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Large Scale Non-targeted Metabolomic Profiling of Serum by Ultra Performance Liquid Chromatography-Mass Spectrometry (UPLC-MS)
Published on: March 14, 2013
A simple workflow to increase MS2 identification rate by subsequent spectral library search.
Erik Ahrné1, Alexandre Masselot, Pierre-Alain Binz
1Swiss Institute of Bioinformatics, Proteome Informatics Group, Geneva, Switzerland. erik.ahrne@isb-sib.ch
Proteomics
|February 24, 2009
Summary
This study enhances protein identification by combining Phenyx sequence searching with SpectraST library searching. This workflow significantly boosts peptide discovery rates, even for challenging spectra.
Area of Science:
- Proteomics
- Bioinformatics
- Mass Spectrometry
Background:
- Spectral library searching is a rapid and accurate method for protein identification using MS/MS data.
- Existing methods may not maximize peptide discovery, especially for complex or modified peptides.
Purpose of the Study:
- To develop and evaluate a hybrid workflow combining sequence and library searching to increase peptide identification rates.
- To assess the computational efficiency of the proposed workflow.
Main Methods:
- A workflow was constructed using Phenyx for sequence searching followed by SpectraST for library searching.
- A consensus spectral library was created from the results of the initial Phenyx search.
- The workflow was applied to MS/MS data to identify peptides.
Main Results:
- The combined Phenyx-SpectraST workflow increased the number of confidently matched spectra by up to 156%.
- SpectraST successfully identified peptides from noisy spectra, missed cleavages, and post-translationally modified peptides.
- The increase in peptide discovery was achieved with minimal additional computational time.
Conclusions:
- A hybrid search strategy significantly enhances peptide and protein identification in MS/MS data.
- This approach is effective for identifying challenging peptides, including those with modifications or from incomplete enzymatic digestion.
- The workflow offers a computationally efficient method for improving proteomic data analysis.
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