A high-throughput de novo sequencing approach for shotgun proteomics using high-resolution tandem mass spectrometry

Chongle Pan, Byung H. Park, William H. McDonald, Patricia A. Carey, Jillian F. Banfield, Nathan C. VerBerkmoes, Robert L. Hettich, Nagiza F. Samatova

    Research output: Contribution to journalArticlepeer-review

    54 Scopus citations

    Abstract

    Background: High-resolution tandem mass spectra can now be readily acquired with hybrid instruments, such as LTQ-Orbitrap and LTQ-FT, in high-throughput shotgun proteomics workflows. The improved spectral quality enables more accurate de novo sequencing for identification of post-translational modifications and amino acid polymorphisms.Results: In this study, a new de novo sequencing algorithm, called Vonode, has been developed specifically for analysis of such high-resolution tandem mass spectra. To fully exploit the high mass accuracy of these spectra, a unique scoring system is proposed to evaluate sequence tags based primarily on mass accuracy information of fragment ions. Consensus sequence tags were inferred for 11,422 spectra with an average peptide length of 5.5 residues from a total of 40,297 input spectra acquired in a 24-hour proteomics measurement of Rhodopseudomonas palustris. The accuracy of inferred consensus sequence tags was 84%. According to our comparison, the performance of Vonode was shown to be superior to the PepNovo v2.0 algorithm, in terms of the number of de novo sequenced spectra and the sequencing accuracy.Conclusions: Here, we improved de novo sequencing performance by developing a new algorithm specifically for high-resolution tandem mass spectral data. The Vonode algorithm is freely available for download at http://compbio.ornl.gov/Vonode.

    Original languageEnglish
    Article number118
    JournalBMC Bioinformatics
    Volume11
    DOIs
    StatePublished - Mar 5 2010

    Funding

    We thank D. Pelletier for R. palustris cell samples. We thank John Yates’ group at The Scripps Research Institute for DTASelect and Pavel Pevzner’s group at University of California at San Diego for PepNovo v2.0. The computational work of this research was supported by the “Ultrascale Computational Modeling of Phenotype-Specific Metabolic Processes in Microbial Communities” project from the U.S. Department of Energy, Office of Advanced Scientific Computing Research and Office of Biological and Environmental Research. The experimental work was supported by The U.S. Department of Energy, Office of Biological and Environmental Research, Genomics: Genomes to Life Program. Oak Ridge National Laboratory is managed by University of Tennessee-Battelle LLC for the Department of Energy under contract DOE-AC05-00OR22725.

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