EBP, a Program for Protein Identification Using Multiple Tandem Mass Spectrometry Datasets

Abstract

MS/MSProteomics methods aim to identify the proteins expressed in biological samples (1) just like transcriptomics methods detect and quantify the abundance of RNA molecules (2). Both techniques can point to particular tissue correlates, such as disease biomarkers, and find networks of co-regulated genes and proteins that reveal the mechanisms underlying biological processes (3). As thousands of simultaneous measurements are taken, however, balancing sensitivity of detection against rates of false positive error is a major challenge. With rapid advances in large scale proteomics technology, there is an urgent need for methods that maximize sensitivity and control of false protein identifications by integrating the results of multiple experiments.In "shotgun" proteomics studies, a protein sample is digested with a proteolytic enzyme, and the resulting peptide mixture is separated by LC prior to conducting collisioninduced dissociation and MS/MS (1). This generates thousands of peptide ion fragmentation spectra containing diagnostic amino acid sequence information. Peptide sequences are inferred by matching the resulting product ion spectra to theoretical or empirical spectra in peptide sequence databases (4). No match is exact, so the peptide identifications are not certain. Database search algorithms either calculate a probability that the spectrum results from a random fragmentation (e.g. MASCOT, Ref. 5) or use heuristic scores (such as SEQUEST, Ref. 6), which can then be converted into probabilities of accurate peptide identification (7,8). A variety of other statistical models have also been proposed to score peptide matches (9). These algorithms have to some extent complementary merits, and the sensitivity and specificity of peptide sequence identifications from MS/MS data can be improved by the use of more than one search algorithm (10 -12). A recent comparison of five database search programs concluded with the recommendation to use consensus scoring from at least two different algorithms and noted the need for appropriate methods of combining scores from different algorithms (11). Database search methods using different assumptions and procedures can result in markedly different sets of protein identifications (e.g. Ref. 13).Probable peptide sequences need to be assembled to proteins (14). A simple method for identifying proteins is to filter the peptide matches using empirically derived thresholds and then sort by parent protein. Variants of this approach do not attempt to estimate false identification rates, nor do they deal in a useful way with "degenerate" peptides whose sequence matches more than one protein in the database (15-17). More sophisticated methods use statistical models to calculate protein expression probabilities (12, 18 -20) or ranking scores (21) based on the number and quality of peptide sequence matches and other criteria, such as protein length, the size of the database, and the degeneracy of the peptide sequence identifications. These models can be empirically validated by search...

Analysis of the Zebrafish Proteome during Embryonic Development

“…Two-dimensional Chromatography and Mass Spectrometry-Offline fractionation and LC-MS/MS were performed as described previously (30). The sample was loaded onto a PolySulfoethyl A column (100 mm ϫ 4.6 mm, 5 m, 300 Å; PolyLC, Columbia, MD) at a flow rate of 0.2 ml/min with mobile phase A (10 mM ammonium formate, 25% acetonitrile, pH 3).…”

“…Results from both searches (SEQUEST and MASCOT) and all biological replicates were combined in a single statistical analysis of protein expression per developmental stage using Empirical Bayes Protein Identifier (EBP) 1.0 as described previously (30). Briefly EBP estimates both sensitivity and false identification rate and has been validated empirically for analysis of zebrafish liquid ion trap mass spectrometry data using a reversed/forward sequence database search approach (30).…”

“…Sample preparation, protein digestion, and chromatographic separation were performed as described previously (30). Briefly, five tubes of 20 -30 embryos were lysed (7 M urea, 2 M thiourea, 4% CHAPS (all Amersham Biosciences), 100 mM DTT (Bio-Rad), Phosphatase Inhibitor Mixture 11 (Sigma), Complete protease inhibitor (Roche Applied Science)) and homogenized (TissueLyser, Qiagen; 2 ϫ 3 min, 30 Hz), and proteins were precipitated and resuspended in 0.2% (w/v) RapiGest TM SF (Waters Co., Milford, MA) in 50 mM ammonium bicarbonate (Sigma).…”

“…A major problem in two-dimensional chromatography-tandem mass spectrometry ("shotgun") proteomics approaches is that many protein identifications have low reproducibility if the sensitivity of detection is not carefully balanced against rates of false identification error by using multiple replicates and rigorous statistics (31). Thus, we applied a novel empirical Bayesian algorithm to integrate data sets from multiple search programs and multiple biological replicates (30). The data are accessible in a fully searchable database (see supplemental data for the URL database link under Instructions for Downloading) that links protein identifications to existing resources including the Zebrafish Information Network (ZFIN) 1 database (32).…”

“…We applied mass spectrometric proteomics methodology and statistical analysis (30) to create profiles of proteins expressed during zebrafish embryonic development. A major problem in two-dimensional chromatography-tandem mass spectrometry ("shotgun") proteomics approaches is that many protein identifications have low reproducibility if the sensitivity of detection is not carefully balanced against rates of false identification error by using multiple replicates and rigorous statistics (31).…”