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      Quantifying label enrichment from two mass isotopomers increases proteome coverage for in vivo protein turnover using heavy water metabolic labeling

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          Abstract

          Heavy water metabolic labeling followed by liquid chromatography coupled with mass spectrometry is a powerful high throughput technique for measuring the turnover rates of individual proteins in vivo. The turnover rate is obtained from the exponential decay modeling of the depletion of the monoisotopic relative isotope abundance. We provide theoretical formulas for the time course dynamics of six mass isotopomers and use the formulas to introduce a method that utilizes partial isotope profiles, only two mass isotopomers, to compute protein turnover rate. The use of partial isotope profiles alleviates the interferences from co-eluting contaminants in complex proteome mixtures and improves the accuracy of the estimation of label enrichment. In five different datasets, the technique consistently doubles the number of peptides with high goodness-of-fit characteristics of the turnover rate model. We also introduce a software tool, d2ome+, which automates the protein turnover estimation from partial isotope profiles.

          Abstract

          Heavy water metabolic labeling followed by liquid chromatography coupled mass spectrometry (LC-MS) is a powerful approach to characterize in vivo protein turnover rates, however, peptide co-elution causes overlap of their isotope profiles in LC-MS and affects the proteome coverage. Here, the authors develop an approach to increase the proteome coverage for in vivo protein turnover by using partial isotope profiles from two mass isotopomers.

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          Most cited references55

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          The STRING database in 2017: quality-controlled protein–protein association networks, made broadly accessible

          A system-wide understanding of cellular function requires knowledge of all functional interactions between the expressed proteins. The STRING database aims to collect and integrate this information, by consolidating known and predicted protein–protein association data for a large number of organisms. The associations in STRING include direct (physical) interactions, as well as indirect (functional) interactions, as long as both are specific and biologically meaningful. Apart from collecting and reassessing available experimental data on protein–protein interactions, and importing known pathways and protein complexes from curated databases, interaction predictions are derived from the following sources: (i) systematic co-expression analysis, (ii) detection of shared selective signals across genomes, (iii) automated text-mining of the scientific literature and (iv) computational transfer of interaction knowledge between organisms based on gene orthology. In the latest version 10.5 of STRING, the biggest changes are concerned with data dissemination: the web frontend has been completely redesigned to reduce dependency on outdated browser technologies, and the database can now also be queried from inside the popular Cytoscape software framework. Further improvements include automated background analysis of user inputs for functional enrichments, and streamlined download options. The STRING resource is available online, at http://string-db.org/.
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            The Gene Ontology resource: enriching a GOld mine

            Abstract The Gene Ontology Consortium (GOC) provides the most comprehensive resource currently available for computable knowledge regarding the functions of genes and gene products. Here, we report the advances of the consortium over the past two years. The new GO-CAM annotation framework was notably improved, and we formalized the model with a computational schema to check and validate the rapidly increasing repository of 2838 GO-CAMs. In addition, we describe the impacts of several collaborations to refine GO and report a 10% increase in the number of GO annotations, a 25% increase in annotated gene products, and over 9,400 new scientific articles annotated. As the project matures, we continue our efforts to review older annotations in light of newer findings, and, to maintain consistency with other ontologies. As a result, 20 000 annotations derived from experimental data were reviewed, corresponding to 2.5% of experimental GO annotations. The website (http://geneontology.org) was redesigned for quick access to documentation, downloads and tools. To maintain an accurate resource and support traceability and reproducibility, we have made available a historical archive covering the past 15 years of GO data with a consistent format and file structure for both the ontology and annotations.
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              The CRAPome: a Contaminant Repository for Affinity Purification Mass Spectrometry Data

              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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                Author and article information

                Contributors
                rgsadygo@utmb.edu
                Journal
                Commun Chem
                Commun Chem
                Communications Chemistry
                Nature Publishing Group UK (London )
                2399-3669
                17 April 2023
                17 April 2023
                2023
                : 6
                : 72
                Affiliations
                [1 ]GRID grid.176731.5, ISNI 0000 0001 1547 9964, Department of Biochemistry and Molecular Biology, , The University of Texas Medical Branch, ; Galveston, TX USA
                [2 ]GRID grid.176731.5, ISNI 0000 0001 1547 9964, Department of Surgery, , The University of Texas Medical Branch, ; Galveston, TX USA
                [3 ]GRID grid.176731.5, ISNI 0000 0001 1547 9964, Sealy Center on Aging, , The University of Texas Medical Branch, ; Galveston, TX USA
                [4 ]GRID grid.176731.5, ISNI 0000 0001 1547 9964, Department of Neuroscience, Cell Biology and Anatomy, , The University of Texas Medical Branch, ; Galveston, TX USA
                [5 ]GRID grid.274264.1, ISNI 0000 0000 8527 6890, Oklahoma Medical Research Foundation, , Oklahoma Nathan Shock Center, Oklahoma Center for Geosciences, Harold Hamm Diabetes Center, ; Oklahoma City, OK USA
                [6 ]Oklahoma City Veterans Association, Oklahoma City, OK USA
                [7 ]GRID grid.27755.32, ISNI 0000 0000 9136 933X, Present Address: Department of Molecular Physiology and Biological Physics, , The University of Virginia, ; Charlottesville, VA USA
                Author information
                http://orcid.org/0000-0003-1590-155X
                Article
                873
                10.1038/s42004-023-00873-x
                10110577
                37069333
                f1d92f5b-02c3-4e5e-9982-f6a9a85fbc55
                © The Author(s) 2023

                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 license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license 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 license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 3 October 2022
                : 31 March 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 :
                Funded by: FundRef https://doi.org/10.13039/100000049, U.S. Department of Health & Human Services | NIH | National Institute on Aging (U.S. National Institute on Aging);
                Award ID: R01AG074551
                Award ID: P30-AG024832
                Award Recipient :
                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 :
                Funded by: U.S. Department of Health & Human Services | NIH | National Institute on Aging (U.S. National Institute on Aging)
                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 :
                Funded by: FundRef https://doi.org/10.13039/100000968, American Heart Association (American Heart Association, Inc.);
                Award ID: 20TPA35490206
                Award Recipient :
                Categories
                Article
                Custom metadata
                © The Author(s) 2023

                proteome,mass spectrometry,peptides,networks and systems biology,protein-protein interaction networks

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