pParse: A method for accurate determination of monoisotopic peaks in high‐resolution mass spectra

Yuan, Zuo-F ei; Liu, Chao; Wang, Haipeng; Sun, Rui-Xiang; Fu, Yan; Zhang, Jingfen; Wang, Le-Heng; Chi, Hao; You, Li; Xiu, Li-Yun; Wang, Wenping; He, Shan

doi:10.1002/pmic.201100081

Cited by 73 publications

(73 citation statements)

References 42 publications

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“…The average value of the mass difference between adjacent isotopic peaks is 1.0032 Da (noted as d) (27). The m/z value of the mono-isotopic peak (noted as P0) is mz, the m/z value of the first isotope (noted as P1) is mzϩd/c, and the m/z value of the second isotope (noted as P2) is mzϩ2*d/c.…”

Section: Methodsmentioning

confidence: 99%

EpiProfile Quantifies Histone Peptides With Modifications by Extracting Retention Time and Intensity in High-resolution Mass Spectra*

Yuan

Lin

Molden

et al. 2015

Molecular & Cellular Proteomics

108

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Histone post-translational modifications contribute to chromatin function through their chemical properties which influence chromatin structure and their ability to recruit chromatin interacting proteins. Nanoflow liquid chromatography coupled with high resolution tandem mass spectrometry (nanoLC-MS/MS) has emerged as the most suitable technology for global histone modification analysis because of the high sensitivity and the high mass accuracy of this approach that provides confident identification. However, analysis of histones with this method is even more challenging because of the large number and variety of isobaric histone peptides and the high dynamic range of histone peptide abundances. Here, we introduce EpiProfile, a software tool that discriminates isobaric histone peptides using the distinguishing fragment ions in their tandem mass spectra and extracts the chromatographic area under the curve using previous knowledge about peptide retention time. The accuracy of EpiProfile was evaluated by analysis of mixtures containing different ratios of synthetic histone peptides. In addition to labelfree quantification of histone peptides, EpiProfile is flexible and can quantify different types of isotopically labeled histone peptides. EpiProfile is unique in generating layouts (i.e. relative retention time) of histone peptides when compared with manual quantification of the data and other programs (such as Skyline), filling the need of an automatic and freely available tool to quantify labeled and non-labeled modified histone peptides. In summary, EpiProfile is a valuable nanoflow liquid chromatography coupled with high resolution tandem mass spectrometrybased quantification tool for histone peptides, which can also be adapted to analyze nonhistone protein samples. Molecular & Cellular

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Section: Methodsmentioning

confidence: 99%

EpiProfile Quantifies Histone Peptides With Modifications by Extracting Retention Time and Intensity in High-resolution Mass Spectra*

Yuan

Lin

Molden

et al. 2015

Molecular & Cellular Proteomics

108

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“…Proteomics MS Data Analysis Including Peptide Identification and Quantification-RAW files were converted to MS1 and MS2 files by xtract 1.0.0.8 in pFind Studio 2.8 (pfind.ict.ac.cn) and converted to MGF files by pParse to calibrate the precursor isotopic mass to the monoisotopic mass and export coeluted precursors, which could increase the identification rate (41). The MS1 and MS2 files were used for quantification and the MGF files were used for database searching and site localization.…”

Section: Methodsmentioning

confidence: 99%

A Novel Quantitative Mass Spectrometry Platform for Determining Protein O-GlcNAcylation Dynamics

Wang

Yuan

Fan

et al. 2016

Molecular & Cellular Proteomics

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Over the past decades, protein O-GlcNAcylation has been found to play a fundamental role in cell cycle control, metabolism, transcriptional regulation, and cellular signaling. Nevertheless, quantitative approaches to determine in vivo GlcNAc dynamics at a large-scale are still not readily available. Here, we have developed an approach to isotopically label O-GlcNAc modifications on proteins by producing 13 C-labeled UDP-GlcNAc from 13 C 6 -glucose via the hexosamine biosynthetic pathway. This metabolic labeling was combined with quantitative mass spectrometry-based proteomics to determine protein O-GlcNAcylation turnover rates. First, an efficient enrichment method for O-GlcNAc peptides was developed with the use of phenylboronic acid solid-phase extraction and anhydrous DMSO. The near stoichiometry reaction between the diol of GlcNAc and boronic acid dramatically improved the enrichment efficiency. Additionally, our kinetic model for turnover rates integrates both metabolomic and proteomic data, which increase the accuracy of the turnover rate estimation. Other advantages of this metabolic labeling method include in vivo application, direct labeling of the O-GlcNAc sites and higher confidence for site identification. Concentrating only on nuclear localized GlcNAc modified proteins, we are able to identify 105 O-GlcNAc peptides on 42 proteins and determine turnover rates of 20 O-GlcNAc peptides from 14 proteins extracted from HeLa nuclei. In general, we found O-GlcNAcylation turnover rates are slower than those published for phosphorylation or acetylation. Nevertheless, the rates widely varied depending on both the protein and the residue modified. We believe this methodology can be broadly applied to reveal turnovers/dynamics of protein OGlcNAcylation from different biological states and will provide more information on the significance of O-GlcNAcylation, enabling us to study the temporal dynamics of this critical modification for the first time. Molecular &

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“…However, the monoisotopic ions of longer peptides (with mass greater than 1,500 Da) may not be those with the highest intensities [39], making the above straightforward peak picking strategy far from ideal for large peptides. Existing tools available in the ProteoWizard software project [30] and pParse [40] have recently made progress on tackling this issue. The RawConverter algorithm also addresses this problem by combining the Averagine model [41] and the information of successive MS1 scans to select the monoisotopic peak for each precursor ion.…”

Section: Computational Pipelinementioning

confidence: 99%

From Raw Data to Biological Discoveries: A Computational Analysis Pipeline for Mass Spectrometry-Based Proteomics

Lavallée‐Adam

Park

Martínez‐Bartolomé

et al. 2015

J. Am. Soc. Mass Spectrom.

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In the last two decades, computational tools for mass spectrometry-based proteomics data analysis have evolved from a few stand-alone software solutions serving specific goals, such as the identification of amino acid sequences based on mass spectrometry spectra, to large-scale complex pipelines integrating multiple computer programs to solve a collection of problems. This software evolution has been mostly driven by the appearance of novel technologies that allowed the community to tackle complex biological problems, such as the identification of proteins that are differentially expressed in two samples under different conditions. The achievement of such objectives requires a large suite of programs to analyze the intricate mass spectrometry data. Our laboratory addresses complex proteomics questions by producing and using algorithms and software packages. Our current computational pipeline includes, among other things, tools for mass spectrometry raw data processing, peptide and protein identification and quantification, post-translational modification analysis, and protein functional enrichment analysis. In this paper, we describe a suite of software packages we have developed to process mass spectrometry-based proteomics data and we highlight some of the new features of previously published programs as well as tools currently under development.

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pParse: A method for accurate determination of monoisotopic peaks in high‐resolution mass spectra

Cited by 73 publications

References 42 publications

EpiProfile Quantifies Histone Peptides With Modifications by Extracting Retention Time and Intensity in High-resolution Mass Spectra*

EpiProfile Quantifies Histone Peptides With Modifications by Extracting Retention Time and Intensity in High-resolution Mass Spectra*

A Novel Quantitative Mass Spectrometry Platform for Determining Protein O-GlcNAcylation Dynamics

From Raw Data to Biological Discoveries: A Computational Analysis Pipeline for Mass Spectrometry-Based Proteomics

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