Abstract
Today’s highly accurate spectra provided by modern tandem mass spectrometers offer considerable advantages for the analysis of proteomic samples of increased complexity. Among other factors, the quantity of reliably identified peptides is considerably influenced by the peptide identification algorithm. While most widely used search engines were developed when high-resolution mass spectrometry data were not readily available for fragment ion masses, we have designed a scoring algorithm particularly suitable for high mass accuracy. Our algorithm, MS Amanda, is generally applicable to HCD, ETD, and CID fragmentation type data. The algorithm confidently explains more spectra at the same false discovery rate than Mascot or SEQUEST on examined high mass accuracy data sets, with excellent overlap and identical peptide sequence identification for most spectra also explained by Mascot or SEQUEST. MS Amanda, available at http://ms.imp.ac.at/?goto=msamanda, is provided free of charge both as standalone version for integration into custom workflows and as a plugin for the Proteome Discoverer platform.
Highlights
Mass spectrometry (MS)-based proteomics has evolved into an indispensable approach in biological sample analysis.[1,2] In shotgun proteomics experiments, proteins are proteolytically cleaved to peptides, separated based on specific physicochemical properties, and subsequently analyzed in a mass spectrometer.Obtained spectra, containing mass-to-charge ratios of either charged peptides (MS1) or fragment ions (MS/MS or MS2)associated with respective ion intensities, are matched to candidate peptides, and a score dependent on an identification algorithm is assigned to each peptide spectrum match (PSM)
We compared PSM and peptide identifications of MS Amanda to Mascot and SEQUEST, two search algorithms widely used for peptide identification in mass spectrometry
In addition to PSM identifications based on a forward decoy database approach at 1% false discovery rate (FDR), we show results for unique peptides at 1% FDR in Supporting Information Table S1
Summary
Associated with respective ion intensities, are matched to candidate peptides, and a score dependent on an identification algorithm is assigned to each peptide spectrum match (PSM). Scoring algorithms such as Mascot,[3] SEQUEST,[4] X-. SEQUEST reports a crosscorrelation score of the acquired mass spectrum matching a modeled peptide spectrum. Mascot estimates the probability that a particular peptide spectrum match is a random event by probabilistic modeling. Other search engines are designed for a particular purpose such as for the analysis of post-translationally modified peptides (e.g., ModifiComb[11] or InsPecT12)
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