Dataset Information

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

ABSTRACT: In this study, we extended the ProteomeTools peptide library (PROPEL, see PXD004732 and PXD010595) to train a deep neural network resulting in chromatographic retention time and fragment ion intensity predictions for (tryptic) peptides that exceed the quality of the experimental data.

INSTRUMENT(S): Orbitrap Fusion ETD

ORGANISM(S): Drosophila Melanogaster (ncbitaxon:7227) Escherichia Coli (ncbitaxon:562) Saccharomyces Cerevisiae (ncbitaxon:4932) Homo Sapiens (ncbitaxon:9606) Caenorhabditis Elegans (ncbitaxon:6239)

SUBMITTER: Bernhard Kuster

PROVIDER: MSV000087047 | MassIVE | Fri Mar 12 19:34:00 GMT 2021

SECONDARY ACCESSION(S): PXD010871

REPOSITORIES: MassIVE

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

Gessulat Siegfried S Schmidt Tobias T Zolg Daniel Paul DP Samaras Patroklos P Schnatbaum Karsten K Zerweck Johannes J Knaute Tobias T Rechenberger Julia J Delanghe Bernard B Huhmer Andreas A Reimer Ulf U Ehrlich Hans-Christian HC Aiche Stephan S Kuster Bernhard B Wilhelm Mathias M

Nature methods 20190527 6

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, r ...[more]

PMID: 31133760

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