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A hierarchical MS2/MS3 database search algorithm for automated analysis of phosphopeptide tandem mass spectra.

ABSTRACT: A novel hierarchical MS(2)/MS(3) database search algorithm has been developed to analyze MS(2)/MS(3) phosphopeptides proteomic data. The algorithm is incorporated in an automated database search program, MassMatrix. The algorithm matches experimental MS(2) spectra against a supplied protein database to determine candidate peptide matches. It then matches the corresponding experimental MS(3) spectra against those candidate peptide matches. The MS(2) and MS(3) spectra are used in concert to arrive at peptide matches with overall higher confidence rather than combining MS(2) and MS(3) data searched separately. Receiver operating characteristic analysis showed that hierarchical MS(2)/MS(3) database searches with MassMatrix had better sensitivity and specificity than the two-stage MS(2)/MS(3) database searches obtained with MassMatrix, MASCOT, and X!Tandem. A greater number of true peptide matches at a given false rate were identified by use of this new algorithm for data collected on both LCQ and LTQ-FTICR mass spectrometers. The additional MS(3) spectral data also improved the overall reliability and the number of true positives (TPs) due to the fact that the TPs of the MS(2)/MS(3) search results had higher scores than those of the MS(2).

SUBMITTER: Xu H

PROVIDER: S-EPMC2775432 | biostudies-literature | 2009 Apr

REPOSITORIES: biostudies-literature

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A hierarchical MS2/MS3 database search algorithm for automated analysis of phosphopeptide tandem mass spectra.

Xu Hua H Wang Liwen L Sallans Larry L Freitas Michael A MA

Proteomics 20090401 7

A novel hierarchical MS(2)/MS(3) database search algorithm has been developed to analyze MS(2)/MS(3) phosphopeptides proteomic data. The algorithm is incorporated in an automated database search program, MassMatrix. The algorithm matches experimental MS(2) spectra against a supplied protein database to determine candidate peptide matches. It then matches the corresponding experimental MS(3) spectra against those candidate peptide matches. The MS(2) and MS(3) spectra are used in concert to arrive ...[more]

PMID: 19288523

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Project description:Peptide characterization using electron transfer dissociation (ETD) is an important analytical tool for protein identification. The fragmentation observed in ETD spectra is complementary to that seen when using the traditional dissociation method, collision activated dissociation (CAD). Applications of ETD enhance the scope and complexity of the peptides that can be studied by mass spectrometry-based methods. For example, ETD is shown to be particularly useful for the study of post-translationally modified peptides. To take advantage of the power provided by ETD, it is important to have an ETD-specific database search engine, an integral tool of mass spectrometry-based analytical proteomics. In this paper, we report on our development of a database search engine using ETD spectra and protein sequence databases to identify peptides. The search engine is based on the probabilistic modeling of shared peaks count and shared peaks intensity between the spectra and the peptide sequences. The shared peaks count accounts for the cumulative variations from amino acid sequences, while shared peaks intensity models the variations between the candidate sequence and product ion intensities. To demonstrate the utility of this algorithm for searching real-world data, we present the results of applications of this model to two high-throughput data sets. Both data sets were obtained from yeast whole cell lysates. The first data set was obtained from a sample digested by Lys-C, and the second data set was obtained by a digestion using trypsin. We searched the data sets against a combined forward and reversed yeast protein database to estimate false discovery rates. We compare the search results from the new methods with the results from a search engine often employed for ETD spectra, OMSSA. Our findings show that overall the new model performs comparably to OMSSA for low false discovery rates. At the same time, we demonstrate that there are substantial differences with OMSSA for results on subsets of data. Therefore, we conclude the new model can be considered as being complementary to previously developed models.

Project description:Glycosylation is a common protein modification with a significant role in many vital cellular processes and human diseases, making the characterization of protein-attached glycan structures important for understanding cell biology and disease processes. Direct analysis of protein N-glycosylation by tandem mass spectrometry of glycopeptides promises site-specific elucidation of N-glycan microheterogeneity, something that detached N-glycan and deglycosylated peptide analyses cannot provide. However, successful implementation of direct N-glycopeptide analysis by tandem mass spectrometry remains a challenge. In this work, we consider algorithmic techniques for the analysis of LC-MS/MS data acquired from glycopeptide-enriched fractions of enzymatic digests of purified proteins. We implement a computational strategy that takes advantage of the properties of CID fragmentation spectra of N-glycopeptides, matching the MS/MS spectra to peptide-glycan pairs from protein sequences and glycan structure databases. Significantly, we also propose a novel false discovery rate estimation technique to estimate and manage the number of false identifications. We use a human glycoprotein standard, haptoglobin, digested with trypsin and GluC, enriched for glycopeptides using HILIC chromatography, and analyzed by LC-MS/MS to demonstrate our algorithmic strategy and evaluate its performance. Our software, GlycoPeptideSearch (GPS), assigned glycopeptide identifications to 246 of the spectra at a false discovery rate of 5.58%, identifying 42 distinct haptoglobin peptide-glycan pairs at each of the four haptoglobin N-linked glycosylation sites. We further demonstrate the effectiveness of this approach by analyzing plasma-derived haptoglobin, identifying 136 N-linked glycopeptide spectra at a false discovery rate of 0.4%, representing 15 distinct glycopeptides on at least three of the four N-linked glycosylation sites. The software, GlycoPeptideSearch, is available for download from http://edwardslab.bmcb.georgetown.edu/GPS .

Dataset Information

A hierarchical MS2/MS3 database search algorithm for automated analysis of phosphopeptide tandem mass spectra.

Publications

A hierarchical MS2/MS3 database search algorithm for automated analysis of phosphopeptide tandem mass spectra.

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