Rapid Validation of Protein Identifications with the Borderline Statistical Confidence via De Novo Sequencing and MS BLAST Searches

peer-reviewed · Journal of Proteome Research · 2006

peer-reviewed · Journal of Proteome Research · 2006. Natalie Wielsch et al. Protein identifications with the borderline statistical confidence are typically produced by matching a few…
Date 2006-09-01
Type peer-reviewed
Venue Journal of Proteome Research
Publisher American Chemical Society (ACS)
Contribution adjacent
DOI 10.1021/pr060200v
Citations (OpenAlex) 41
Venue 2-year citedness 3.83

Abstract

Protein identifications with the borderline statistical confidence are typically produced by matching a few marginal quality MS/MS spectra to database peptide sequences and represent a significant bottleneck in the reliable and reproducible characterization of proteomes. Here, we present a method for rapid validation of borderline hits that circumvents the need in, often biased, manual inspection of raw MS/MS spectra. The approach takes advantage of the independent interpretation of corresponding MS/MS spectra by PepNovo de novo sequencing software followed by mass spectrometry-driven BLAST (MS BLAST) sequence-similarity database searches that utilize all partially inaccurate, degenerate and redundant candidate peptide sequences. In a case study involving the identification of more than 180 Caenorhabditis elegans proteins by nanoLC-MS/MS analysis on a linear ion trap LTQ mass spectrometer, the approach enabled rapid assignment (confirmation or rejection) of more than 70% of Mascot hits of borderline statistical confidence.

Authors

  1. Natalie Wielsch · Max Planck Institute of Molecular Cell Biology and Genetics, University of California San Diego
  2. Henrik Thomas · Max Planck Institute of Molecular Cell Biology and Genetics, University of California San Diego
  3. Vineeth Surendranath · Max Planck Institute of Molecular Cell Biology and Genetics, University of California San Diego
  4. Patrice Waridel · Max Planck Institute of Molecular Cell Biology and Genetics, University of California San Diego
  5. Ari Frank · Affectivon, Inc., Max Planck Institute of Molecular Cell Biology and Genetics, University of California San Diego
  6. Pavel A. Pevzner · Max Planck Institute of Molecular Cell Biology and Genetics, Saint Petersburg Academic University, St Petersburg University, St. Petersburg State University, University of California San Diego, University of Southern California
  7. Andrej Shevchenko · European Molecular Biology Laboratory, Max Planck Institute of Molecular Cell Biology and Genetics, Max Planck Society, University of California San Diego

Methods and tools

  • MS BLAST: Homology search of error-tolerant de novo sequences against a protein database, introduced for charting the proteomes of organisms with unsequenced genomes and later used to validate borderline identifications.

Cites (12)

Cited by (9)

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