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  Deep learning the collisional cross sections of the peptide universe from a million experimental values

Meier, F., Koehler, N. D., Brunner, A.-D., Wanka, J.-M.-H., Voytik, E., Strauss, M. T., et al. (2021). Deep learning the collisional cross sections of the peptide universe from a million experimental values. Nature Communications, 12(1): 1185. doi:10.1038/s41467-021-21352-8.

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 Creators:
Meier, Florian1, Author              
Koehler, Niklas D.2, Author
Brunner, Andreas-David1, Author              
Wanka, Jean-Marc H.2, Author
Voytik, Eugenia1, Author              
Strauss, Maximilian T.1, Author              
Theis, Fabian J.2, Author
Mann, Matthias1, Author              
Affiliations:
1Mann, Matthias / Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Max Planck Society, ou_1565159              
2external, ou_persistent22              

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Free keywords: Science & Technology - Other Topics;
 Abstract: The size and shape of peptide ions in the gas phase are an under-explored dimension for mass spectrometry-based proteomics. To investigate the nature and utility of the peptide collisional cross section (CCS) space, we measure more than a million data points from whole-proteome digests of five organisms with trapped ion mobility spectrometry (TIMS) and parallel accumulation-serial fragmentation (PASEF). The scale and precision (CV<1%) of our data is sufficient to train a deep recurrent neural network that accurately predicts CCS values solely based on the peptide sequence. Cross section predictions for the synthetic ProteomeTools peptides validate the model within a 1.4% median relative error (R>0.99). Hydrophobicity, proportion of prolines and position of histidines are main determinants of the cross sections in addition to sequence-specific interactions. CCS values can now be predicted for any peptide and organism, forming a basis for advanced proteomics workflows that make full use of the additional information. Proteomics has been advanced by algorithms that can predict different peptide features, but predicting peptide collisional cross sections (CCS) has remained challenging. Here, the authors measure over one million CCS values of tryptic peptides and develop a deep learning model for peptide CCS prediction.

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Language(s): eng - English
 Dates: 2021
 Publication Status: Published online
 Pages: 12
 Publishing info: -
 Table of Contents: -
 Rev. Type: Peer
 Degree: -

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Title: Nature Communications
  Abbreviation : Nat. Commun.
Source Genre: Journal
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Publ. Info: London : Nature Publishing Group
Pages: - Volume / Issue: 12 (1) Sequence Number: 1185 Start / End Page: - Identifier: ISSN: 2041-1723
CoNE: https://pure.mpg.de/cone/journals/resource/2041-1723