Dataset Information

High quality MS/MS spectrum prediction for data-dependent and -independent acquisition data analysis

ABSTRACT: Peptide fragmentation spectra are routinely predicted in the interpretation of mass spectrometry-based proteomics data. Unfortunately, the generation of fragment ions is not well enough understood to estimate fragment ion intensities accurately. Here, we demonstrate that machine learning can predict peptide fragmentation patterns in mass spectrometers with accuracy within the uncertainty of the measurements. Moreover, analysis of our models reveals that peptide fragmentation depends on long-range interactions within a peptide sequence. We illustrate the utility of our models by applying them to the analysis of both data-dependent and data-independent acquisition datasets. In the former case, we observe a significant increase in the total number of peptide identifications at fixed false discovery rate. In the latter case we demonstrate that the use of predicted MS/MS spectra is equivalent to the use of spectra from experimentallibraries, indicating that fragmentation libraries for proteomics are becoming obsolete.

INSTRUMENT(S): Q Exactive

ORGANISM(S): Homo Sapiens (human)

TISSUE(S): Blood Plasma

SUBMITTER: Shivani Tiwary

LAB HEAD: Juergen Cox; Peter Cimermancic

PROVIDER: PXD010382 | Pride | 2019-03-13

REPOSITORIES: Pride

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Dataset's files

Source:

			Action	DRS
	20161213_NGHF_DBJ_SA_Exp3A_HeLa_1ug_60min_15000_01.raw	Raw
	20161213_NGHF_DBJ_SA_Exp3A_HeLa_1ug_60min_15000_02.raw	Raw
	20161213_NGHF_DBJ_SA_Exp3A_HeLa_1ug_60min_15000_03.raw	Raw
	20171102_091300_Test-InSilico-20171030-real_Report.zip	Other
	20171102_091314_Test-Insilico-20171030-2d_Report.zip	Other

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Publications

High-quality MS/MS spectrum prediction for data-dependent and data-independent acquisition data analysis.

Tiwary Shivani S Levy Roie R Gutenbrunner Petra P Salinas Soto Favio F Palaniappan Krishnan K KK Deming Laura L Berndl Marc M Brant Arthur A Cimermancic Peter P Cox Jürgen J

Nature methods 20190527 6

Peptide fragmentation spectra are routinely predicted in the interpretation of mass-spectrometry-based proteomics data. However, the generation of fragment ions has not been understood well enough for scientists to estimate fragment ion intensities accurately. Here, we demonstrate that machine learning can predict peptide fragmentation patterns in mass spectrometers with accuracy within the uncertainty of measurement. Moreover, analysis of our models reveals that peptide fragmentation depends on ...[more]

PMID: 31133761

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