A large-scale LC-MS dataset of murine liver proteome from time course of heavy water metabolic labeling

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Abstract

Metabolic stable isotope labeling with heavy water followed by liquid chromatography coupled with mass spectrometry (LC-MS) is a powerful tool for in vivo protein turnover studies. Several algorithms and tools have been developed to determine the turnover rates of peptides and proteins from time-course stable isotope labeling experiments. The availability of benchmark mass spectrometry data is crucial to compare and validate the effectiveness of newly developed techniques and algorithms. In this work, we report a heavy water-labeled LC-MS dataset from the murine liver for protein turnover rate analysis. The dataset contains eighteen mass spectral data with their corresponding database search results from nine different labeling durations and quantification outputs from d2ome+ software. The dataset also contains eight mass spectral data from two-dimensional fractionation experiments on unlabeled samples.

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The CRAPome: a Contaminant Repository for Affinity Purification Mass Spectrometry Data

Dattatreya Mellacheruvu, Zachary Wright, Amber L Couzens … (2013)

Affinity purification coupled with mass spectrometry (AP-MS) is now a widely used approach for the identification of protein-protein interactions. However, for any given protein of interest, determining which of the identified polypeptides represent bona fide interactors versus those that are background contaminants (e.g. proteins that interact with the solid-phase support, affinity reagent or epitope tag) is a challenging task. While the standard approach is to identify nonspecific interactions using one or more negative controls, most small-scale AP-MS studies do not capture a complete, accurate background protein set. Fortunately, negative controls are largely bait-independent. Hence, aggregating negative controls from multiple AP-MS studies can increase coverage and improve the characterization of background associated with a given experimental protocol. Here we present the Contaminant Repository for Affinity Purification (the CRAPome) and describe the use of this resource to score protein-protein interactions. The repository (currently available for Homo sapiens and Saccharomyces cerevisiae) and computational tools are freely available online at www.crapome.org.

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Probability-based protein identification by searching sequence databases using mass spectrometry data

David N Perkins, Darryl J. C. Pappin, David Creasy … (1999)

Several algorithms have been described in the literature for protein identification by searching a sequence database using mass spectrometry data. In some approaches, the experimental data are peptide molecular weights from the digestion of a protein by an enzyme. Other approaches use tandem mass spectrometry (MS/MS) data from one or more peptides. Still others combine mass data with amino acid sequence data. We present results from a new computer program, Mascot, which integrates all three types of search. The scoring algorithm is probability based, which has a number of advantages: (i) A simple rule can be used to judge whether a result is significant or not. This is particularly useful in guarding against false positives. (ii) Scores can be compared with those from other types of search, such as sequence homology. (iii) Search parameters can be readily optimised by iteration. The strengths and limitations of probability-based scoring are discussed, particularly in the context of high throughput, fully automated protein identification.

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ProteoWizard: open source software for rapid proteomics tools development

Darren Kessner, Matt Chambers, Robert A. Burke … (2008)

Summary: The ProteoWizard software project provides a modular and extensible set of open-source, cross-platform tools and libraries. The tools perform proteomics data analyses; the libraries enable rapid tool creation by providing a robust, pluggable development framework that simplifies and unifies data file access, and performs standard proteomics and LCMS dataset computations. The library contains readers and writers of the mzML data format, which has been written using modern C++ techniques and design principles and supports a variety of platforms with native compilers. The software has been specifically released under the Apache v2 license to ensure it can be used in both academic and commercial projects. In addition to the library, we also introduce a rapidly growing set of companion tools whose implementation helps to illustrate the simplicity of developing applications on top of the ProteoWizard library. Availability: Cross-platform software that compiles using native compilers (i.e. GCC on Linux, MSVC on Windows and XCode on OSX) is available for download free of charge, at http://proteowizard.sourceforge.net. This website also provides code examples, and documentation. It is our hope the ProteoWizard project will become a standard platform for proteomics development; consequently, code use, contribution and further development are strongly encouraged. Contact: darren@proteowizard.org; parag@ucla.edu Supplementary information: Supplementary data are available at Bioinformatics online.

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Author and article information

Contributors

Henock M. Deberneh: hmdebern@utmb.edu

Rovshan G. Sadygov:

ORCID: http://orcid.org/0000-0003-1590-155X

rgsadygo@utmb.edu

Journal

Journal ID (nlm-ta): Sci Data

Journal ID (iso-abbrev): Sci Data

Title: Scientific Data

Publisher: Nature Publishing Group UK (London )

ISSN (Electronic): 2052-4463

Publication date (Electronic): 19 September 2023

Publication date PMC-release: 19 September 2023

Publication date Collection: 2023

Volume: 10

Electronic Location Identifier: 635

Affiliations

[1 ]Department of Biochemistry and Molecular Biology, The University of Texas Medical Branch, ( https://ror.org/016tfm930) Galveston, Texas USA

[2 ]Department of Surgery, The University of Texas Medical Branch, ( https://ror.org/016tfm930) Galveston, Texas USA

[3 ]Sealy Center of Aging, The University of Texas Medical Branch, ( https://ror.org/016tfm930) Galveston, Texas USA

[4 ]Department of Neuroscience, Cell Biology and Anatomy, The University of Texas Medical Branch, ( https://ror.org/016tfm930) Galveston, Texas USA

[5 ]Aging and Metabolism Research Foundation, Oklahoma Medical Research Foundation, ( https://ror.org/035z6xf33) Oklahoma City, Oklahoma USA

[6 ]Oklahoma City VA, ( https://ror.org/010md9d18) Oklahoma City, Oklahoma USA

[7 ]Present Address: Department of Molecular Physiology and Biological Physics, The University of Virginia, ( https://ror.org/0153tk833) Charlottesville, Virginia USA

Author information

William K. Russell http://orcid.org/0000-0003-1931-4555

Benjamin F. Miller http://orcid.org/0000-0003-3283-0685

Rovshan G. Sadygov http://orcid.org/0000-0003-1590-155X

Article

Publisher ID: 2537

DOI: 10.1038/s41597-023-02537-w

PMC ID: 10509199

PubMed ID: 37726365

SO-VID: bf78af28-0264-429a-bd40-e80ff58e0db3

License:

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

History

Date received : 31 May 2023

Date accepted : 4 September 2023

Funding

Funded by: FundRef https://doi.org/10.13039/100000057, U.S. Department of Health & Human Services | NIH | National Institute of General Medical Sciences (NIGMS);

Award ID: R01GM112044

Award Recipient : Henock M. Deberneh Rovshan G. Sadygov

Funded by: U.S. Department of Health & Human Services | NIH | National Institute of General Medical Sciences (NIGMS)

Funded by: FundRef https://doi.org/10.13039/100000009, Foundation for the National Institutes of Health (Foundation for the National Institutes of Health, Inc.);

Award ID: P30-AG024832

Award ID: R01 AG074551

Award Recipient : Doaa R. Abdelrahman Andrew J. Murton Benjamin F. Miller

Funded by: FundRef https://doi.org/10.13039/100000050, U.S. Department of Health & Human Services | NIH | National Heart, Lung, and Blood Institute (NHLBI);

Award ID: 1R01HL157780-01A

Award Recipient : Sunil K. Verma Muge N. Kuyumcu-Martinez

Funded by: FundRef https://doi.org/10.13039/100004917, Cancer Prevention and Research Institute of Texas (Cancer Prevention Research Institute of Texas);

Award ID: RP190682

Award Recipient : Jennifer J. Linares William K. Russell

Funded by: U.S. Department of Health & Human Services | NIH | National Heart, Lung, and Blood Institute (NHLBI)

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Keywords: bioinformatics,mass spectrometry,proteomic analysis,protein-protein interaction networks

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Keywords: bioinformatics, mass spectrometry, proteomic analysis, protein-protein interaction networks

A large-scale LC-MS dataset of murine liver proteome from time course of heavy water metabolic labeling

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Most cited references 29

The CRAPome: a Contaminant Repository for Affinity Purification Mass Spectrometry Data

Probability-based protein identification by searching sequence databases using mass spectrometry data

ProteoWizard: open source software for rapid proteomics tools development

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