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

Tiwary, S., Levy, R., Gutenbrunner, P., Salinas Soto, F., Palaniappan, K. K., Deming, L., et al. (2019). High-quality MS/MS spectrum prediction for data-dependent and data-independent acquisition data analysis. Nature methods, 16(6), 519-525. doi:10.1038/s41592-019-0427-6.

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Genre: Journal Article

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 Creators:
Tiwary, Shivani1, Author              
Levy, Roie2, Author
Gutenbrunner, Petra1, Author              
Salinas Soto, Favio1, Author              
Palaniappan, Krishnan K.2, Author
Deming, Laura2, Author
Berndl, Marc2, Author
Brant, Arthur2, Author
Cimermancic, Peter2, Author
Cox, Jürgen1, Author              
Affiliations:
1Cox, Jürgen / Computational Systems Biochemistry, Max Planck Institute of Biochemistry, Max Planck Society, ou_2063284              
2external, ou_persistent22              

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Free keywords: PROTEIN SECONDARY STRUCTURE; MASS-SPECTROMETRY; PEPTIDE; DISSOCIATION; FRAGMENTATIONBiochemistry & Molecular Biology;
 Abstract: 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 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 q-value-dependent increase in the total number of peptide identifications. In the latter case, we confirm that the use of predicted tandem mass spectrometry spectra is nearly equivalent to the use of spectra from experimental libraries.

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Language(s): eng - English
 Dates: 2019
 Publication Status: Published in print
 Pages: 9
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: ISI: 000469455200021
DOI: 10.1038/s41592-019-0427-6
 Degree: -

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Project name : -
Grant ID : 686547
Funding program : Horizon 2020 (H2020)
Funding organization : European Commission (EC)
Project name : GA ERC-2012-SyG_318987–ToPAG
Grant ID : 318987
Funding program : Funding Programme 7 (FP7)
Funding organization : European Commission (EC)
Project name : Marie Skłodowska-Curie European Training Network TEMPERA
Grant ID : 722606
Funding program : Horizon 2020 (H2020)
Funding organization : European Commission (EC)

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Title: Nature methods
  Other : Nature methods
Source Genre: Journal
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Affiliations:
Publ. Info: New York, NY : Nature Pub. Group
Pages: - Volume / Issue: 16 (6) Sequence Number: - Start / End Page: 519 - 525 Identifier: ISSN: 1548-7091
CoNE: https://pure.mpg.de/cone/journals/resource/111088195279556