High-Confidence de Novo Peptide Sequencing Using Positive Charge Derivatization and Tandem MS Spectra Merging

peer-reviewed · Analytical Chemistry · 2013

peer-reviewed · Analytical Chemistry · 2013. Mingrui An et al. De novo peptide sequencing holds great promise in discovering new protein sequences and modifications but has…
Date 2013-04-16
Type peer-reviewed
Venue Analytical Chemistry
Publisher ACS
Contribution algorithm
DOI 10.1021/ac4001699
Citations (OpenAlex) 30
Venue 2-year citedness 6.29

Abstract

De novo peptide sequencing holds great promise in discovering new protein sequences and modifications but has often been hindered by low success rate of mass spectra interpretation, mainly due to the diversity of fragment ion types and insufficient information for each ion series. Here, we describe a novel methodology that combines highly efficient on-tip charge derivatization and tandem MS spectra merging, which greatly boosts the performance of interpretation. TMPP-Ac-OSu (succinimidyloxycarbonylmethyl tris(2,4,6-trimethoxyphenyl)phosphonium bromide) was used to derivatize peptides at N-termini on tips to reduce mass spectra complexity. Then, a novel approach of spectra merging was adopted to combine the benefits of collision-induced dissociation (CID) and electron transfer dissociation (ETD) fragmentation. We applied this methodology to rat C6 glioma cells and the Cyprinus carpio and searched the resulting peptide sequences against the protein database. Then, we achieved thousands of high-confidence peptide sequences, a level that conventional de novo sequencing methods could not reach. Next, we identified dozens of novel peptide sequences by homology searching of sequences that were fully backbone covered but unmatched during the database search. Furthermore, we randomly chose 34 sequences discovered in rat C6 cells and verified them. Finally, we conclude that this novel methodology that combines on-tip positive charge derivatization and tandem MS spectra merging will greatly facilitate the discovery of novel proteins and the proteome analysis of nonmodel organisms.

Authors

  1. Mingrui An · Peking University
  2. Xiao Zou · Peking University
  3. Qingsong Wang · Peking University
  4. Xuyang Zhao · Peking University
  5. Jing Wu · Peking University
  6. Li-Ming Xu · Peking University
  7. Hong-Yan Shen · Peking University
  8. Xueyuan Xiao · Beijing Normal University
  9. Dacheng He · Beijing Normal University
  10. Jianguo Ji · Peking University

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