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Mutation-tolerant protein identification by mass spectrometry.
P A Pevzner1, V Dancík, C L Tang
1Departments of Computer Science and Engineering, University of California at San Diego, La Jolla, CA 92093, USA.
Summary
This study introduces a novel spectral similarity method for identifying related peptides, even with mutations or modifications. This advances mutation-tolerant database searching and spectral clustering in proteomics.
Area of Science:
- Proteomics
- Bioinformatics
- Mass Spectrometry
Background:
- High-throughput spectral acquisition in mass spectrometry presents challenges in identifying proteins with genetic variations and modifications.
- Analyzing large, uncharacterized spectral datasets from diverse individuals (normal and diseased) requires robust methods for functional proteomics.
Purpose of the Study:
- To develop a method for cross-correlating and clustering related spectra from large, uncharacterized collections.
- To address the challenge of identifying related peptides despite significant spectral variations due to mutations or modifications.
Main Methods:
- Introduction of a new "spectral similarity" notion to identify related spectra from peptides with multiple variations.
- Development of a novel algorithm for mutation-tolerant database searching.
- Implementation of a method for cross-correlating related uncharacterized spectra.
Main Results:
- The new spectral similarity notion effectively identifies related spectra from peptides with multiple modifications or mutations.
- The developed algorithm enables mutation-tolerant database searching, improving protein identification accuracy.
- The cross-correlation method facilitates the clustering of related uncharacterized spectra.
Conclusions:
- The proposed spectral similarity approach and associated algorithms significantly enhance the analysis of complex proteomic datasets.
- This work provides valuable tools for functional proteomics, particularly in studies involving genetic variation and post-translational modifications.