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      The Archaeal Proteome Project advances knowledge about archaeal cell biology through comprehensive proteomics

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          Abstract

          While many aspects of archaeal cell biology remain relatively unexplored, systems biology approaches like mass spectrometry (MS) based proteomics offer an opportunity for rapid advances. Unfortunately, the enormous amount of MS data generated often remains incompletely analyzed due to a lack of sophisticated bioinformatic tools and field-specific biological expertise for data interpretation. Here we present the initiation of the Archaeal Proteome Project (ArcPP), a community-based effort to comprehensively analyze archaeal proteomes. Starting with the model archaeon Haloferax volcanii, we reanalyze MS datasets from various strains and culture conditions. Optimized peptide spectrum matching, with strict control of false discovery rates, facilitates identifying > 72% of the reference proteome, with a median protein sequence coverage of 51%. These analyses, together with expert knowledge in diverse aspects of cell biology, provide meaningful insights into processes such as N-terminal protein maturation, N-glycosylation, and metabolism. Altogether, ArcPP serves as an invaluable blueprint for comprehensive prokaryotic proteomics.

          Abstract

          While archaeal proteomics advanced rapidly, a comprehensive proteome database for archaea is lacking. Therefore, the authors here launch the Archaeal Proteome Project, a community-effort providing insights into archaeal cell biology via the combined reanalysis of Haloferax volcanii proteomics data.

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          MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification.

          Efficient analysis of very large amounts of raw data for peptide identification and protein quantification is a principal challenge in mass spectrometry (MS)-based proteomics. Here we describe MaxQuant, an integrated suite of algorithms specifically developed for high-resolution, quantitative MS data. Using correlation analysis and graph theory, MaxQuant detects peaks, isotope clusters and stable amino acid isotope-labeled (SILAC) peptide pairs as three-dimensional objects in m/z, elution time and signal intensity space. By integrating multiple mass measurements and correcting for linear and nonlinear mass offsets, we achieve mass accuracy in the p.p.b. range, a sixfold increase over standard techniques. We increase the proportion of identified fragmentation spectra to 73% for SILAC peptide pairs via unambiguous assignment of isotope and missed-cleavage state and individual mass precision. MaxQuant automatically quantifies several hundred thousand peptides per SILAC-proteome experiment and allows statistically robust identification and quantification of >4,000 proteins in mammalian cell lysates.
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            The PRIDE database and related tools and resources in 2019: improving support for quantification data

            Abstract The PRoteomics IDEntifications (PRIDE) database (https://www.ebi.ac.uk/pride/) is the world’s largest data repository of mass spectrometry-based proteomics data, and is one of the founding members of the global ProteomeXchange (PX) consortium. In this manuscript, we summarize the developments in PRIDE resources and related tools since the previous update manuscript was published in Nucleic Acids Research in 2016. In the last 3 years, public data sharing through PRIDE (as part of PX) has definitely become the norm in the field. In parallel, data re-use of public proteomics data has increased enormously, with multiple applications. We first describe the new architecture of PRIDE Archive, the archival component of PRIDE. PRIDE Archive and the related data submission framework have been further developed to support the increase in submitted data volumes and additional data types. A new scalable and fault tolerant storage backend, Application Programming Interface and web interface have been implemented, as a part of an ongoing process. Additionally, we emphasize the improved support for quantitative proteomics data through the mzTab format. At last, we outline key statistics on the current data contents and volume of downloads, and how PRIDE data are starting to be disseminated to added-value resources including Ensembl, UniProt and Expression Atlas.
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              Universal sample preparation method for proteome analysis.

              We describe a method, filter-aided sample preparation (FASP), which combines the advantages of in-gel and in-solution digestion for mass spectrometry-based proteomics. We completely solubilized the proteome in sodium dodecyl sulfate, which we then exchanged by urea on a standard filtration device. Peptides eluted after digestion on the filter were pure, allowing single-run analyses of organelles and an unprecedented depth of proteome coverage.
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                Author and article information

                Contributors
                pohlschr@sas.upenn.edu
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                19 June 2020
                19 June 2020
                2020
                : 11
                : 3145
                Affiliations
                [1 ]ISNI 0000 0004 1936 8972, GRID grid.25879.31, Department of Biology, , University of Pennsylvania, ; Philadelphia, PA 19104 USA
                [2 ]ISNI 0000 0004 1936 8091, GRID grid.15276.37, Department of Microbiology and Cell Science, Institute of Food and Agricultural Sciences, , University of Florida, ; Gainesville, FL 32603 USA
                [3 ]ISNI 0000 0000 9969 0902, GRID grid.412221.6, Institute of Biological Research (IIB-CONICET-UNMDP), , National University of Mar del Plata, ; Mar del Plata, 7600 Argentina
                [4 ]ISNI 0000 0001 2190 5763, GRID grid.7727.5, Biochemistry III – Institute for Biochemistry, Genetics and Microbiology, , University of Regensburg, ; 93053 Regensburg, Germany
                [5 ]ISNI 0000 0001 2190 4373, GRID grid.7700.0, Institute of Pharmacy and Molecular Biotechnology, , Heidelberg University, ; 69120 Heidelberg, Germany
                [6 ]ISNI 0000 0001 2172 9288, GRID grid.5949.1, Institute of Biology and Biotechnology of Plants, , University of Münster, ; 48143 Münster, Germany
                [7 ]ISNI 0000 0001 1302 4472, GRID grid.261356.5, Institute of Plant Science and Resources, , Okayama University, ; Kurashiki, Okayama 710-0046 Japan
                [8 ]ISNI 0000 0001 2104 4211, GRID grid.418140.8, Bioanalytical Mass Spectrometry Group, , Max Planck Institute for Biophysical Chemistry, ; 37077 Göttingen, Germany
                [9 ]ISNI 0000 0001 0482 5331, GRID grid.411984.1, Institute of Clinical Chemistry, , University Medical Center Göttingen, ; 37075 Göttingen, Germany
                [10 ]ISNI 0000 0004 1936 9748, GRID grid.6582.9, Biology II, Ulm University, ; 89069 Ulm, Germany
                [11 ]ISNI 0000 0004 1936 8091, GRID grid.15276.37, Genetics Institute, , University of Florida, ; Gainesville, FL 32608 USA
                [12 ]ISNI 0000 0004 0491 845X, GRID grid.418615.f, Computational Biology Group, , Max Planck Institute of Biochemistry, ; 82152 Martinsried, Germany
                [13 ]ISNI 0000 0004 0490 981X, GRID grid.5570.7, Plant Biochemistry, , Ruhr University Bochum, ; 44801 Bochum, Germany
                [14 ]Center for Marine and Molecular Biotechnology, Qingdao, 266237 China
                [15 ]ISNI 0000 0001 2152 3263, GRID grid.4422.0, College of Marine Life Sciences, , Ocean University of China, ; Qingdao, 266003 China
                Author information
                http://orcid.org/0000-0002-4771-7987
                http://orcid.org/0000-0002-5095-4086
                http://orcid.org/0000-0001-8889-4245
                http://orcid.org/0000-0002-0522-843X
                http://orcid.org/0000-0001-9670-6101
                http://orcid.org/0000-0002-0946-8166
                http://orcid.org/0000-0002-1382-1794
                http://orcid.org/0000-0001-6105-0923
                http://orcid.org/0000-0003-4691-3246
                http://orcid.org/0000-0001-7729-1342
                Article
                16784
                10.1038/s41467-020-16784-7
                7305310
                32561711
                75bd3043-d1d5-4246-a920-9a1cd87ea719
                © The Author(s) 2020

                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
                : 30 December 2019
                : 18 May 2020
                Funding
                Funded by: FundRef https://doi.org/10.13039/501100001659, Deutsche Forschungsgemeinschaft (German Research Foundation);
                Award ID: 398625447
                Award ID: HI737/12-1
                Award ID: MA1538/24-1
                Award Recipient :
                Funded by: FundRef https://doi.org/10.13039/100011373, International Agency for the Prevention of Blindness (IAPB);
                Funded by: National Agency for the Promotion of Science and Technology -ANPCyT- (PICT1477) and MINCyT-BMBF (Argentina-Germany) (AL/13/02)
                Funded by: FundRef https://doi.org/10.13039/100000015, U.S. Department of Energy (DOE);
                Award ID: DOE DE-FG02-05ER15650
                Award Recipient :
                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: NIH R01 GM57498
                Award Recipient :
                Funded by: FundRef https://doi.org/10.13039/100000001, National Science Foundation (NSF);
                Award ID: 1817518
                Award Recipient :
                Categories
                Article
                Custom metadata
                © The Author(s) 2020

                Uncategorized
                proteome informatics,archaeal biology
                Uncategorized
                proteome informatics, archaeal biology

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