An Information Theoretic Approach to Rescoring Peptides Produced by De Novo Peptide Sequencing
peer-reviewed · World Academy of Science, Engineering and Technology · 2010
| Date | 2010-10-29 |
| Type | peer-reviewed |
| Venue | World Academy of Science, Engineering and Technology |
| Publisher | WASET |
| Contribution | post-processor |
| DOI | 10.5281/zenodo.1070516 |
Abstract
Tandem mass spectrometry (MS/MS) is the engine driving high-throughput protein identification. Protein mixtures possibly representing thousands of proteins from multiple species are treated with proteolytic enzymes, cutting the proteins into smaller peptides that are then analyzed generating MS/MS spectra. The task of determining the identity of the peptide from its spectrum is currently the weak point in the process. Current approaches to de novo sequencing are able to compute candidate peptides efficiently. The problem lies in the limitations of current scoring functions. In this paper we introduce the concept of proteome signature. By examining proteins and compiling proteome signatures (amino acid usage) it is possible to characterize likely combinations of amino acids and better distinguish between candidate peptides. Our results strongly support the hypothesis that a scoring function that considers amino acid usage patterns is better able to distinguish between candidate peptides. This in turn leads to higher accuracy in peptide prediction.
Methods and tools
- Proteome signature rescoring: Rescores de novo peptide candidates using proteome signatures, the amino acid usage patterns compiled across a proteome, so the scoring function can prefer plausible residue combinations.