Prosit: proteome-wide prediction of peptide tandem mass spectra by deep learning.
Nat Methods
; 16(6): 509-518, 2019 06.
Article
en En
| MEDLINE
| ID: mdl-31133760
ABSTRACT
In mass-spectrometry-based proteomics, the identification and quantification of peptides and proteins heavily rely on sequence database searching or spectral library matching. The lack of accurate predictive models for fragment ion intensities impairs the realization of the full potential of these approaches. Here, we extended the ProteomeTools synthetic peptide library to 550,000 tryptic peptides and 21 million high-quality tandem mass spectra. We trained a deep neural network, termed Prosit, resulting in chromatographic retention time and fragment ion intensity predictions that exceed the quality of the experimental data. Integrating Prosit into database search pipelines led to more identifications at >10× lower false discovery rates. We show the general applicability of Prosit by predicting spectra for proteases other than trypsin, generating spectral libraries for data-independent acquisition and improving the analysis of metaproteomes. Prosit is integrated into ProteomicsDB, allowing search result re-scoring and custom spectral library generation for any organism on the basis of peptide sequence alone.
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Fragmentos de Péptidos
/
Programas Informáticos
/
Redes Neurales de la Computación
/
Biblioteca de Péptidos
/
Proteoma
/
Espectrometría de Masas en Tándem
/
Aprendizaje Profundo
Tipo de estudio:
Prognostic_studies
/
Risk_factors_studies
Límite:
Animals
/
Humans
Idioma:
En
Revista:
Nat Methods
Asunto de la revista:
TECNICAS E PROCEDIMENTOS DE LABORATORIO
Año:
2019
Tipo del documento:
Article
País de afiliación:
Alemania