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Automatic validation of phosphopeptide identifications from tandem mass spectra.

ABSTRACT: We developed and compared two approaches for automated validation of phosphopeptide tandem mass spectra identified using database searching algorithms. Phosphopeptide identifications were obtained through SEQUEST searches of a protein database appended with its decoy (reversed sequences). Statistical evaluation and iterative searches were employed to create a high-quality data set of phosphopeptides. Automation of postsearch validation was approached by two different strategies. By using statistical multiple testing, we calculate a p value for each tentative peptide phosphorylation. In a second method, we use a support vector machine (SVM; a machine learning algorithm) binary classifier to predict whether a tentative peptide phosphorylation is true. We show good agreement (85%) between postsearch validation of phosphopeptide/spectrum matches by multiple testing and that from support vector machines. Automatic methods conform very well with manual expert validation in a blinded test. Additionally, the algorithms were tested on the identification of synthetic phosphopeptides. We show that phosphate neutral losses in tandem mass spectra can be used to assess the correctness of phosphopeptide/spectrum matches. An SVM classifier with a radial basis function provided classification accuracy from 95.7% to 96.8% of the positive data set, depending on search algorithm used. Establishing the efficacy of an identification is a necessary step for further postsearch interrogation of the spectra for complete localization of phosphorylation sites. Our current implementation performs validation of phosphoserine/phosphothreonine-containing peptides having one or two phosphorylation sites from data gathered on an ion trap mass spectrometer. The SVM-based algorithm has been implemented in the software package DeBunker. We illustrate the application of the SVM-based software DeBunker on a large phosphorylation data set.

SUBMITTER: Lu B

PROVIDER: S-EPMC2527591 | biostudies-literature | 2007 Feb

REPOSITORIES: biostudies-literature

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Automatic validation of phosphopeptide identifications from tandem mass spectra.

Lu Bingwen B Ruse Cristian C Xu Tao T Park Sung Kyu SK Yates John J

Analytical chemistry 20070201 4

We developed and compared two approaches for automated validation of phosphopeptide tandem mass spectra identified using database searching algorithms. Phosphopeptide identifications were obtained through SEQUEST searches of a protein database appended with its decoy (reversed sequences). Statistical evaluation and iterative searches were employed to create a high-quality data set of phosphopeptides. Automation of postsearch validation was approached by two different strategies. By using statist ...[more]

PMID: 17297928

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Project description:The complexity of proteomic instrumentation for LC-MS/MS introduces many possible sources of variability. Data-dependent sampling of peptides constitutes a stochastic element at the heart of discovery proteomics. Although this variation impacts the identification of peptides, proteomic identifications are far from completely random. In this study, we analyzed interlaboratory data sets from the NCI Clinical Proteomic Technology Assessment for Cancer to examine repeatability and reproducibility in peptide and protein identifications. Included data spanned 144 LC-MS/MS experiments on four Thermo LTQ and four Orbitrap instruments. Samples included yeast lysate, the NCI-20 defined dynamic range protein mix, and the Sigma UPS 1 defined equimolar protein mix. Some of our findings reinforced conventional wisdom, such as repeatability and reproducibility being higher for proteins than for peptides. Most lessons from the data, however, were more subtle. Orbitraps proved capable of higher repeatability and reproducibility, but aberrant performance occasionally erased these gains. Even the simplest protein digestions yielded more peptide ions than LC-MS/MS could identify during a single experiment. We observed that peptide lists from pairs of technical replicates overlapped by 35-60%, giving a range for peptide-level repeatability in these experiments. Sample complexity did not appear to affect peptide identification repeatability, even as numbers of identified spectra changed by an order of magnitude. Statistical analysis of protein spectral counts revealed greater stability across technical replicates for Orbitraps, making them superior to LTQ instruments for biomarker candidate discovery. The most repeatable peptides were those corresponding to conventional tryptic cleavage sites, those that produced intense MS signals, and those that resulted from proteins generating many distinct peptides. Reproducibility among different instruments of the same type lagged behind repeatability of technical replicates on a single instrument by several percent. These findings reinforce the importance of evaluating repeatability as a fundamental characteristic of analytical technologies.

Dataset Information

Automatic validation of phosphopeptide identifications from tandem mass spectra.

Publications

Automatic validation of phosphopeptide identifications from tandem mass spectra.

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