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      Discovery and Visualization of Uncharacterized Drug–Protein Adducts Using Mass Spectrometry

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          Abstract

          Drugs are often metabolized to reactive intermediates that form protein adducts. Adducts can inhibit protein activity, elicit immune responses, and cause life-threatening adverse drug reactions. The masses of reactive metabolites are frequently unknown, rendering traditional mass spectrometry-based proteomics approaches incapable of adduct identification. Here, we present Magnum, an open-mass search algorithm optimized for adduct identification, and Limelight, a web-based data processing package for analysis and visualization of data from all existing algorithms. Limelight incorporates tools for sample comparisons and xenobiotic-adduct discovery. We validate our tools with three drug/protein combinations and apply our label-free workflow to identify novel xenobiotic-protein adducts in CYP3A4. Our new methods and software enable accurate identification of xenobiotic-protein adducts with no prior knowledge of adduct masses or protein targets. Magnum outperforms existing label-free tools in xenobiotic-protein adduct discovery, while Limelight fulfills a major need in the rapidly developing field of open-mass searching, which until now lacked comprehensive data visualization tools.

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          Most cited references50

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          Skyline: an open source document editor for creating and analyzing targeted proteomics experiments.

          Skyline is a Windows client application for targeted proteomics method creation and quantitative data analysis. It is open source and freely available for academic and commercial use. The Skyline user interface simplifies the development of mass spectrometer methods and the analysis of data from targeted proteomics experiments performed using selected reaction monitoring (SRM). Skyline supports using and creating MS/MS spectral libraries from a wide variety of sources to choose SRM filters and verify results based on previously observed ion trap data. Skyline exports transition lists to and imports the native output files from Agilent, Applied Biosystems, Thermo Fisher Scientific and Waters triple quadrupole instruments, seamlessly connecting mass spectrometer output back to the experimental design document. The fast and compact Skyline file format is easily shared, even for experiments requiring many sample injections. A rich array of graphs displays results and provides powerful tools for inspecting data integrity as data are acquired, helping instrument operators to identify problems early. The Skyline dynamic report designer exports tabular data from the Skyline document model for in-depth analysis with common statistical tools. Single-click, self-updating web installation is available at http://proteome.gs.washington.edu/software/skyline. This web site also provides access to instructional videos, a support board, an issues list and a link to the source code project.
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            A Cross-platform Toolkit for Mass Spectrometry and Proteomics

            Mass-spectrometry-based proteomics has become an important component of biological research. Numerous proteomics methods have been developed to identify and quantify the proteins in biological and clinical samples 1 , identify pathways affected by endogenous and exogenous perturbations 2 , and characterize protein complexes 3 . Despite successes, the interpretation of vast proteomics datasets remains a challenge. There have been several calls for improvements and standardization of proteomics data analysis frameworks, as well as for an application-programming interface for proteomics data access 4,5 . In response, we have developed the ProteoWizard Toolkit, a robust set of open-source, software libraries and applications designed to facilitate proteomics research. The libraries implement the first-ever, non-commercial, unified data access interface for proteomics, bridging field-standard open formats and all common vendor formats. In addition, diverse software classes enable rapid development of vendor-agnostic proteomics software. Additionally, ProteoWizard projects and applications, building upon the core libraries, are becoming standard tools for enabling significant proteomics inquiries.
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              Semi-supervised learning for peptide identification from shotgun proteomics datasets.

              Shotgun proteomics uses liquid chromatography-tandem mass spectrometry to identify proteins in complex biological samples. We describe an algorithm, called Percolator, for improving the rate of confident peptide identifications from a collection of tandem mass spectra. Percolator uses semi-supervised machine learning to discriminate between correct and decoy spectrum identifications, correctly assigning peptides to 17% more spectra from a tryptic Saccharomyces cerevisiae dataset, and up to 77% more spectra from non-tryptic digests, relative to a fully supervised approach.
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                Author and article information

                Journal
                Anal Chem
                Anal Chem
                ac
                ancham
                Analytical Chemistry
                American Chemical Society
                0003-2700
                1520-6882
                21 February 2022
                01 March 2022
                : 94
                : 8
                : 3501-3509
                Affiliations
                []Department of Biochemistry, University of Washington , Seattle, Washington 98195, United States
                []Institute for Systems Biology , Seattle, Washington 98109, United States
                [§ ]Department of Pharmaceutics, University of Washington , Seattle, Washington 98195, United States
                []Department of Genome Sciences, University of Washington , Seattle, Washington 98195, United States
                Author notes
                Article
                10.1021/acs.analchem.1c04101
                8892443
                35184559
                855cfcaf-b000-44b0-b94a-37e569210ee2
                © 2022 The Authors. Published by American Chemical Society

                Permits non-commercial access and re-use, provided that author attribution and integrity are maintained; but does not permit creation of adaptations or other derivative works ( https://creativecommons.org/licenses/by-nc-nd/4.0/).

                Funding
                Funded by: National Institute on Aging, doi 10.13039/100000049;
                Award ID: U19AG023122
                Funded by: University of Washington, doi 10.13039/100007812;
                Award ID: UWPR95794
                Funded by: National Institute of General Medical Sciences, doi 10.13039/100000057;
                Award ID: P41GM103533
                Categories
                Article
                Custom metadata
                ac1c04101
                ac1c04101

                Analytical chemistry
                Analytical chemistry

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