pNovo: De novo Peptide Sequencing and Identification Using HCD Spectra

peer-reviewed · Journal of Proteome Research · 2010

peer-reviewed · Journal of Proteome Research · 2010. Hao Chi et al. De novo peptide sequencing has improved remarkably in the past decade as a result of better instruments and…
Date 2010-05-07
Type peer-reviewed
Venue Journal of Proteome Research
Publisher American Chemical Society (ACS)
Contribution algorithm
DOI 10.1021/pr100182k
Citations (OpenAlex) 168
Venue 2-year citedness 3.83

Abstract

De novo peptide sequencing has improved remarkably in the past decade as a result of better instruments and computational algorithms. However, de novo sequencing can correctly interpret only approximately 30% of high- and medium-quality spectra generated by collision-induced dissociation (CID), which is much less than database search. This is mainly due to incomplete fragmentation and overlap of different ion series in CID spectra. In this study, we show that higher-energy collisional dissociation (HCD) is of great help to de novo sequencing because it produces high mass accuracy tandem mass spectrometry (MS/MS) spectra without the low-mass cutoff associated with CID in ion trap instruments. Besides, abundant internal and immonium ions in the HCD spectra can help differentiate similar peptide sequences. Taking advantage of these characteristics, we developed an algorithm called pNovo for efficient de novo sequencing of peptides from HCD spectra. pNovo gave correct identifications to 80% or more of the HCD spectra identified by database search. The number of correct full-length peptides sequenced by pNovo is comparable with that obtained by database search. A distinct advantage of de novo sequencing is that deamidated peptides and peptides with amino acid mutations can be identified efficiently without extra cost in computation. In summary, implementation of the HCD characteristics makes pNovo an excellent tool for de novo peptide sequencing from HCD spectra.

Authors

  1. Hao Chi · Chinese Academy of Sciences, Institute of Computing Technology, University of Chinese Academy of Sciences
  2. Rui-Xiang Sun · Chinese Academy of Sciences
  3. Bing Yang · National Institute of Biological Sciences, Beijing
  4. Chun-Qing Song · National Institute of Biological Sciences, Beijing
  5. Le-Heng Wang · Chinese Academy of Sciences
  6. Chao Liu · Beihang University, Chinese Academy of Sciences, University of Chinese Academy of Sciences
  7. Yan Fu · Chinese Academy of Sciences, University of Chinese Academy of Sciences
  8. Zuo-Fei Yuan · Chinese Academy of Sciences, University of Chinese Academy of Sciences
  9. Haipeng Wang · Chinese Academy of Sciences, Shandong University of Technology, University of Chinese Academy of Sciences
  10. Si-Min He · Chinese Academy of Sciences, Institute of Computing Technology, University of Chinese Academy of Sciences
  11. Meng-Qiu Dong · National Institute of Biological Sciences, Beijing, Tsinghua University

Methods and tools

  • pNovo: First HCD-focused de novo

Cites (26)

Cited by (91)

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