Deep Learning the Collisional Cross Sections of the Peptide Universe From a Million Experimental Values

MCML Authors

Fabian Theis

Prof. Dr.

Principal Investigator

Mathematical Modelling of Biological Systems

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.

article

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Nature Communications

12.1185. Feb. 2021.

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Authors

F. Meier • N. D. Köhler • A.-D. Brunner • J.-M. H. Wanka • E. Voytik • M. T. Strauss • F. J. Theis • M. Mann

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Links

DOI

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Research Area

 C2 | Biology