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.
journal_name
Nat Methodsjournal_title
Nature methodsauthors
Gessulat S,Schmidt T,Zolg DP,Samaras P,Schnatbaum K,Zerweck J,Knaute T,Rechenberger J,Delanghe B,Huhmer A,Reimer U,Ehrlich HC,Aiche S,Kuster B,Wilhelm Mdoi
10.1038/s41592-019-0426-7subject
Has Abstractpub_date
2019-06-01 00:00:00pages
509-518issue
6eissn
1548-7091issn
1548-7105pii
10.1038/s41592-019-0426-7journal_volume
16pub_type
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