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)	40
Venue 2-year citedness	3.48

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, 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, University of California San Diego

Methods and tools

• MS BLAST validation: Validation of borderline protein identifications using de novo sequence tags and MS BLAST searches.

Cites (5)

• Proteomics-Grade de Novo Sequencing Approach (2005) crossref
• AUDENS: A Tool for Automated Peptide de Novo Sequencing (2005) crossref
• PepNovo: de novo peptide sequencing via probabilistic network modeling (2005) crossref
• Peptide and protein de novo sequencing by mass spectrometry (2003) crossref
• De novo peptide sequencing via tandem mass spectrometry (1999) crossref

Cited by (2)

• Uncovering Hidden Members and Functions of the Soil Microbiome Using De Novo Metaproteomics (2022) semanticscholar
• Lessons in de novo peptide sequencing by tandem mass spectrometry (2015) both

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