular Mass Spectrometry, Freising, Germany. kuster@tum.de.
8
Chair of Proteomics and Bioanalytics, Technical University of Munich, Freising, Germany. mathias.wilhelm@tum.de.

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.

PMID:
31133760
DOI:
10.1038/s41592-019-0426-7

Supplemental Content

Full text links

Icon for Nature Publishing Group
Loading ...
Support Center