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High-quality MS/MS spectrum prediction for data-dependent and data-independent acquisition data analysis.
Tiwary, Shivani; Levy, Roie; Gutenbrunner, Petra; Salinas Soto, Favio; Palaniappan, Krishnan K; Deming, Laura; Berndl, Marc; Brant, Arthur; Cimermancic, Peter; Cox, Jürgen.
Afiliación
  • Tiwary S; Computational Systems Biochemistry Research Group, Max Planck Institute of Biochemistry, Martinsried, Germany.
  • Levy R; Verily Life Sciences, South San Francisco, CA, USA.
  • Gutenbrunner P; Computational Systems Biochemistry Research Group, Max Planck Institute of Biochemistry, Martinsried, Germany.
  • Salinas Soto F; Computational Systems Biochemistry Research Group, Max Planck Institute of Biochemistry, Martinsried, Germany.
  • Palaniappan KK; Verily Life Sciences, South San Francisco, CA, USA.
  • Deming L; Google LLC, Mountain View, CA, USA.
  • Berndl M; Google LLC, Mountain View, CA, USA.
  • Brant A; Verily Life Sciences, South San Francisco, CA, USA.
  • Cimermancic P; Verily Life Sciences, South San Francisco, CA, USA. cpeter@verily.com.
  • Cox J; Computational Systems Biochemistry Research Group, Max Planck Institute of Biochemistry, Martinsried, Germany. cox@biochem.mpg.de.
Nat Methods ; 16(6): 519-525, 2019 06.
Article en En | MEDLINE | ID: mdl-31133761
Peptide fragmentation spectra are routinely predicted in the interpretation of mass-spectrometry-based proteomics data. However, the generation of fragment ions has not been understood well enough for scientists to estimate fragment ion intensities accurately. Here, we demonstrate that machine learning can predict peptide fragmentation patterns in mass spectrometers with accuracy within the uncertainty of measurement. Moreover, analysis of our models reveals that peptide fragmentation depends on long-range interactions within a peptide sequence. We illustrate the utility of our models by applying them to the analysis of both data-dependent and data-independent acquisition datasets. In the former case, we observe a q-value-dependent increase in the total number of peptide identifications. In the latter case, we confirm that the use of predicted tandem mass spectrometry spectra is nearly equivalent to the use of spectra from experimental libraries.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Fragmentos de Péptidos / Programas Informáticos / Biomarcadores / Biblioteca de Péptidos / Proteoma / Espectrometría de Masas en Tándem / Análisis de Datos Tipo de estudio: Prognostic_studies / Risk_factors_studies Límite: 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 Pais de publicación: Estados Unidos
Texto completo
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Fragmentos de Péptidos / Programas Informáticos / Biomarcadores / Biblioteca de Péptidos / Proteoma / Espectrometría de Masas en Tándem / Análisis de Datos Tipo de estudio: Prognostic_studies / Risk_factors_studies Límite: 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 Pais de publicación: Estados Unidos