DtaRefinery, a Software Tool for Elimination of Systematic Errors from Parent Ion Mass Measurements in Tandem Mass Spectra Data Sets

Abstract

Hybrid two-stage mass spectrometers capable of both highly accurate mass measurement and high throughput MS/MS fragmentation have become widely available in recent years, allowing for significantly better discrimination between true and false MS/MS peptide identifications by the application of a relatively narrow window for maximum allowable deviations of measured parent ion masses. To fully gain the advantage of highly accurate parent ion mass measurements, it is important to limit systematic mass measurement errors. Based on our previous studies of systematic biases in mass measurement errors, here, we have designed an algorithm and software tool that eliminates the systematic errors from the peptide ion masses in MS/MS data. We demonstrate that the elimination of the systematic mass measurement errors allows for the use of tighter criteria on the deviation of measured mass from theoretical monoisotopic peptide mass, resulting in a reduction of both false discovery and false negative rates of peptide identification. A software implementation of this algorithm called DtaRefinery reads a set of fragmentation spectra, searches for MS/MS peptide identifications using a FASTA file containing expected protein sequences, fits a regression model that can estimate systematic errors, and then corrects the parent ion mass entries by removing the estimated systematic error components. The output is a new file with fragmentation spectra with updated parent ion masses. The software is freely available. Molecular & Cellular Proteomics 9: 486 -496, 2010.A key component in modern proteomics research is peptide identification through LC coupled to tandem MS where a selected parent or precursor ion from an MS scan undergoes fragmentation by collisionally activated/induced dissociation or any other methods (1). Identification of the putative peptides corresponding to the parent ions selected for fragmentation is performed by matching the observed to the theoretical MS/MS fragmentation patterns. The first step in the data analysis process is to create a set of input files representing the fragmentation spectra. For example, for the data sets from LTQ 1 FT and LTQ Orbitrap instruments, software tools such as extract_msn (part of BioWorks software package, Thermo Electron, San Jose, CA) or DeconMSn (2) are often used for this step, creating files in ".dta" or other formats for the fragmentation spectra. These files contain the mass and charge of the parent ion and observed fragmentation pattern in the form of a list of m/z and intensity pairs. Once created, database search tools such as SEQUEST (3), X!Tandem (4), OMSSA (5), InsPect (6), MASCOT (7), Spectrum Mill (8), RAId_DbS (9), and others are used to analyze the .dta files to associate each MS/MS fragmentation pattern with a corresponding putative peptide sequence. Therefore, MS/MS fragmentation pattern information plays a primary role and ultimately can be used as essentially the only type of information for peptide identification in LC-MS/MS experiments (4, 10). However, in thi...

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“…DtaRefinery performs a preliminary database search using X!Tandem and knowledge of peptide identifications is utilized in multivariate calibration to reduce systematic instrumental mass errors. 4 And then iPE-MMR generates MS/MS files that are refined and calibrated, which are subsequently subjected to a several database search. In order to utilize protein database search engines for identification of peptides and proteins, whole iPE-MMR processed data must convert to mzXML file format.…”

“…2 This method integrates steps (1) generation of refined MS/MS data by DeconMSn; 3 (2) additional refinement of the resultant MS/MS data by a modified version of PE-MMR; and (3) elimination of systematic errors of precursor masses using DtaRefinery. 4 As a result iPE-MMR increases sensitivity in peptide identification and provides increased accuracy when applied to complex high-throughput proteomics data. The second problem can be combated with the help of various advanced *Reprint requests to Jong-Moon Park E-mail: bio4647@naver.com…”