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Huang Q, Szklarczyk D, Wang M, Simonovic M, von Mering C. PaxDb 5.0: Curated Protein Quantification Data Suggests Adaptive Proteome Changes in Yeasts. Mol Cell Proteomics 2023; 22:100640. [PMID: 37659604 PMCID: PMC10551891 DOI: 10.1016/j.mcpro.2023.100640] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2023] [Revised: 08/25/2023] [Accepted: 08/30/2023] [Indexed: 09/04/2023] Open
Abstract
The "Protein Abundances Across Organisms" database (PaxDb) is an integrative metaresource dedicated to protein abundance levels, in tissue-specific or whole-organism proteomes. PaxDb focuses on computing best-estimate abundances for proteins in normal/healthy contexts and expresses abundance values for each protein in "parts per million" in relation to all other protein molecules in the cell. The uniform data reprocessing, quality scoring, and integrated orthology relations have made PaxDb one of the preferred tools for comparisons between individual datasets, tissues, or organisms. In describing the latest version 5.0 of PaxDb, we particularly emphasize the data integration from various types of raw data and how we expanded the number of organisms and tissue groups as well as the proteome coverage. The current collection of PaxDb includes 831 original datasets from 170 species, including 22 Archaea, 81 Bacteria, and 67 Eukaryota. Apart from detailing the data update, we also present a comparative analysis of the human proteome subset of PaxDb against the two most widely used human proteome data resources: Human Protein Atlas and Genotype-Tissue Expression. Lastly, through our protein abundance data, we reveal an evolutionary trend in the usage of sulfur-containing amino acids in the proteomes of Fungi.
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Affiliation(s)
- Qingyao Huang
- Department of Molecular Life Sciences and Swiss Institute of Bioinformatics, University of Zurich, Zurich, Switzerland
| | - Damian Szklarczyk
- Department of Molecular Life Sciences and Swiss Institute of Bioinformatics, University of Zurich, Zurich, Switzerland
| | - Mingcong Wang
- Department of Molecular Life Sciences and Swiss Institute of Bioinformatics, University of Zurich, Zurich, Switzerland
| | - Milan Simonovic
- Department of Molecular Life Sciences and Swiss Institute of Bioinformatics, University of Zurich, Zurich, Switzerland
| | - Christian von Mering
- Department of Molecular Life Sciences and Swiss Institute of Bioinformatics, University of Zurich, Zurich, Switzerland.
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Nie M, Li H. Innovation in Cross-Linking Mass Spectrometry Workflows: Toward a Comprehensive, Flexible, and Customizable Data Analysis Platform. JOURNAL OF THE AMERICAN SOCIETY FOR MASS SPECTROMETRY 2023; 34:1949-1956. [PMID: 37537999 DOI: 10.1021/jasms.3c00123] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/05/2023]
Abstract
Cross-linking mass spectrometry (XL-MS) is widely used in the analysis of protein structure and protein-protein interactions (PPIs). Throughout the entire workflow, the utilization of cross-linkers and the interpretation of cross-linking data are the core steps. In recent years, the development of cross-linkers and analytical software mostly follow up on the classical models of non-cleavable cross-linkers such as BS3/DSS and MS-cleavable cross-linkers such as DSSO. Although such a paradigm promotes the maturity and robustness of the XL-MS field, it confines the innovation and flexibility of new cross-linkers and analytical software. This critical insight will discuss the classification, advantages, and disadvantages of existing data analysis search engines. Take the new platinum-based metal cross-linker as an example, potential pitfalls in characterization of cross-linked peptides using existing software are discussed. Finally, ideas on developing more flexible, comprehensive, and user-friendly cross-linkers and software tools are proposed.
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Affiliation(s)
- Minhan Nie
- School of Pharmaceutical Sciences, Sun Yat-sen University, Guangzhou 510006, China
| | - Huilin Li
- School of Pharmaceutical Sciences, Sun Yat-sen University, Guangzhou 510006, China
- Guangdong Key Laboratory of Chiral Molecule and Drug Discovery, School of Pharmaceutical Sciences, Sun Yat-sen University, Guangzhou 510006, China
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Khaje NA, Eletsky A, Biehn SE, Mobley CK, Rogals MJ, Kim Y, Mishra SK, Doerksen RJ, Lindert S, Prestegard JH, Sharp JS. Validated determination of NRG1 Ig-like domain structure by mass spectrometry coupled with computational modeling. Commun Biol 2022; 5:452. [PMID: 35551273 PMCID: PMC9098640 DOI: 10.1038/s42003-022-03411-y] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2022] [Accepted: 04/25/2022] [Indexed: 01/03/2023] Open
Abstract
High resolution hydroxyl radical protein footprinting (HR-HRPF) is a mass spectrometry-based method that measures the solvent exposure of multiple amino acids in a single experiment, offering constraints for experimentally informed computational modeling. HR-HRPF-based modeling has previously been used to accurately model the structure of proteins of known structure, but the technique has never been used to determine the structure of a protein of unknown structure. Here, we present the use of HR-HRPF-based modeling to determine the structure of the Ig-like domain of NRG1, a protein with no close homolog of known structure. Independent determination of the protein structure by both HR-HRPF-based modeling and heteronuclear NMR was carried out, with results compared only after both processes were complete. The HR-HRPF-based model was highly similar to the lowest energy NMR model, with a backbone RMSD of 1.6 Å. To our knowledge, this is the first use of HR-HRPF-based modeling to determine a previously uncharacterized protein structure. A mass spectrometry-based method guides computational modeling for de novo protein structure prediction.
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Affiliation(s)
- Niloofar Abolhasani Khaje
- Department of BioMolecular Sciences, University of Mississippi, University, MS, USA.,Analytical Operations Department, Gilead Sciences, Foster City, CA, USA
| | - Alexander Eletsky
- Complex Carbohydrate Research Center, University of Georgia, Athens, GA, USA
| | - Sarah E Biehn
- Department of Chemistry and Biochemistry, Ohio State University, Columbus, OH, USA
| | - Charles K Mobley
- Department of BioMolecular Sciences, University of Mississippi, University, MS, USA.,Protein Discovery Department, Impossible Foods, Redwood City, CA, USA
| | - Monique J Rogals
- Complex Carbohydrate Research Center, University of Georgia, Athens, GA, USA
| | - Yoonkyoo Kim
- Complex Carbohydrate Research Center, University of Georgia, Athens, GA, USA
| | - Sushil K Mishra
- Department of BioMolecular Sciences, University of Mississippi, University, MS, USA.,Glycoscience Center of Research Excellence, University of Mississippi, University, MS, USA
| | - Robert J Doerksen
- Department of BioMolecular Sciences, University of Mississippi, University, MS, USA.,Glycoscience Center of Research Excellence, University of Mississippi, University, MS, USA
| | - Steffen Lindert
- Department of Chemistry and Biochemistry, Ohio State University, Columbus, OH, USA
| | - James H Prestegard
- Complex Carbohydrate Research Center, University of Georgia, Athens, GA, USA
| | - Joshua S Sharp
- Department of BioMolecular Sciences, University of Mississippi, University, MS, USA. .,Glycoscience Center of Research Excellence, University of Mississippi, University, MS, USA. .,Department of Chemistry and Biochemistry, University of Mississippi, University, MS, USA.
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