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

peer-reviewed · Nature Methods · 2019

peer-reviewed · Nature Methods · 2019. Siegfried Gessulat et al.
Date 2019-05-27
Type peer-reviewed
Venue Nature Methods
Publisher Springer Science and Business Media LLC
Contribution adjacent
DOI 10.1038/s41592-019-0426-7
Citations (OpenAlex) 994
Venue 2-year citedness 20.00

Authors

  1. Siegfried Gessulat · SAP SE, Technical University of Munich
  2. Tobias Schmidt · Technical University of Munich
  3. Daniel Paul Zolg · Technical University of Munich
  4. Patroklos Samaras · Technical University of Munich
  5. Karsten Schnatbaum · JPT Peptide Technologies GmbH
  6. Johannes Zerweck · JPT Peptide Technologies GmbH
  7. Tobias Knaute · JPT Peptide Technologies GmbH
  8. Julia Rechenberger · Technical University of Munich
  9. Bernard Delanghe · Thermo Fisher Scientific GmbH
  10. Andreas Huhmer · Thermo Fisher Scientific
  11. Ulf Reimer · JPT Peptide Technologies GmbH
  12. Hans-Christian Ehrlich · SAP SE
  13. Stephan Aiche · SAP SE
  14. Bernhard Kuster · Bavarian Center for Biomolecular Mass Spectrometry, Technical University of Munich
  15. Mathias Wilhelm · Technical University of Munich

Methods and tools

  • Prosit: Deep-learning (bidirectional RNN + attention) predictor of peptide fragment-ion intensities and retention times: widely used to validate and rescore de novo sequencing candidates against predicted spectra.

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