Deep learning the collisional cross sections of the peptide universe from a million experimental values.
Nat Commun
; 12(1): 1185, 2021 02 19.
Article
em En
| MEDLINE
| ID: mdl-33608539
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.
Texto completo:
1
Coleções:
01-internacional
Contexto em Saúde:
3_ND
Base de dados:
MEDLINE
Assunto principal:
Peptídeos
/
Proteoma
/
Proteômica
/
Espectrometria de Massas em Tandem
/
Aprendizado Profundo
Tipo de estudo:
Prognostic_studies
Limite:
Animals
/
Humans
Idioma:
En
Revista:
Nat Commun
Ano de publicação:
2021
Tipo de documento:
Article