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rPTMDetermine: A Fully Automated Methodology for Endogenous Tyrosine Nitration Validation, Site-Localization, and Beyond.
Dong, Naiping; Spencer, Daniel M; Quan, Quan; Le Blanc, J C Yves; Feng, Jinwen; Li, Mengzhu; Siu, K W Michael; Chu, Ivan K.
Afiliação
  • Dong N; Department of Chemistry, The University of Hong Kong, Pokfulam, Hong Kong, China.
  • Spencer DM; Department of Chemistry, The University of Hong Kong, Pokfulam, Hong Kong, China.
  • Quan Q; Department of Chemistry, The University of Hong Kong, Pokfulam, Hong Kong, China.
  • Le Blanc JCY; SCIEX, 71 Four Valley Drive, Concord, Ontario L4K 4V8, Canada.
  • Feng J; Department of Chemistry, The University of Hong Kong, Pokfulam, Hong Kong, China.
  • Li M; Department of Chemistry, The University of Hong Kong, Pokfulam, Hong Kong, China.
  • Siu KWM; Department of Chemistry, The University of Hong Kong, Pokfulam, Hong Kong, China.
  • Chu IK; Department of Chemistry and Centre for Research in Mass Spectrometry, York University, Toronto, Ontario M3J 1P3, Canada.
Anal Chem ; 92(15): 10768-10776, 2020 08 04.
Article em En | MEDLINE | ID: mdl-32628467
ABSTRACT
We present herein rPTMDetermine, an adaptive and fully automated methodology for validation of the identification of rarely occurring post-translational modifications (PTMs), using a semisupervised approach with a linear discriminant analysis (LDA) algorithm. With this strategy, verification is enhanced through similarity scoring of tandem mass spectrometry (MS/MS) comparisons between modified peptides and their unmodified analogues. We applied rPTMDetermine to (1) perform fully automated validation steps for modified peptides identified from an in silico database and (2) retrieve potential yet-to-be-identified modified peptides from raw data (that had been missed through conventional database searches). In part (1), 99 of 125 3-nitrotyrosyl-containing (nitrated) peptides obtained from a ProteinPilot search were validated and localized. Twenty nitrated peptides were falsely assigned because of incorrect monoisotopic peak assignments, leading to erroneous identification of deamidation and nitration. Five additional nitrated peptides were, however, validated after performing nonmonoisotopic peak correction. In part (2), an additional 236 unique nitrated peptides were retrieved and localized, containing 113 previously unreported nitration sites; 25 endogenous nitrated peptides with novel sites were selected and verified by comparison with synthetic analogues. In summary, we identified and confidently validated 296 unique nitrated peptides-collectively representing the largest number of endogenously identified 3-nitrotyrosyl-containing peptides from the cerebral cortex proteome of a Macaca fascicularis model of stroke. Furthermore, we harnessed the rPTMDetermine strategy to complement conventional database searching and enhance the confidence of assigning rarely occurring PTMs, while recovering many missed peptides. In a final demonstration, we successfully extended the application of rPTMDetermine to peptides featuring tryptophan oxidation.
Assuntos

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Tirosina / Processamento de Proteína Pós-Traducional / Aprendizado de Máquina Supervisionado / Nitratos Tipo de estudo: Prognostic_studies Idioma: En Ano de publicação: 2020 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Tirosina / Processamento de Proteína Pós-Traducional / Aprendizado de Máquina Supervisionado / Nitratos Tipo de estudo: Prognostic_studies Idioma: En Ano de publicação: 2020 Tipo de documento: Article