1
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Rojas Echeverri JC, Hause F, Iacobucci C, Ihling CH, Tänzler D, Shulman N, Riffle M, MacLean BX, Sinz A. A Workflow for Improved Analysis of Cross-Linking Mass Spectrometry Data Integrating Parallel Accumulation-Serial Fragmentation with MeroX and Skyline. Anal Chem 2024; 96:7373-7379. [PMID: 38696819 PMCID: PMC11099889 DOI: 10.1021/acs.analchem.4c00829] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2024] [Revised: 04/24/2024] [Accepted: 04/29/2024] [Indexed: 05/04/2024]
Abstract
Cross-linking mass spectrometry (XL-MS) has evolved into a pivotal technique for probing protein interactions. This study describes the implementation of Parallel Accumulation-Serial Fragmentation (PASEF) on timsTOF instruments, enhancing the detection and analysis of protein interactions by XL-MS. Addressing the challenges in XL-MS, such as the interpretation of complex spectra, low abundant cross-linked peptides, and a data acquisition bias, our current study integrates a peptide-centric approach for the analysis of XL-MS data and presents the foundation for integrating data-independent acquisition (DIA) in XL-MS with a vendor-neutral and open-source platform. A novel workflow is described for processing data-dependent acquisition (DDA) of PASEF-derived information. For this, software by Bruker Daltonics is used, enabling the conversion of these data into a format that is compatible with MeroX and Skyline software tools. Our approach significantly improves the identification of cross-linked products from complex mixtures, allowing the XL-MS community to overcome current analytical limitations.
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Affiliation(s)
- Juan Camilo Rojas Echeverri
- Department
of Pharmaceutical Chemistry and Bioanalytics, Martin-Luther-University Halle-Wittenberg, 06120 Halle, Germany
- Center
for Structural Mass Spectrometry, Martin-Luther-University
Halle-Wittenberg, 06120 Halle, Germany
| | - Frank Hause
- Department
of Pharmaceutical Chemistry and Bioanalytics, Martin-Luther-University Halle-Wittenberg, 06120 Halle, Germany
- Center
for Structural Mass Spectrometry, Martin-Luther-University
Halle-Wittenberg, 06120 Halle, Germany
- Institute
for Molecular Medicine, Martin-Luther-University
Halle-Wittenberg, 06120 Halle, Germany
| | - Claudio Iacobucci
- Department
of Pharmaceutical Chemistry and Bioanalytics, Martin-Luther-University Halle-Wittenberg, 06120 Halle, Germany
- Center
for Structural Mass Spectrometry, Martin-Luther-University
Halle-Wittenberg, 06120 Halle, Germany
- Department
of Physical and Chemical Sciences, University
of L’Aquila, 67100 L’Aquila, Italy
| | - Christian H. Ihling
- Department
of Pharmaceutical Chemistry and Bioanalytics, Martin-Luther-University Halle-Wittenberg, 06120 Halle, Germany
- Center
for Structural Mass Spectrometry, Martin-Luther-University
Halle-Wittenberg, 06120 Halle, Germany
| | - Dirk Tänzler
- Department
of Pharmaceutical Chemistry and Bioanalytics, Martin-Luther-University Halle-Wittenberg, 06120 Halle, Germany
- Center
for Structural Mass Spectrometry, Martin-Luther-University
Halle-Wittenberg, 06120 Halle, Germany
| | - Nicholas Shulman
- Department
of Genome Sciences, University of Washington, Seattle, Washington 98195, United States
| | - Michael Riffle
- Department
of Biochemistry, University of Washington, Seattle, Washington 98195, United States
| | - Brendan X. MacLean
- Department
of Genome Sciences, University of Washington, Seattle, Washington 98195, United States
| | - Andrea Sinz
- Department
of Pharmaceutical Chemistry and Bioanalytics, Martin-Luther-University Halle-Wittenberg, 06120 Halle, Germany
- Center
for Structural Mass Spectrometry, Martin-Luther-University
Halle-Wittenberg, 06120 Halle, Germany
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2
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Gent L, Chiappetta ME, Hesketh S, Palmowski P, Porter A, Bonicelli A, Schwalbe EC, Procopio N. Bone Proteomics Method Optimization for Forensic Investigations. J Proteome Res 2024; 23:1844-1858. [PMID: 38621258 PMCID: PMC11077585 DOI: 10.1021/acs.jproteome.4c00151] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2024] [Revised: 03/30/2024] [Accepted: 04/03/2024] [Indexed: 04/17/2024]
Abstract
The application of proteomic analysis to forensic skeletal remains has gained significant interest in improving biological and chronological estimations in medico-legal investigations. To enhance the applicability of these analyses to forensic casework, it is crucial to maximize throughput and proteome recovery while minimizing interoperator variability and laboratory-induced post-translational protein modifications (PTMs). This work compared different workflows for extracting, purifying, and analyzing bone proteins using liquid chromatography with tandem mass spectrometry (LC-MS)/MS including an in-StageTip protocol previously optimized for forensic applications and two protocols using novel suspension-trap technology (S-Trap) and different lysis solutions. This study also compared data-dependent acquisition (DDA) with data-independent acquisition (DIA). By testing all of the workflows on 30 human cortical tibiae samples, S-Trap workflows resulted in increased proteome recovery with both lysis solutions tested and in decreased levels of induced deamidations, and the DIA mode resulted in greater sensitivity and window of identification for the identification of lower-abundance proteins, especially when open-source software was utilized for data processing in both modes. The newly developed S-Trap protocol is, therefore, suitable for forensic bone proteomic workflows and, particularly when paired with DIA mode, can offer improved proteomic outcomes and increased reproducibility, showcasing its potential in forensic proteomics and contributing to achieving standardization in bone proteomic analyses for forensic applications.
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Affiliation(s)
- Luke Gent
- School
of Law and Policing, Research Centre for Field Archaeology and Forensic
Taphonomy, University of Central Lancashire, Preston PR1 2HE, United Kingdom
| | - Maria Elena Chiappetta
- School
of Law and Policing, Research Centre for Field Archaeology and Forensic
Taphonomy, University of Central Lancashire, Preston PR1 2HE, United Kingdom
- Department
of Biology, Ecology and Earth Sciences (DiBEST), University of Calabria, Arcavacata
di Rende 87036, Italy
| | - Stuart Hesketh
- School
of Medicine, University of Central Lancashire, Preston PR1 2HE, United Kingdom
| | - Pawel Palmowski
- NUPPA
Facility, Medical School, Newcastle University, Newcastle Upon Tyne NE1
7RU, United Kingdom
| | - Andrew Porter
- NUPPA
Facility, Medical School, Newcastle University, Newcastle Upon Tyne NE1
7RU, United Kingdom
| | - Andrea Bonicelli
- School
of Law and Policing, Research Centre for Field Archaeology and Forensic
Taphonomy, University of Central Lancashire, Preston PR1 2HE, United Kingdom
| | - Edward C. Schwalbe
- Department
of Applied Sciences, Northumbria University, Newcastle Upon Tyne NE1
8ST, United Kingdom
| | - Noemi Procopio
- School
of Law and Policing, Research Centre for Field Archaeology and Forensic
Taphonomy, University of Central Lancashire, Preston PR1 2HE, United Kingdom
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3
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Hevler JF, Heck AJR. Higher-Order Structural Organization of the Mitochondrial Proteome Charted by In Situ Cross-Linking Mass Spectrometry. Mol Cell Proteomics 2023; 22:100657. [PMID: 37805037 PMCID: PMC10651688 DOI: 10.1016/j.mcpro.2023.100657] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2023] [Revised: 09/14/2023] [Accepted: 10/04/2023] [Indexed: 10/09/2023] Open
Abstract
Mitochondria are densely packed with proteins, of which most are involved physically or more transiently in protein-protein interactions (PPIs). Mitochondria host among others all enzymes of the Krebs cycle and the oxidative phosphorylation pathway and are foremost associated with cellular bioenergetics. However, mitochondria are also important contributors to apoptotic cell death and contain their own genome indicating that they play additionally an eminent role in processes beyond bioenergetics. Despite intense efforts in identifying and characterizing mitochondrial protein complexes by structural biology and proteomics techniques, many PPIs have remained elusive. Several of these (membrane embedded) PPIs are less stable in vitro hampering their characterization by most contemporary methods in structural biology. Particularly in these cases, cross-linking mass spectrometry (XL-MS) has proven valuable for the in-depth characterization of mitochondrial protein complexes in situ. Here, we highlight experimental strategies for the analysis of proteome-wide PPIs in mitochondria using XL-MS. We showcase the ability of in situ XL-MS as a tool to map suborganelle interactions and topologies and aid in refining structural models of protein complexes. We describe some of the most recent technological advances in XL-MS that may benefit the in situ characterization of PPIs even further, especially when combined with electron microscopy and structural modeling.
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Affiliation(s)
- Johannes F Hevler
- Division of Biomolecular Mass Spectrometry and Proteomics, Bijvoet Center for Biomolecular Research and Utrecht Institute for Pharmaceutical Sciences, University of Utrecht, Utrecht, The Netherlands; Netherlands Proteomics Center, Utrecht, The Netherlands
| | - Albert J R Heck
- Division of Biomolecular Mass Spectrometry and Proteomics, Bijvoet Center for Biomolecular Research and Utrecht Institute for Pharmaceutical Sciences, University of Utrecht, Utrecht, The Netherlands; Netherlands Proteomics Center, Utrecht, The Netherlands.
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4
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Kitata RB, Yang JC, Chen YJ. Advances in data-independent acquisition mass spectrometry towards comprehensive digital proteome landscape. MASS SPECTROMETRY REVIEWS 2023; 42:2324-2348. [PMID: 35645145 DOI: 10.1002/mas.21781] [Citation(s) in RCA: 36] [Impact Index Per Article: 36.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/09/2021] [Revised: 12/17/2021] [Accepted: 01/21/2022] [Indexed: 06/15/2023]
Abstract
The data-independent acquisition mass spectrometry (DIA-MS) has rapidly evolved as a powerful alternative for highly reproducible proteome profiling with a unique strength of generating permanent digital maps for retrospective analysis of biological systems. Recent advancements in data analysis software tools for the complex DIA-MS/MS spectra coupled to fast MS scanning speed and high mass accuracy have greatly expanded the sensitivity and coverage of DIA-based proteomics profiling. Here, we review the evolution of the DIA-MS techniques, from earlier proof-of-principle of parallel fragmentation of all-ions or ions in selected m/z range, the sequential window acquisition of all theoretical mass spectra (SWATH-MS) to latest innovations, recent development in computation algorithms for data informatics, and auxiliary tools and advanced instrumentation to enhance the performance of DIA-MS. We further summarize recent applications of DIA-MS and experimentally-derived as well as in silico spectra library resources for large-scale profiling to facilitate biomarker discovery and drug development in human diseases with emphasis on the proteomic profiling coverage. Toward next-generation DIA-MS for clinical proteomics, we outline the challenges in processing multi-dimensional DIA data set and large-scale clinical proteomics, and continuing need in higher profiling coverage and sensitivity.
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Affiliation(s)
| | - Jhih-Ci Yang
- Institute of Chemistry, Academia Sinica, Taipei, Taiwan
- Sustainable Chemical Science and Technology, Taiwan International Graduate Program, Academia Sinica and National Yang Ming Chiao Tung University, Taipei, Taiwan
- Department of Applied Chemistry, National Yang Ming Chiao Tung University, Hsinchu, Taiwan
| | - Yu-Ju Chen
- Institute of Chemistry, Academia Sinica, Taipei, Taiwan
- Sustainable Chemical Science and Technology, Taiwan International Graduate Program, Academia Sinica and National Yang Ming Chiao Tung University, Taipei, Taiwan
- Department of Chemistry, National Taiwan University, Taipei, Taiwan
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5
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Hao Y, Chen M, Huang X, Xu H, Wu P, Chen S. 4D-diaXLMS: Proteome-wide Four-Dimensional Data-Independent Acquisition Workflow for Cross-Linking Mass Spectrometry. Anal Chem 2023; 95:14077-14085. [PMID: 37691250 DOI: 10.1021/acs.analchem.3c02824] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/12/2023]
Abstract
Cross-linking mass spectrometry (XL-MS) is a powerful tool for examining protein structures and interactions. Nevertheless, analysis of low-abundance cross-linked peptides is often limited in the data-dependent acquisition (DDA) mode due to its semistochastic nature. To address this issue, we introduced a workflow called 4D-diaXLMS, representing the first-ever application of four-dimensional data-independent acquisition for proteome-wide cross-linking analysis. Cross-linking studies of the HeLa cell proteome were evaluated using the classical cross-linker disuccinimidyl suberate as an example. Compared with the DDA analysis, 4D-diaXLMS exhibited marked improvement in the identification coverage of cross-linked peptides, with a total increase of 36% in single-shot analysis across all 16 SCX fractions. This advantage was further amplified when reducing the fraction number to 8 and 4, resulting in 125 and 149% improvements, respectively. Using 4D-diaXLMS, up to 83% of the cross-linked peptides were repeatedly identified in three replicates, more than twice the 38% in the DDA mode. Furthermore, 4D-diaXLMS showed good performance in the quantitative analysis of yeast cross-linked peptides even in a 15-fold excess amount of HeLa cell matrix, with a low coefficient of variation and high quantitative accuracies in all concentrations. Overall, 4D-diaXLMS was proven to have high coverage, good reproducibility, and accurate quantification for in-depth XL-MS analysis in complex samples, demonstrating its immense potential for advances in the field.
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Affiliation(s)
- Yanhong Hao
- The Institute for Advanced Studies, Wuhan University, Wuhan, Hubei 430072, China
| | - Moran Chen
- The Institute for Advanced Studies, Wuhan University, Wuhan, Hubei 430072, China
| | - Xiao Huang
- The Institute for Advanced Studies, Wuhan University, Wuhan, Hubei 430072, China
| | - Hui Xu
- The Institute for Advanced Studies, Wuhan University, Wuhan, Hubei 430072, China
| | - Pengfei Wu
- The Institute for Advanced Studies, Wuhan University, Wuhan, Hubei 430072, China
| | - Suming Chen
- The Institute for Advanced Studies, Wuhan University, Wuhan, Hubei 430072, China
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6
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Chen ZA, Rappsilber J. Protein structure dynamics by crosslinking mass spectrometry. Curr Opin Struct Biol 2023; 80:102599. [PMID: 37104977 DOI: 10.1016/j.sbi.2023.102599] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2023] [Revised: 03/22/2023] [Accepted: 03/27/2023] [Indexed: 04/29/2023]
Abstract
Crosslinking mass spectrometry captures protein structures in solution. The crosslinks reveal spatial proximities as distance restraints, but do not easily reveal which of these restraints derive from the same protein conformation. This superposition can be reduced by photo-crosslinking, and adding information from protein structure models, or quantitative crosslinking reveals conformation-specific crosslinks. As a consequence, crosslinking MS has proven useful already in the context of multiple dynamic protein systems. We foresee a breakthrough in the resolution and scale of studying protein dynamics when crosslinks are used to guide deep-learning-based protein modelling. Advances in crosslinking MS, such as photoactivatable crosslinking and in-situ crosslinking, will then reveal protein conformation dynamics in the cellular context, at a pseudo-atomic resolution, and plausibly in a time-resolved manner.
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Affiliation(s)
- Zhuo Angel Chen
- Technische Universität Berlin, Chair of Bioanalytics, 10623 Berlin, Germany
| | - Juri Rappsilber
- Technische Universität Berlin, Chair of Bioanalytics, 10623 Berlin, Germany; Si-M/"Der Simulierte Mensch", a Science Framework of Technische Universität Berlin and Charité - Universitätsmedizin Berlin, 10623 Berlin, Germany; Wellcome Centre for Cell Biology, University of Edinburgh, Edinburgh EH9 3BF, UK.
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7
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Dammer EB, Seyfried NT, Johnson ECB. Batch Correction and Harmonization of -Omics Datasets with a Tunable Median Polish of Ratio. FRONTIERS IN SYSTEMS BIOLOGY 2023; 3:1092341. [PMID: 37122388 PMCID: PMC10137904 DOI: 10.3389/fsysb.2023.1092341] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/02/2023]
Abstract
Large scale -omics datasets can provide new insights into normal and disease-related biology when analyzed through a systems biology framework. However, technical artefacts present in most -omics datasets due to variations in sample preparation, batching, platform settings, personnel, and other experimental procedures prevent useful analyses of such data without prior adjustment for these technical factors. Here, we demonstrate a tunable median polish of ratio (TAMPOR) approach for batch effect correction and agglomeration of multiple, multi-batch, site-specific cohorts into a single analyte abundance data matrix that is suitable for systems biology analyses. We illustrate the utility and versatility of TAMPOR through four distinct use cases where the method has been applied to different proteomic datasets, some of which contain a specific defect that must be addressed prior to analysis. We compare quality control metrics and sources of variance before and after application of TAMPOR to show that TAMPOR is effective at removing batch effects and other unwanted sources of variance in -omics data. We also show how TAMPOR can be used to harmonize -omics datasets even when the data are acquired using different analytical approaches. TAMPOR is a powerful and flexible approach for cleaning and harmonization of -omics data prior to downstream systems biology analysis.
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Affiliation(s)
- Eric B Dammer
- Goizueta Alzheimer's Disease Research Center, Emory University School of Medicine, Atlanta, GA, USA
- Department of Biochemistry, Emory University School of Medicine, Atlanta, GA, USA
| | - Nicholas T Seyfried
- Goizueta Alzheimer's Disease Research Center, Emory University School of Medicine, Atlanta, GA, USA
- Department of Biochemistry, Emory University School of Medicine, Atlanta, GA, USA
- Department of Neurology, Emory University School of Medicine, Atlanta, GA, USA
| | - Erik C B Johnson
- Goizueta Alzheimer's Disease Research Center, Emory University School of Medicine, Atlanta, GA, USA
- Department of Neurology, Emory University School of Medicine, Atlanta, GA, USA
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8
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Santorelli L, Caterino M, Costanzo M. Dynamic Interactomics by Cross-Linking Mass Spectrometry: Mapping the Daily Cell Life in Postgenomic Era. OMICS : A JOURNAL OF INTEGRATIVE BIOLOGY 2022; 26:633-649. [PMID: 36445175 DOI: 10.1089/omi.2022.0137] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/03/2022]
Abstract
The majority of processes that occur in daily cell life are modulated by hundreds to thousands of dynamic protein-protein interactions (PPI). The resulting protein complexes constitute a tangled network that, with its continuous remodeling, builds up highly organized functional units. Thus, defining the dynamic interactome of one or more proteins allows determining the full range of biological activities these proteins are capable of. This conceptual approach is poised to gain further traction and significance in the current postgenomic era wherein the treatment of severe diseases needs to be tackled at both genomic and PPI levels. This also holds true for COVID-19, a multisystemic disease affecting biological networks across the biological hierarchy from genome to proteome to metabolome. In this overarching context and the current historical moment of the COVID-19 pandemic where systems biology increasingly comes to the fore, cross-linking mass spectrometry (XL-MS) has become highly relevant, emerging as a powerful tool for PPI discovery and characterization. This expert review highlights the advanced XL-MS approaches that provide in vivo insights into the three-dimensional protein complexes, overcoming the static nature of common interactomics data and embracing the dynamics of the cell proteome landscape. Many XL-MS applications based on the use of diverse cross-linkers, MS detection methods, and predictive bioinformatic tools for single proteins or proteome-wide interactions were shown. We conclude with a future outlook on XL-MS applications in the field of structural proteomics and ways to sustain the remarkable flexibility of XL-MS for dynamic interactomics and structural studies in systems biology and planetary health.
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Affiliation(s)
- Lucia Santorelli
- Department of Oncology and Hematology-Oncology, University of Milano, Milan, Italy.,IFOM ETS, The AIRC Institute of Molecular Oncology, Milan, Italy
| | - Marianna Caterino
- Department of Molecular Medicine and Medical Biotechnology, University of Naples Federico II, Naples, Italy.,CEINGE-Biotecnologie Avanzate s.c.ar.l., Naples, Italy
| | - Michele Costanzo
- Department of Molecular Medicine and Medical Biotechnology, University of Naples Federico II, Naples, Italy.,CEINGE-Biotecnologie Avanzate s.c.ar.l., Naples, Italy
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9
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Wei W, Liu S, Han Y, Lu S, Yan G, Sun H, Wang X. Rapid identification of chemical components in Zhizi Baipi decoction by ultra performance liquid chromatography quadrupole time-of-flight mass spectrometry coupled with a novel informatics UNIFI platform. J Sep Sci 2022; 45:3679-3690. [PMID: 35943222 DOI: 10.1002/jssc.202200306] [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: 05/09/2022] [Revised: 07/14/2022] [Accepted: 07/25/2022] [Indexed: 11/10/2022]
Abstract
Zhizi Baipi Decoction is a classic traditional Chinese medicine formula for the treatment of jaundice and various liver diseases. The chemical components of Zhizi baipi decoction were not clear resulting from the paucity of relevant studies, which hindered the elucidation of the pharmacological mechanism, the comprehensive development and utilization of Zhizi baipi decoction in clinical. In this study, ultra performance liquid chromatography-quadrupole-time of flight mass spectrometry combined with UNIFI natural product information analysis platform was used to rapidly analyze and identify the chemical components in Zhizi baipi decoction. A total of 122 chemical components, including 53 flavonoids, 16 alkaloids, 25 terpenoids, 5 phenylpropanoids, 14 organic acids, and 7 others, were identified from Zhizi baipi decoction. These compounds may be the active components of Zhizi baipi decoction. The method established in this study can systematically, rapidly, and accurately resolve the chemical components in Zhizi baipi decoction, which lays the foundation for further establishment of the pharmacodynamic substance basis and quality control of Zhizi baipi decoction. This article is protected by copyright. All rights reserved.
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Affiliation(s)
- Wenfeng Wei
- National Chinmedomics Research Center, National TCM Key Laboratory of Serum Pharmacochemistry, Metabolomics Laboratory, Department of Pharmaceutical Analysis, Heilongjiang University of Chinese Medicine, Heping Road 24, Harbin, 150040, China
| | - Shaobo Liu
- National Chinmedomics Research Center, National TCM Key Laboratory of Serum Pharmacochemistry, Metabolomics Laboratory, Department of Pharmaceutical Analysis, Heilongjiang University of Chinese Medicine, Heping Road 24, Harbin, 150040, China
| | - Ying Han
- National Chinmedomics Research Center, National TCM Key Laboratory of Serum Pharmacochemistry, Metabolomics Laboratory, Department of Pharmaceutical Analysis, Heilongjiang University of Chinese Medicine, Heping Road 24, Harbin, 150040, China
| | - Shengwen Lu
- National Chinmedomics Research Center, National TCM Key Laboratory of Serum Pharmacochemistry, Metabolomics Laboratory, Department of Pharmaceutical Analysis, Heilongjiang University of Chinese Medicine, Heping Road 24, Harbin, 150040, China
| | - Guangli Yan
- National Chinmedomics Research Center, National TCM Key Laboratory of Serum Pharmacochemistry, Metabolomics Laboratory, Department of Pharmaceutical Analysis, Heilongjiang University of Chinese Medicine, Heping Road 24, Harbin, 150040, China
| | - Hui Sun
- National Chinmedomics Research Center, National TCM Key Laboratory of Serum Pharmacochemistry, Metabolomics Laboratory, Department of Pharmaceutical Analysis, Heilongjiang University of Chinese Medicine, Heping Road 24, Harbin, 150040, China
| | - Xijun Wang
- National Chinmedomics Research Center, National TCM Key Laboratory of Serum Pharmacochemistry, Metabolomics Laboratory, Department of Pharmaceutical Analysis, Heilongjiang University of Chinese Medicine, Heping Road 24, Harbin, 150040, China.,State Key Laboratory of Quality Research in Chinese Medicine, Macau University of Science and Technology, Avenida Wai Long, Taipa, Macau
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10
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Britt HM, Cragnolini T, Khatun S, Hatimy A, James J, Page N, Williams JP, Hughes C, Denny R, Thalassinos K, Vissers JPC. Evaluation of acquisition modes for semi-quantitative analysis by targeted and untargeted mass spectrometry. RAPID COMMUNICATIONS IN MASS SPECTROMETRY : RCM 2022; 36:e9308. [PMID: 35353398 PMCID: PMC9287043 DOI: 10.1002/rcm.9308] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/01/2022] [Revised: 03/26/2022] [Accepted: 03/27/2022] [Indexed: 06/14/2023]
Abstract
RATIONALE Analyte quantitation by mass spectrometry underpins a diverse range of scientific endeavors. The fast-growing field of mass spectrometer development has resulted in several targeted and untargeted acquisition modes suitable for these applications. By characterizing the acquisition methods available on an ion mobility (IM)-enabled orthogonal acceleration time-of-flight (oa-ToF) instrument, the optimum modes for analyte semi-quantitation can be deduced. METHODS Serial dilutions of commercial metabolite, peptide, or cross-linked peptide analytes were prepared in matrices of human urine or Escherichia coli digest. Each analyte dilution was introduced into an IM separation-enabled oa-ToF mass spectrometer by reversed-phase liquid chromatography and electrospray ionization. Data were acquired for each sample in duplicate using nine different acquisition modes, including four IM-enabled acquisitions modes, available on the mass spectrometer. RESULTS Five (metabolite) or seven (peptide/cross-linked peptide) point calibration curves were prepared for analytes across each of the acquisition modes. A nonlinear response was observed at high concentrations for some modes, attributed to saturation effects. Two correction methods, one MS1 isotope-correction and one MS2 ion intensity-correction, were applied to address this observation, resulting in an up to twofold increase in dynamic range. By averaging the semi-quantitative results across analyte classes, two parameters, linear dynamic range (LDR) and lower limit of quantification (LLOQ), were determined to evaluate each mode. CONCLUSION A comparison of the acquisition modes revealed that data-independent acquisition and parallel reaction monitoring methods are most robust for semi-quantitation when considering achievable LDR and LLOQ. IM-enabled modes exhibited sensitivity increases, but a simultaneous reduction in dynamic range required correction methods to recover. These findings will assist users in identifying the optimum acquisition mode for their analyte quantitation needs, supporting a diverse range of applications and providing guidance for future acquisition mode developments.
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Affiliation(s)
- Hannah M. Britt
- Institute of Structural and Molecular Biology, Division of BiosciencesUniversity College LondonLondonUK
| | - Tristan Cragnolini
- Institute of Structural and Molecular Biology, Division of BiosciencesUniversity College LondonLondonUK
- Institute of Structural and Molecular Biology, Birkbeck CollegeUniversity of LondonLondonUK
| | - Suniya Khatun
- Institute of Structural and Molecular Biology, Division of BiosciencesUniversity College LondonLondonUK
| | - Abubakar Hatimy
- Institute of Structural and Molecular Biology, Division of BiosciencesUniversity College LondonLondonUK
| | - Juliette James
- Institute of Structural and Molecular Biology, Division of BiosciencesUniversity College LondonLondonUK
| | - Nathanael Page
- Institute of Structural and Molecular Biology, Division of BiosciencesUniversity College LondonLondonUK
- LGC GroupTeddingtonUK
| | | | | | | | - Konstantinos Thalassinos
- Institute of Structural and Molecular Biology, Division of BiosciencesUniversity College LondonLondonUK
- Institute of Structural and Molecular Biology, Birkbeck CollegeUniversity of LondonLondonUK
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11
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Wippel HH, Chavez JD, Tang X, Bruce JE. Quantitative interactome analysis with chemical cross-linking and mass spectrometry. Curr Opin Chem Biol 2022; 66:102076. [PMID: 34393043 PMCID: PMC8837725 DOI: 10.1016/j.cbpa.2021.06.011] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2021] [Revised: 06/17/2021] [Accepted: 06/23/2021] [Indexed: 01/03/2023]
Abstract
Structural plasticity and dynamic protein-protein interactions are critical determinants of protein function within living systems. Quantitative chemical cross-linking with mass spectrometry (qXL-MS) is an emerging technology able to provide information on changes in protein conformations and interactions. Importantly, qXL-MS is applicable to complex biological systems, including living cells and tissues, thereby providing insights into proteins within their native environments. Here, we present an overview of recent technological developments and applications involving qXL-MS, including design and synthesis of isotope-labeled cross-linkers, development of new liquid chromatography-MS methodologies, and computational developments enabling interpretation of the data.
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Affiliation(s)
- Helisa H Wippel
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
| | - Juan D Chavez
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
| | - Xiaoting Tang
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
| | - James E Bruce
- Department of Genome Sciences, University of Washington, Seattle, WA, USA.
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12
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Jia SY, Zhang YL, Sun XY, Yuan C, Zheng SG. Impact of the Glycemic Level on the Salivary Proteome of Middle-Aged and Elderly People With Type 2 Diabetes Mellitus: An Observational Study. Front Mol Biosci 2021; 8:790091. [PMID: 34957219 PMCID: PMC8703016 DOI: 10.3389/fmolb.2021.790091] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2021] [Accepted: 11/22/2021] [Indexed: 11/16/2022] Open
Abstract
Type 2 diabetes mellitus (T2DM) is an increasing global public health concern, but its impact on the salivary proteome is still unclear. To evaluate the effect of glycemic levels in middle-aged and elderly individuals with T2DM on salivary proteomics, we compared the differences by liquid chromatography tandem mass spectrometry (LC–MS/MS). Unstimulated whole saliva samples from 8 T2DM patients with good glycemic control (G group, HbA1c <6.5%) and 16 patients with poor control (P group, HbA1c ≥6.5%) were analyzed by LC–MS/MS in the data-independent acquisition mode (Clinical register number: ChiCTR1900023582.). After functional annotation, cluster analysis and receiver operating characteristic (ROC) curve analysis were carried out to screen and evaluate candidate proteins. A total of 5,721 proteins were quantified, while 40 proteins differed significantly. In the P group, proteins involved in oxidative stress-related processes were upregulated, whereas proteins related to salivary secretion were downregulated. The combination of thioredoxin domain-containing protein 17, zymogen granule protein 16B, and FAM3 metabolism regulating signaling molecule D yielded an area under the curve of 0.917 which showed a robust ability to distinguish the P and G groups. In conclusion, poorly controlled hyperglycemia may affect salivary proteins through various pathways, including oxidative stress and glandular secretion. Furthermore, the differentially expressed proteins, especially the three proteins with the best differentiation, might serve as an anchor point for the further study of hyperglycemia and oral diseases.
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Affiliation(s)
- Shu Yuan Jia
- Department of Preventive Dentistry, Peking University School and Hospital of Stomatology, National Center of Stomatology, National Clinical Research Center for Oral Diseases, National Engineering Laboratory for Digital and Material Technology of Stomatology, Beijing, China
| | - Yan Ling Zhang
- Department of Periodontology, Peking University School and Hospital of Stomatology, National Center of Stomatology, National Clinical Research Center for Oral Diseases, National Engineering Laboratory for Digital and Material Technology of Stomatology, Beijing, China
| | - Xiang Yu Sun
- Department of Preventive Dentistry, Peking University School and Hospital of Stomatology, National Center of Stomatology, National Clinical Research Center for Oral Diseases, National Engineering Laboratory for Digital and Material Technology of Stomatology, Beijing, China
| | - Chao Yuan
- Department of Preventive Dentistry, Peking University School and Hospital of Stomatology, National Center of Stomatology, National Clinical Research Center for Oral Diseases, National Engineering Laboratory for Digital and Material Technology of Stomatology, Beijing, China
| | - Shu Guo Zheng
- Department of Preventive Dentistry, Peking University School and Hospital of Stomatology, National Center of Stomatology, National Clinical Research Center for Oral Diseases, National Engineering Laboratory for Digital and Material Technology of Stomatology, Beijing, China
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13
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Piersimoni L, Kastritis PL, Arlt C, Sinz A. Cross-Linking Mass Spectrometry for Investigating Protein Conformations and Protein-Protein Interactions─A Method for All Seasons. Chem Rev 2021; 122:7500-7531. [PMID: 34797068 DOI: 10.1021/acs.chemrev.1c00786] [Citation(s) in RCA: 81] [Impact Index Per Article: 27.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Mass spectrometry (MS) has become one of the key technologies of structural biology. In this review, the contributions of chemical cross-linking combined with mass spectrometry (XL-MS) for studying three-dimensional structures of proteins and for investigating protein-protein interactions are outlined. We summarize the most important cross-linking reagents, software tools, and XL-MS workflows and highlight prominent examples for characterizing proteins, their assemblies, and interaction networks in vitro and in vivo. Computational modeling plays a crucial role in deriving 3D-structural information from XL-MS data. Integrating XL-MS with other techniques of structural biology, such as cryo-electron microscopy, has been successful in addressing biological questions that to date could not be answered. XL-MS is therefore expected to play an increasingly important role in structural biology in the future.
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Affiliation(s)
- Lolita Piersimoni
- Department of Pharmaceutical Chemistry & Bioanalytics, Institute of Pharmacy, Kurt-Mothes-Strasse 3, D-06120 Halle (Saale), Germany.,Center for Structural Mass Spectrometry, Kurt-Mothes-Strasse 3, D-06120 Halle (Saale), Germany
| | - Panagiotis L Kastritis
- Interdisciplinary Research Center HALOmem, Charles Tanford Protein Center, Kurt-Mothes-Strasse 3a, D-06120 Halle (Saale), Germany.,Institute of Biochemistry and Biotechnology, Martin Luther University Halle-Wittenberg, Kurt-Mothes-Strasse 3, D-06120 Halle (Saale), Germany.,Biozentrum, Weinbergweg 22, D-06120 Halle (Saale), Germany
| | - Christian Arlt
- Department of Pharmaceutical Chemistry & Bioanalytics, Institute of Pharmacy, Kurt-Mothes-Strasse 3, D-06120 Halle (Saale), Germany.,Center for Structural Mass Spectrometry, Kurt-Mothes-Strasse 3, D-06120 Halle (Saale), Germany
| | - Andrea Sinz
- Department of Pharmaceutical Chemistry & Bioanalytics, Institute of Pharmacy, Kurt-Mothes-Strasse 3, D-06120 Halle (Saale), Germany.,Center for Structural Mass Spectrometry, Kurt-Mothes-Strasse 3, D-06120 Halle (Saale), Germany
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14
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Yugandhar K, Zhao Q, Gupta S, Xiong D, Yu H. Progress in methodologies and quality-control strategies in protein cross-linking mass spectrometry. Proteomics 2021; 21:e2100145. [PMID: 34647422 DOI: 10.1002/pmic.202100145] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2021] [Accepted: 10/04/2021] [Indexed: 11/10/2022]
Abstract
Deciphering the interaction networks and structural dynamics of proteins is pivotal to better understanding their biological functions. Cross-linking mass spectrometry (XL-MS) is a powerful and increasingly popular technology that provides information about protein-protein interactions and their structural constraints for individual proteins and multiprotein complexes on a proteome-scale. In this review, we first assess the coverage and depth of the XL-MS technique by utilizing publicly available datasets. We then delve into the progress in XL-MS experimental and computational methodologies and examine different quality-control strategies reported in the literature. Finally, we discuss the progress in XL-MS applications along with the scope for future improvements.
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Affiliation(s)
- Kumar Yugandhar
- Department of Computational Biology, Cornell University, New York, USA.,Weill Institute for Cell and Molecular Biology, Cornell University, New York, USA
| | - Qiuye Zhao
- Department of Computational Biology, Cornell University, New York, USA.,Weill Institute for Cell and Molecular Biology, Cornell University, New York, USA
| | - Shobhita Gupta
- Department of Computational Biology, Cornell University, New York, USA.,Weill Institute for Cell and Molecular Biology, Cornell University, New York, USA
| | - Dapeng Xiong
- Department of Computational Biology, Cornell University, New York, USA.,Weill Institute for Cell and Molecular Biology, Cornell University, New York, USA
| | - Haiyuan Yu
- Department of Computational Biology, Cornell University, New York, USA.,Weill Institute for Cell and Molecular Biology, Cornell University, New York, USA
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15
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Britt HM, Cragnolini T, Thalassinos K. Integration of Mass Spectrometry Data for Structural Biology. Chem Rev 2021; 122:7952-7986. [PMID: 34506113 DOI: 10.1021/acs.chemrev.1c00356] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
Abstract
Mass spectrometry (MS) is increasingly being used to probe the structure and dynamics of proteins and the complexes they form with other macromolecules. There are now several specialized MS methods, each with unique sample preparation, data acquisition, and data processing protocols. Collectively, these methods are referred to as structural MS and include cross-linking, hydrogen-deuterium exchange, hydroxyl radical footprinting, native, ion mobility, and top-down MS. Each of these provides a unique type of structural information, ranging from composition and stoichiometry through to residue level proximity and solvent accessibility. Structural MS has proved particularly beneficial in studying protein classes for which analysis by classic structural biology techniques proves challenging such as glycosylated or intrinsically disordered proteins. To capture the structural details for a particular system, especially larger multiprotein complexes, more than one structural MS method with other structural and biophysical techniques is often required. Key to integrating these diverse data are computational strategies and software solutions to facilitate this process. We provide a background to the structural MS methods and briefly summarize other structural methods and how these are combined with MS. We then describe current state of the art approaches for the integration of structural MS data for structural biology. We quantify how often these methods are used together and provide examples where such combinations have been fruitful. To illustrate the power of integrative approaches, we discuss progress in solving the structures of the proteasome and the nuclear pore complex. We also discuss how information from structural MS, particularly pertaining to protein dynamics, is not currently utilized in integrative workflows and how such information can provide a more accurate picture of the systems studied. We conclude by discussing new developments in the MS and computational fields that will further enable in-cell structural studies.
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Affiliation(s)
- Hannah M Britt
- Institute of Structural and Molecular Biology, Division of Biosciences, University College London, London WC1E 6BT, United Kingdom
| | - Tristan Cragnolini
- Institute of Structural and Molecular Biology, Division of Biosciences, University College London, London WC1E 6BT, United Kingdom.,Institute of Structural and Molecular Biology, Birkbeck College, University of London, London WC1E 7HX, United Kingdom
| | - Konstantinos Thalassinos
- Institute of Structural and Molecular Biology, Division of Biosciences, University College London, London WC1E 6BT, United Kingdom.,Institute of Structural and Molecular Biology, Birkbeck College, University of London, London WC1E 7HX, United Kingdom
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16
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HUANG M, WU H, YU W, WANG Y, WANG F, ZHANG C, ZHOU L, LI Z. [Rapid identification of chemical components in Qi-Yu-San-Long decoction by ultra high performance liquid chromatography-quadrupole time-of-flight mass spectrometry]. Se Pu 2021; 39:730-743. [PMID: 34227371 PMCID: PMC9404180 DOI: 10.3724/sp.j.1123.2020.10016] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2020] [Indexed: 11/25/2022] Open
Abstract
Qi-Yu-San-Long decoction (QYSLD) is a classic traditional Chinese medicine prescription consisting of ten types of herbal medicines, including Astragali Radix, Polygonati Odorati Rhizoma, Scolopendra, Pheretima, Solanum nigrum L., Hedyotis diffusa Willd., Coicis Semen, Euphorbia helioscopia L., Curcumae Rhizoma, and Fritillariae Cirrhosae Bulbus, combined in a ratio of 15∶5∶3∶3∶10∶10∶10∶3∶5∶3 by weight. QYSLD has been used to treat non-small cell lung cancer (NSCLC) for over 20 years in clinical practice, and its curative effect is considered credible. However, the chemical constituents of QYSLD have not been revealed because of their complexity, which has significantly hindered the systematic clarification of the efficacy of the materials and quality evaluation. In this study, a reliable strategy based on the data-independent acquisition (DIA) technology of ultra high performance liquid chromatography-quadrupole time-of-flight mass spectrometry (UPLC-QTOF-MS) combined with a targeted screening method was established to investigate the chemical components of QYSLD. A 2-μL aliquot from each vial was injected into a Waters ACQUITY UPLC BEH C18 column (100 mm×2.1 mm, 1.7 μm) to separate complex components. The temperature of the column was 35 ℃, and the flow rate was set at 0.2 mL/min. The mobile phase consisted of 0.1% formic acid aqueous solution and acetonitrile. Detection was conducted using an Xevo G2-XS QTOF-MS with a LockSpray capable-electrospray interface. The data for complex components in QYSLD were collected by full-information tandem mass spectrometry (MS E) in the positive and negative ion modes. In the MSE mode, data acquisition was performed using a mass spectrometer by rapidly switching from a low-collision-energy (CE) scan to a high-CE scan during a single LC run. Thus, accurate precursor and fragment ions were collected in a single run, which was helpful for the structural elucidation of multiple components in QYSLD. In addition, systematic information on isolated chemical compounds was collected and distinguished from the ten individual herbs in QYSLD using databases such as China Academic Journals Full-text database (CNKI), PubMed, Web of Science, Medline, and ChemSpider. Accordingly, a self-building library of QYSLD, including the component name, molecular formula, and structure of the components from the herbs, was established. Subsequently, the raw MSE data of the collected samples and the self-building chemical composition library were imported into a natural product post-processing screening (UNIFI) platform for targeted screening of the chemical components in QYSLD. The parameters for UNIFI platform were as follows: the retention time deviation was ±0.1 min; an error margin of no more than 5×10 -6 for the identified compounds was allowed; positive adducts, including [M+H]+and [M+Na]+, were selected; and negative adducts, including [M-H]- and [M+HCOO]-, were selected. The results showed that a total of 166 compounds were initially identified, including 22 saponins, 13 alkaloids, 27 flavonoids, 32 terpenes, 20 amino acids, 16 phenylpropanoids, 9 organic acids, 6 sterols, 6 anthraquinones, and 15 other components. Among them, sixteen components were confirmed unambiguously with the reference substances. To better understand the chemical contribution of individual herbs to the entire decoction, the attributes of each component were summarized. This study provides a foundation for exploring the pharmacodynamic substances of QYSLD.
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17
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Ye S, Zhai L, Hu H, Tan M, Du S. BoxCar increases the depth and reproducibility of diabetic urinary proteome analysis. Proteomics Clin Appl 2021; 15:e2000092. [PMID: 33929778 DOI: 10.1002/prca.202000092] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2020] [Revised: 04/18/2021] [Indexed: 11/06/2022]
Abstract
PURPOSE Mass spectrometry-based proteomics performs well in high throughput detection of urinary proteins. Nonetheless, protein identification depth and reproducibility remain the challenges in diabetic urinary proteome with high complexity and broad dynamic range, especially for low-abundant proteins. As a new data acquisition strategy, the BoxCar method was reported to benefit for low-abundant protein identification. Whether it is propitious to diabetic samples with high dynamic range proteomes has not been discussed yet. We aimed to apply BoxCar method to diabetic urine sample analysis, and to compare it with standard data dependent acquisition (DDA) method on protein identification in detail. EXPERIMENTAL DESIGN We performed seven technical replicates analysis on two urine samples from healthy individuals and diabetic patients to evaluate protein detection of BoxCar and standard DDA methods on single sample. Further comparison of two methods was made on multiple diabetic urine samples. RESULTS BoxCar could increase over 20% of identified proteins and performed better quantitative reproducibility than standard DDA method either in single or multiple diabetic urinary samples. BoxCar also improved the detection of low-abundant proteins. Functional enrichment analysis of normal albuminuria or microalbuminuria samples indicated that BoxCar acquired more diabetes-related biological information. CONCLUSIONS AND CLINICAL RELEVANCE The study demonstrates that BoxCar could enhance the depth and reproducibility in diabetic urinary proteome analysis, which provides reference for mass spectrometry approach selection in clinical urinary proteomic research.
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Affiliation(s)
- Shu Ye
- Department of Endocrinology, Xinhua Hospital Affiliated to Shanghai Jiaotong University, School of Medicine, Shanghai, China
| | - Linhui Zhai
- State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China
| | - Hao Hu
- State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China
| | - Minjia Tan
- State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China
| | - Shichun Du
- Department of Endocrinology, Xinhua Hospital Affiliated to Shanghai Jiaotong University, School of Medicine, Shanghai, China
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18
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Chen ZL, Mao PZ, Zeng WF, Chi H, He SM. pDeepXL: MS/MS Spectrum Prediction for Cross-Linked Peptide Pairs by Deep Learning. J Proteome Res 2021; 20:2570-2582. [PMID: 33821641 DOI: 10.1021/acs.jproteome.0c01004] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
In cross-linking mass spectrometry, the identification of cross-linked peptide pairs heavily relies on the ability of a database search engine to measure the similarities between experimental and theoretical MS/MS spectra. However, the lack of accurate ion intensities in theoretical spectra impairs the performance of search engines, in particular, on proteome scales. Here we introduce pDeepXL, a deep neural network to predict MS/MS spectra of cross-linked peptide pairs. To train pDeepXL, we used the transfer-learning technique because it facilitated the training with limited benchmark data of cross-linked peptide pairs. Test results on more than ten data sets showed that pDeepXL accurately predicted the spectra of both noncleavable DSS/BS3/Leiker cross-linked peptide pairs (>80% of predicted spectra have Pearson's r values higher than 0.9) and cleavable DSSO/DSBU cross-linked peptide pairs (>75% of predicted spectra have Pearson's r values higher than 0.9). pDeepXL also achieved the accurate prediction on unseen data sets using an online fine-tuning technique. Lastly, integrating pDeepXL into a database search engine increased the number of identified cross-link spectra by 18% on average.
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Affiliation(s)
- Zhen-Lin Chen
- Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS), Institute of Computing Technology, CAS, Beijing 100190, China.,University of Chinese Academy of Sciences, Beijing 100049, China
| | - Peng-Zhi Mao
- Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS), Institute of Computing Technology, CAS, Beijing 100190, China.,University of Chinese Academy of Sciences, Beijing 100049, China
| | - Wen-Feng Zeng
- Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS), Institute of Computing Technology, CAS, Beijing 100190, China.,University of Chinese Academy of Sciences, Beijing 100049, China
| | - Hao Chi
- Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS), Institute of Computing Technology, CAS, Beijing 100190, China.,University of Chinese Academy of Sciences, Beijing 100049, China
| | - Si-Min He
- Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS), Institute of Computing Technology, CAS, Beijing 100190, China.,University of Chinese Academy of Sciences, Beijing 100049, China
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19
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Kalathiya U, Padariya M, Faktor J, Coyaud E, Alfaro JA, Fahraeus R, Hupp TR, Goodlett DR. Interfaces with Structure Dynamics of the Workhorses from Cells Revealed through Cross-Linking Mass Spectrometry (CLMS). Biomolecules 2021; 11:382. [PMID: 33806612 PMCID: PMC8001575 DOI: 10.3390/biom11030382] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2021] [Revised: 02/26/2021] [Accepted: 03/01/2021] [Indexed: 12/28/2022] Open
Abstract
The fundamentals of how protein-protein/RNA/DNA interactions influence the structures and functions of the workhorses from the cells have been well documented in the 20th century. A diverse set of methods exist to determine such interactions between different components, particularly, the mass spectrometry (MS) methods, with its advanced instrumentation, has become a significant approach to analyze a diverse range of biomolecules, as well as bring insights to their biomolecular processes. This review highlights the principal role of chemistry in MS-based structural proteomics approaches, with a particular focus on the chemical cross-linking of protein-protein/DNA/RNA complexes. In addition, we discuss different methods to prepare the cross-linked samples for MS analysis and tools to identify cross-linked peptides. Cross-linking mass spectrometry (CLMS) holds promise to identify interaction sites in larger and more complex biological systems. The typical CLMS workflow allows for the measurement of the proximity in three-dimensional space of amino acids, identifying proteins in direct contact with DNA or RNA, and it provides information on the folds of proteins as well as their topology in the complexes. Principal CLMS applications, its notable successes, as well as common pipelines that bridge proteomics, molecular biology, structural systems biology, and interactomics are outlined.
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Affiliation(s)
- Umesh Kalathiya
- International Centre for Cancer Vaccine Science, University of Gdansk, ul. Kładki 24, 80-822 Gdansk, Poland; (M.P.); (J.F.); (J.A.A.); (R.F.); (T.R.H.)
| | - Monikaben Padariya
- International Centre for Cancer Vaccine Science, University of Gdansk, ul. Kładki 24, 80-822 Gdansk, Poland; (M.P.); (J.F.); (J.A.A.); (R.F.); (T.R.H.)
| | - Jakub Faktor
- International Centre for Cancer Vaccine Science, University of Gdansk, ul. Kładki 24, 80-822 Gdansk, Poland; (M.P.); (J.F.); (J.A.A.); (R.F.); (T.R.H.)
| | - Etienne Coyaud
- Protéomique Réponse Inflammatoire Spectrométrie de Mass—PRISM, Inserm U1192, University Lille, CHU Lille, F-59000 Lille, France;
| | - Javier A. Alfaro
- International Centre for Cancer Vaccine Science, University of Gdansk, ul. Kładki 24, 80-822 Gdansk, Poland; (M.P.); (J.F.); (J.A.A.); (R.F.); (T.R.H.)
- Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, Scotland EH4 2XR, UK
| | - Robin Fahraeus
- International Centre for Cancer Vaccine Science, University of Gdansk, ul. Kładki 24, 80-822 Gdansk, Poland; (M.P.); (J.F.); (J.A.A.); (R.F.); (T.R.H.)
| | - Ted R. Hupp
- International Centre for Cancer Vaccine Science, University of Gdansk, ul. Kładki 24, 80-822 Gdansk, Poland; (M.P.); (J.F.); (J.A.A.); (R.F.); (T.R.H.)
- Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, Scotland EH4 2XR, UK
| | - David R. Goodlett
- International Centre for Cancer Vaccine Science, University of Gdansk, ul. Kładki 24, 80-822 Gdansk, Poland; (M.P.); (J.F.); (J.A.A.); (R.F.); (T.R.H.)
- Department of Biochemistry & Microbiology, University of Victoria, Victoria, BC V8Z 7X8, Canada
- Genome BC Proteome Centre, University of Victoria, Victoria, BC V8Z 5N3, Canada
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20
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Ma L, Muscat JE, Sinha R, Sun D, Xiu G. Proteomics of exhaled breath condensate in lung cancer and controls using data-independent acquisition (DIA): a pilot study. J Breath Res 2021; 15. [DOI: 10.1088/1752-7163/abd07e] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2020] [Accepted: 12/03/2020] [Indexed: 12/13/2022]
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21
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Chantada-Vázquez MDP, García Vence M, Serna A, Núñez C, Bravo SB. SWATH-MS Protocols in Human Diseases. Methods Mol Biol 2021; 2259:105-141. [PMID: 33687711 DOI: 10.1007/978-1-0716-1178-4_7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
Identification of molecular biomarkers for human diseases is one of the most important disciplines in translational science as it helps to elucidate their origin and early progression. Thus, it is a key factor in better diagnosis, prognosis, and treatment. Proteomics can help to solve the problem of sample complexity when the most common primary sample specimens were analyzed: organic fluids of easy access. The latest developments in high-throughput and label-free quantitative proteomics (SWATH-MS), together with more advanced liquid chromatography, have enabled the analysis of large sample sets with the sensitivity and depth needed to succeed in this task. In this chapter, we show different sample processing methods (major protein depletion, digestion, etc.) and a micro LC-SWATH-MS protocol to identify/quantify several proteins in different types of samples (serum/plasma, saliva, urine, tears).
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Affiliation(s)
| | - María García Vence
- Proteomic Unit, Instituto de Investigaciones Sanitarias-IDIS, Complejo Hospitalario Universitario de Santiago de Compostela (CHUS), Santiago de Compostela, Spain
| | | | - Cristina Núñez
- Research Unit, Hospital Universitario Lucus Augusti (HULA), Servizo Galego de Saúde (SERGAS), Lugo, Spain.
| | - Susana B Bravo
- Proteomic Unit, Instituto de Investigaciones Sanitarias-IDIS, Complejo Hospitalario Universitario de Santiago de Compostela (CHUS), Santiago de Compostela, Spain.
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22
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Rozanova S, Barkovits K, Nikolov M, Schmidt C, Urlaub H, Marcus K. Quantitative Mass Spectrometry-Based Proteomics: An Overview. Methods Mol Biol 2021; 2228:85-116. [PMID: 33950486 DOI: 10.1007/978-1-0716-1024-4_8] [Citation(s) in RCA: 77] [Impact Index Per Article: 25.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
In recent decades, mass spectrometry has moved more than ever before into the front line of protein-centered research. After being established at the qualitative level, the more challenging question of quantification of proteins and peptides using mass spectrometry has become a focus for further development. In this chapter, we discuss and review actual strategies and problems of the methods for the quantitative analysis of peptides, proteins, and finally proteomes by mass spectrometry. The common themes, the differences, and the potential pitfalls of the main approaches are presented in order to provide a survey of the emerging field of quantitative, mass spectrometry-based proteomics.
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Affiliation(s)
- Svitlana Rozanova
- Medizinisches Proteom-Center, Medical Faculty, Ruhr-University Bochum, Bochum, Germany.,Medical Proteome Analysis, Center for protein diagnostics (PRODI), Ruhr-University Bochum, Bochum, Germany
| | - Katalin Barkovits
- Medizinisches Proteom-Center, Medical Faculty, Ruhr-University Bochum, Bochum, Germany.,Medical Proteome Analysis, Center for protein diagnostics (PRODI), Ruhr-University Bochum, Bochum, Germany
| | - Miroslav Nikolov
- Bioanalytical Mass Spectrometry Group, Max Planck Institute for Biophysical Chemistry, Goettingen, Germany
| | - Carla Schmidt
- Interdisciplinary Research Center HALOmem, Charles Tanford Protein Center, Institute for Biochemistry and Biotechnology, Martin Luther University Halle-Wittenberg, Halle, Germany
| | - Henning Urlaub
- Bioanalytical Mass Spectrometry Group, Max Planck Institute for Biophysical Chemistry, Goettingen, Germany.,Bioanalytics Group, Institute of Clinical Chemistry, University Medical Center Goettingen, Goettingen, Germany.,Hematology/Oncology, Department of Medicine II, Johann Wolfgang Goethe University, Frankfurt, Germany
| | - Katrin Marcus
- Medizinisches Proteom-Center, Medical Faculty, Ruhr-University Bochum, Bochum, Germany. .,Medical Proteome Analysis, Center for protein diagnostics (PRODI), Ruhr-University Bochum, Bochum, Germany.
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23
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Sinitcyn P, Hamzeiy H, Salinas Soto F, Itzhak D, McCarthy F, Wichmann C, Steger M, Ohmayer U, Distler U, Kaspar-Schoenefeld S, Prianichnikov N, Yılmaz Ş, Rudolph JD, Tenzer S, Perez-Riverol Y, Nagaraj N, Humphrey SJ, Cox J. MaxDIA enables library-based and library-free data-independent acquisition proteomics. Nat Biotechnol 2021; 39:1563-1573. [PMID: 34239088 PMCID: PMC8668435 DOI: 10.1038/s41587-021-00968-7] [Citation(s) in RCA: 82] [Impact Index Per Article: 27.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2020] [Accepted: 05/27/2021] [Indexed: 02/06/2023]
Abstract
MaxDIA is a software platform for analyzing data-independent acquisition (DIA) proteomics data within the MaxQuant software environment. Using spectral libraries, MaxDIA achieves deep proteome coverage with substantially better coefficients of variation in protein quantification than other software. MaxDIA is equipped with accurate false discovery rate (FDR) estimates on both library-to-DIA match and protein levels, including when using whole-proteome predicted spectral libraries. This is the foundation of discovery DIA-hypothesis-free analysis of DIA samples without library and with reliable FDR control. MaxDIA performs three- or four-dimensional feature detection of fragment data, and scoring of matches is augmented by machine learning on the features of an identification. MaxDIA's bootstrap DIA workflow performs multiple rounds of matching with increasing quality of recalibration and stringency of matching to the library. Combining MaxDIA with two new technologies-BoxCar acquisition and trapped ion mobility spectrometry-both lead to deep and accurate proteome quantification.
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Affiliation(s)
- Pavel Sinitcyn
- grid.418615.f0000 0004 0491 845XComputational Systems Biochemistry Research Group, Max-Planck Institute of Biochemistry, Martinsried, Germany
| | - Hamid Hamzeiy
- grid.418615.f0000 0004 0491 845XComputational Systems Biochemistry Research Group, Max-Planck Institute of Biochemistry, Martinsried, Germany
| | - Favio Salinas Soto
- grid.418615.f0000 0004 0491 845XComputational Systems Biochemistry Research Group, Max-Planck Institute of Biochemistry, Martinsried, Germany
| | - Daniel Itzhak
- grid.499295.aChan Zuckerberg Biohub, San Francisco, CA USA
| | - Frank McCarthy
- grid.499295.aChan Zuckerberg Biohub, San Francisco, CA USA
| | - Christoph Wichmann
- grid.418615.f0000 0004 0491 845XComputational Systems Biochemistry Research Group, Max-Planck Institute of Biochemistry, Martinsried, Germany
| | | | | | - Ute Distler
- grid.5802.f0000 0001 1941 7111Institute for Immunology, Johannes Gutenberg University, Mainz, Germany
| | | | - Nikita Prianichnikov
- grid.418615.f0000 0004 0491 845XComputational Systems Biochemistry Research Group, Max-Planck Institute of Biochemistry, Martinsried, Germany
| | - Şule Yılmaz
- grid.418615.f0000 0004 0491 845XComputational Systems Biochemistry Research Group, Max-Planck Institute of Biochemistry, Martinsried, Germany
| | - Jan Daniel Rudolph
- grid.418615.f0000 0004 0491 845XComputational Systems Biochemistry Research Group, Max-Planck Institute of Biochemistry, Martinsried, Germany ,grid.6584.f0000 0004 0553 2276Bosch Center for Artificial Intelligence, Renningen, Germany
| | - Stefan Tenzer
- grid.5802.f0000 0001 1941 7111Institute for Immunology, Johannes Gutenberg University, Mainz, Germany
| | - Yasset Perez-Riverol
- grid.225360.00000 0000 9709 7726European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, UK
| | | | - Sean J. Humphrey
- grid.1013.30000 0004 1936 834XSchool of Life and Environmental Sciences, Charles Perkins Centre, University of Sydney, Camperdown, New South Wales Australia
| | - Jürgen Cox
- grid.418615.f0000 0004 0491 845XComputational Systems Biochemistry Research Group, Max-Planck Institute of Biochemistry, Martinsried, Germany ,grid.7914.b0000 0004 1936 7443Department of Biological and Medical Psychology, University of Bergen, Bergen, Norway
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24
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Matzinger M, Mechtler K. Cleavable Cross-Linkers and Mass Spectrometry for the Ultimate Task of Profiling Protein-Protein Interaction Networks in Vivo. J Proteome Res 2021; 20:78-93. [PMID: 33151691 PMCID: PMC7786381 DOI: 10.1021/acs.jproteome.0c00583] [Citation(s) in RCA: 47] [Impact Index Per Article: 15.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2020] [Indexed: 12/11/2022]
Abstract
Cross-linking mass spectrometry (XL-MS) has matured into a potent tool to identify protein-protein interactions or to uncover protein structures in living cells, tissues, or organelles. The unique ability to investigate the interplay of proteins within their native environment delivers valuable complementary information to other advanced structural biology techniques. This Review gives a comprehensive overview of the current possible applications as well as the remaining limitations of the technique, focusing on cross-linking in highly complex biological systems like cells, organelles, or tissues. Thanks to the commercial availability of most reagents and advances in user-friendly data analysis, validation, and visualization tools, studies using XL-MS can, in theory, now also be utilized by nonexpert laboratories.
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Affiliation(s)
- Manuel Matzinger
- Research
Institute of Molecular Pathology (IMP), Campus-Vienna-Biocenter 1, Vienna 1030, Austria
| | - Karl Mechtler
- Research
Institute of Molecular Pathology (IMP), Campus-Vienna-Biocenter 1, Vienna 1030, Austria
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25
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Abstract
Cross-linking, in general, involves the covalent linkage of two amino acid residues of proteins or protein complexes in close proximity. Mass spectrometry and computational analysis are then applied to identify the formed linkage and deduce structural information such as distance restraints. Quantitative cross-linking coupled with mass spectrometry is well suited to study protein dynamics and conformations of protein complexes. The quantitative cross-linking workflow described here is based on the application of isotope labelled cross-linkers. Proteins or protein complexes present in different structural states are differentially cross-linked using a "light" and a "heavy" cross-linker. The intensity ratios of cross-links (i.e., light/heavy or heavy/light) indicate structural changes or interactions that are maintained in the different states. These structural insights lead to a better understanding of the function of the proteins or protein complexes investigated. The described workflow is applicable to a wide range of research questions including, for instance, protein dynamics or structural changes upon ligand binding.
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Affiliation(s)
- Marie Barth
- Interdisciplinary Research Center HALOmem, Charles Tanford Protein Center, Institute for Biochemistry and Biotechnology, Martin Luther University Halle-Wittenberg, Halle, Germany
| | - Carla Schmidt
- Interdisciplinary Research Center HALOmem, Charles Tanford Protein Center, Institute for Biochemistry and Biotechnology, Martin Luther University Halle-Wittenberg, Halle, Germany.
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26
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Sinnott M, Malhotra S, Madhusudhan MS, Thalassinos K, Topf M. Combining Information from Crosslinks and Monolinks in the Modeling of Protein Structures. Structure 2020; 28:1061-1070.e3. [DOI: 10.1016/j.str.2020.05.012] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2020] [Revised: 05/08/2020] [Accepted: 05/22/2020] [Indexed: 11/30/2022]
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27
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Müller F, Rappsilber J. A protocol for studying structural dynamics of proteins by quantitative crosslinking mass spectrometry and data-independent acquisition. J Proteomics 2020; 218:103721. [PMID: 32109607 DOI: 10.1016/j.jprot.2020.103721] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2019] [Revised: 11/13/2019] [Accepted: 02/24/2020] [Indexed: 10/24/2022]
Abstract
Quantitative crosslinking mass spectrometry (QCLMS) reveals structural details of protein conformations in solution. QCLMS can benefit from data-independent acquisition (DIA), which maximises accuracy, reproducibility and throughput of the approach. This DIA-QCLMS protocol comprises of three main sections: sample preparation, spectral library generation and quantitation. The DIA-QCLMS workflow supports isotope-labelling as well as label-free quantitation strategies, uses xiSEARCH for crosslink identification, and xiDIA-Library to create a spectral library for a peptide-centric quantitative approach. We integrated Spectronaut, a leading quantitation software, to analyse DIA data. Spectronaut supports DIA-QCLMS data to quantify crosslinks. It can be used to reveal the structural dynamics of proteins and protein complexes, even against a complex background. In combination with photoactivatable crosslinkers (photo-DIA-QCLMS), the workflow can increase data density and better capture protein dynamics due to short reaction times. Additionally, this can reveal conformational changes caused by environmental influences that would otherwise affect crosslinking itself, such as changing pH conditions. SIGNIFICANCE: This protocol is an detailed step-by-step description on how to implement our previously published DIA-QCLMS workflow (Müller et al. Mol Cell Proteomics. 2019 Apr;18(4):786-795). It includes sample preparation for QCLMS, Optimization of DIA strategies, implementation of the Spectronaut software and required python scripts and guideline on how to analyse quantitative crosslinking data. The DIA-QCLMS workflow widen the scope for a range of new crosslinking applications and this step-by-step protocol enhances the accessibility to a broad scientific user base.
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Affiliation(s)
- Fränze Müller
- Bioanalytics, Institute of Biotechnology, Technische Universität Berlin, 13355 Berlin, Germany
| | - Juri Rappsilber
- Bioanalytics, Institute of Biotechnology, Technische Universität Berlin, 13355 Berlin, Germany; Wellcome Centre for Cell Biology, School of Biological Sciences, University of Edinburgh, Edinburgh EH9 3BF, Scotland, United Kingdom.
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28
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Müller F, Graziadei A, Rappsilber J. Quantitative Photo-crosslinking Mass Spectrometry Revealing Protein Structure Response to Environmental Changes. Anal Chem 2019; 91:9041-9048. [PMID: 31274288 PMCID: PMC6639777 DOI: 10.1021/acs.analchem.9b01339] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2019] [Accepted: 06/17/2019] [Indexed: 12/14/2022]
Abstract
Protein structures respond to changes in their chemical and physical environment. However, studying such conformational changes is notoriously difficult, as many structural biology techniques are also affected by these parameters. Here, the use of photo-crosslinking, coupled with quantitative crosslinking mass spectrometry (QCLMS), offers an opportunity, since the reactivity of photo-crosslinkers is unaffected by changes in environmental parameters. In this study, we introduce a workflow combining photo-crosslinking using sulfosuccinimidyl 4,4'-azipentanoate (sulfo-SDA) with our recently developed data-independent acquisition (DIA)-QCLMS. This novel photo-DIA-QCLMS approach is then used to quantify pH-dependent conformational changes in human serum albumin (HSA) and cytochrome C by monitoring crosslink abundances as a function of pH. Both proteins show pH-dependent conformational changes resulting in acidic and alkaline transitions. 93% and 95% of unique residue pairs (URP) were quantifiable across triplicates for HSA and cytochrome C, respectively. Abundance changes of URPs and hence conformational changes of both proteins were visualized using hierarchical clustering. For HSA we distinguished the N-F and the N-B form from the native conformation. In addition, we observed for cytochrome C acidic and basic conformations. In conclusion, our photo-DIA-QCLMS approach distinguished pH-dependent conformers of both proteins.
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Affiliation(s)
- Fränze Müller
- Bioanalytics,
Institute of Biotechnology, Technische Universität
Berlin, 13355 Berlin, Germany
| | - Andrea Graziadei
- Bioanalytics,
Institute of Biotechnology, Technische Universität
Berlin, 13355 Berlin, Germany
| | - Juri Rappsilber
- Bioanalytics,
Institute of Biotechnology, Technische Universität
Berlin, 13355 Berlin, Germany
- Wellcome
Centre for Cell Biology, School of Biological Sciences, University of Edinburgh, Edinburgh EH9 3BF, Scotland, United Kingdom
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