Deep learning the collisional cross sections of the peptide universe from a million experimental values
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- Deep learning the collisional cross sections of the peptide universe from a million experimental values
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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.
Original language | English |
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Article number | 1185 |
Journal | Nature Communications |
Volume | 12 |
Number of pages | 12 |
ISSN | 2041-1723 |
DOIs | |
Publication status | Published - 2021 |
Externally published | Yes |
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