ProteomeTools

Translating the Human Proteome into Molecular and Digital Tools for Drug Discovery, Personalized Medicine and Life Science Research
The verification of the correct assignment of fragment ion mass spectra to peptide sequences is still a challenge in mass spectrometry based proteomics. The preferred way to achieve this is the use of reference spectra of synthetic peptide standards. Within the ProteomeTools project we synthesized >1 million individual tryptic and non-tryptic, non-modified and modified peptides representing all human proteins and systematically created CID, HCD, ETD and EThcD spectra.
The libraries of peptides and spectra will greatly facilitate the validation of discovery proteomics data, will form a basis for targeted proteomic assays, enable FDR estimation in large datasets or repositories and help to better understand peptide fragmentation thus improving identification algorithms and spectra acquisition. Based on the data, new prototypes and products will be developed that form the basis for advancing research in a broad range of biomedical areas, like biomarker discovery, target validation, and drug discovery.
The project was funded by Bundesministerium für Bildung und Forschung (BMBF). 

References

References

  • Prosit: Proteome-wide Prediction of Peptide Tandem Mass Spectra by Deep Learning
    Gessulat et al., Nature Methods (2019)
  • ProteomeTools: Systematic Characterization of 21 Post-translational Protein Modifications by Liquid Chromatography Tandem Mass Spectrometry (LC-MS/MS) Using Synthetic Peptides
    Zolg et al., Mol Cell Proteomics. (2018)
  • PROCAL: A Set of 40 Peptide Standards for Retention Time Indexing, Column Performance Monitoring and Collision Energy Calibration
    Zolg et al., Proteomics (2017)
  • Building ProteomeTools Based on a Complete Synthetic Human Proteome
    Zolg et al., Nature Methods (2017) – PMID: 28135259
  • Mass-spectrometry-based Draft of the Human Proteome
    Wilhelm et al., Nature (2014) – PMID: 24870543
Project Partners

Project Partners

  • Technical University of Munich
  • SAP
  • Thermo Fisher Scientific
  • Funded by Bundesministerium für Bildung und Forschung (BMBF)
Technologies
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