Direct Protein Identification from Nonspecific Peptide Pools by High‐Accuracy MS Data Filtering
peer-reviewed · Angewandte Chemie International Edition · 2006
peer-reviewed · Angewandte Chemie International Edition · 2006. Vinh An Thieu et al. Validate your results! High-accuracy mass spectrometric analysis and composition-based de novo sequencing…
| Date | 2006-05-12 |
| Type | peer-reviewed |
| Venue | Angewandte Chemie International Edition |
| Publisher | Wiley |
| Contribution | algorithm |
| DOI | 10.1002/anie.200503787 |
| Citations (OpenAlex) | 20 |
Abstract
Validate your results! High-accuracy mass spectrometric analysis and composition-based de novo sequencing (CBS) combined with database(DB)-assisted protein identification allows for the rapid production of validated analytical results in proteome research. A simplified sample preparation scheme is sufficient to provide for reliable identification of proteins even when following nonspecific and unknown degradation processes.
Methods and tools
- Composition-based sequencing: Two-step database-free strategy enabled by FT-ICR mass accuracy: determine the peptide’s amino acid COMPOSITION first, then score only the permutations that composition allows, which is a far smaller search space than conventional de novo sequencing explores.
Cites (1)
Cited by (6)
- Isolation and sequence analysis of peptides from the skin secretion of the Middle East tree frog Hyla savignyi (2010) crossref
- Characterization of a peptide family from the skin secretion of the Middle East Tree Frog Hyla savignyi by composition‐based de novo sequencing (2010) crossref
- Identification of collagen IV derived danger/alarm signals in insect immunity by nanoLC-FTICR MS (2009) crossref
- Simplified validation of borderline hits of database searches (2008) both
- Accurate Mass as a Bioinformatic Parameter in Data-to-Knowledge Conversion: Fourier Transform Ion Cyclotron Resonance Mass Spectrometry for Peptide De Novo Sequencing (2007) crossref
- Rapid Validation of Protein Identifications with the Borderline Statistical Confidence via De Novo Sequencing and MS BLAST Searches (2006) crossref