Deep learning the collisional cross sections of the peptide universe from a million experimental values
Meier F, Koehler ND, Brunner AD, Wanka JMH, Voytik E, Strauss MT, Theis FJ, Mann M (2021)
Publication Type: Journal article
Publication year: 2021
Journal
Book Volume: 12
Article Number: 1185
Journal Issue: 1
DOI: 10.1038/s41467-021-21352-8
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.
Involved external institutions
Max-Planck-Institut für Biochemie / Max Planck Institute of Biochemistry
Germany (DE)
Helmholtz Zentrum München - Deutsches Forschungszentrum für Gesundheit und Umwelt (HMGU) / Helmholtz Munich
Germany (DE)
Germany (DE)
Helmholtz Zentrum München - Deutsches Forschungszentrum für Gesundheit und Umwelt (HMGU) / Helmholtz Munich
Germany (DE)
How to cite
APA:
Meier, F., Koehler, N.D., Brunner, A.-D., Wanka, J.-M.H., Voytik, E., Strauss, M.T.,... Mann, M. (2021). Deep learning the collisional cross sections of the peptide universe from a million experimental values. Nature Communications, 12(1). https://doi.org/10.1038/s41467-021-21352-8
MLA:
Meier, Florian, et al. "Deep learning the collisional cross sections of the peptide universe from a million experimental values." Nature Communications 12.1 (2021).
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