Analysis of the Resolution Limitations of Peptide Identification Algorithms

Citations Over TimeTop 10% of 2011 papers

Abstract

Proteome identification using peptide-centric proteomics techniques is a routinely used analysis technique. One of the most powerful and popular methods for the identification of peptides from MS/MS spectra is protein database matching using search engines. Significance thresholding through false discovery rate (FDR) estimation by target/decoy searches is used to ensure the retention of predominantly confident assignments of MS/MS spectra to peptides. However, shortcomings have become apparent when such decoy searches are used to estimate the FDR. To study these shortcomings, we here introduce a novel kind of decoy database that contains isobaric mutated versions of the peptides that were identified in the original search. Because of the supervised way in which the entrapment sequences are generated, we call this a directed decoy database. Since the peptides found in our directed decoy database are thus specifically designed to look quite similar to the forward identifications, the limitations of the existing search algorithms in making correct calls in such strongly confusing situations can be analyzed. Interestingly, for the vast majority of confidently identified peptide identifications, a directed decoy peptide-to-spectrum match can be found that has a better or equal match score than the forward match score, highlighting an important issue in the interpretation of peptide identifications in present-day high-throughput proteomics.

Related Papers

• → A Decoy-Free Approach to the Identification of Peptides(2015)40 cited
• → Averaging Strategy To Reduce Variability in Target-Decoy Estimates of False Discovery Rate(2018)34 cited
• → Bias in False Discovery Rate Estimation in Mass-Spectrometry-Based Peptide Identification(2019)31 cited
• → A note on the false discovery rate of novel peptides in proteogenomics(2015)33 cited
• → An averaging strategy to reduce variability in target-decoy estimates of false discovery rate(2018)3 cited