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

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 a...

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Podrobná bibliografie
Vydáno v:	Nature methods Ročník 16; číslo 6; s. 509 - 518
Hlavní autoři:	Gessulat, Siegfried, Schmidt, Tobias, Zolg, Daniel Paul, Samaras, Patroklos, Schnatbaum, Karsten, Zerweck, Johannes, Knaute, Tobias, Rechenberger, Julia, Delanghe, Bernard, Huhmer, Andreas, Reimer, Ulf, Ehrlich, Hans-Christian, Aiche, Stephan, Kuster, Bernhard, Wilhelm, Mathias
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	United States Nature Publishing Group 01.06.2019
Témata:	Animals Artificial neural networks Caenorhabditis elegans - metabolism Database searching Databases, Protein Deep Learning Drosophila melanogaster - metabolism HEK293 Cells Humans Libraries Mass spectra Mass spectroscopy Neural networks Neural Networks, Computer Peptide Fragments - analysis Peptide Fragments - metabolism Peptide Library Peptides Prediction models Proteome - analysis Proteome - metabolism Proteomics Retention time Saccharomyces cerevisiae - metabolism Software Spectrometry Tandem Mass Spectrometry - methods Trypsin Tryptic peptides
ISSN:	1548-7091, 1548-7105, 1548-7105
On-line přístup:	Získat plný text
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Popis
Shrnutí:	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.
Bibliografie:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23
ISSN:	1548-7091 1548-7105 1548-7105
DOI:	10.1038/s41592-019-0426-7