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A Protocol for Computer-Based Protein Structure and Function Prediction
Published on: November 3, 2011
Defining parameters for homology-tolerant database searching
J P Kayser1, J L Vallet, R L Cerny
1USDA, ARS, RLH US Meat Animal Research Center, Clay Center, NE 68933, USA.
Journal of Biomolecular Techniques : JBT
|December 9, 2004
Summary
De novo sequencing with tandem mass spectrometry (MS/MS) enables protein identification. A homology search strategy using 20 peptides and 30% mismatch optimizes protein database searching when sequence data is limited.
Area of Science:
- Proteomics
- Bioinformatics
- Mass Spectrometry
Background:
- De novo interpretation of tandem mass spectrometry (MS/MS) spectra is crucial for protein database searching with limited sequence information.
- Developing effective strategies for homology-tolerant database searches is essential for accurate protein identification.
Purpose of the Study:
- To define an optimal strategy for homology-tolerant database searching using de novo MS/MS spectra.
- To compare the efficacy of homology searches with traditional peptide mass searching (Mascot).
Main Methods:
- Homology searches were performed using MS-Homology software with varying numbers of abundant peptides (20, 10, or 5) and mismatch tolerances (50%, 30%, 10%).
- Protein scores were corrected against a threshold derived from random peptides.
- Protein identification results from homology searches were compared to those obtained using Mascot with MS/MS data.
Main Results:
- The optimal homology search strategy involved submitting 20 peptides with a 30% mismatch tolerance, yielding the highest corrected protein scores (p < .01).
- Mascot and homology searches (using 20 or all peptides) identified the same top-ranking protein for 63.4% of analyzed spots.
- Mascot achieved greater percent coverage (25.1%) compared to homology searches using all (18.3%) or the top 20 peptides (10.6%).
- De novo sequences showed complete matches to known sequences in 35% of cases, increasing to 44.0% when limited to the top 20 peptides.
Conclusions:
- A homology search strategy using 20 peptides and 30% mismatch is effective for protein identification from de novo MS/MS sequences.
- While Mascot offers higher percent coverage, homology searches are valuable for database searching with limited sequence data.
- Combining MS-Homology for initial identification with a subsequent peptide mass search can enhance overall protein coverage.
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