1
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Chakrabarty S, Wang S, Roychowdhury T, Ginsberg SD, Chiosis G. Introducing dysfunctional Protein-Protein Interactome (dfPPI) - A platform for systems-level protein-protein interaction (PPI) dysfunction investigation in disease. Curr Opin Struct Biol 2024; 88:102886. [PMID: 39003916 PMCID: PMC11392609 DOI: 10.1016/j.sbi.2024.102886] [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: 05/05/2024] [Revised: 06/19/2024] [Accepted: 06/21/2024] [Indexed: 07/16/2024]
Abstract
Protein-protein interactions (PPIs) play a crucial role in cellular function and disease manifestation, with dysfunctions in PPI networks providing a direct link between stressors and phenotype. The dysfunctional Protein-Protein Interactome (dfPPI) platform, formerly known as epichaperomics, is a newly developed chemoproteomic method aimed at detecting dynamic changes at the systems level in PPI networks under stressor-induced cellular perturbations within disease states. This review provides an overview of dfPPIs, emphasizing the novel methodology, data analytics, and applications in disease research. dfPPI has applications in cancer research, where it identifies dysfunctions integral to maintaining malignant phenotypes and discovers strategies to enhance the efficacy of current therapies. In neurodegenerative disorders, dfPPI uncovers critical dysfunctions in cellular processes and stressor-specific vulnerabilities. Challenges, including data complexity and the potential for integration with other omics datasets are discussed. The dfPPI platform is a potent tool for dissecting disease systems biology by directly informing on dysfunctions in PPI networks and holds promise for advancing disease identification and therapeutics.
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Affiliation(s)
- Souparna Chakrabarty
- Chemical Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
| | - Shujuan Wang
- Chemical Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
| | - Tanaya Roychowdhury
- Chemical Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
| | - Stephen D Ginsberg
- Departments of Psychiatry, Neuroscience & Physiology & the NYU Neuroscience Institute, NYU Grossman School of Medicine, New York, NY, 10016, USA; Center for Dementia Research, Nathan Kline Institute, Orangeburg, NY, 10962, USA
| | - Gabriela Chiosis
- Chemical Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA; Department of Medicine, Division of Solid Tumors, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA.
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2
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Akkulak H, İnce HK, Goc G, Lebrilla CB, Kabasakal BV, Ozcan S. Structural proteomics of a bacterial mega membrane protein complex: FtsH-HflK-HflC. Int J Biol Macromol 2024; 269:131923. [PMID: 38697437 DOI: 10.1016/j.ijbiomac.2024.131923] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2024] [Revised: 04/24/2024] [Accepted: 04/25/2024] [Indexed: 05/05/2024]
Abstract
Recent advances in mass spectrometry (MS) yielding sensitive and accurate measurements along with developments in software tools have enabled the characterization of complex systems routinely. Thus, structural proteomics and cross-linking mass spectrometry (XL-MS) have become a useful method for structural modeling of protein complexes. Here, we utilized commonly used XL-MS software tools to elucidate the protein interactions within a membrane protein complex containing FtsH, HflK, and HflC, over-expressed in E. coli. The MS data were processed using MaxLynx, MeroX, MS Annika, xiSEARCH, and XlinkX software tools. The number of identified inter- and intra-protein cross-links varied among software. Each interaction was manually checked using the raw MS and MS/MS data and distance restraints to verify inter- and intra-protein cross-links. A total of 37 inter-protein and 148 intra-protein cross-links were determined in the FtsH-HflK-HflC complex. The 59 of them were new interactions on the lacking region of recently published structures. These newly identified interactions, when combined with molecular docking and structural modeling, present opportunities for further investigation. The results provide valuable information regarding the complex structure and function to decipher the intricate molecular mechanisms underlying the FtsH-HflK-HflC complex.
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Affiliation(s)
- Hatice Akkulak
- Department of Chemistry, Middle East Technical University, Ankara 06800, Turkiye
| | - H Kerim İnce
- Department of Chemistry, Middle East Technical University, Ankara 06800, Turkiye
| | - Gunce Goc
- Turkish Accelerator and Radiation Laboratory (TARLA), Ankara 06830, Turkiye
| | - Carlito B Lebrilla
- Department of Chemistry, University of California, Davis, 95616, CA, USA
| | - Burak V Kabasakal
- Turkish Accelerator and Radiation Laboratory (TARLA), Ankara 06830, Turkiye; School of Biochemistry, University of Bristol, Bristol BS8 1TD, UK.
| | - Sureyya Ozcan
- Department of Chemistry, Middle East Technical University, Ankara 06800, Turkiye; Cancer Systems Biology Laboratory (CanSyL), Middle East Technical University, 06800 Ankara, Turkiye
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3
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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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4
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Zhang Z, Zhao Q, Gong Z, Du R, Liu M, Zhang Y, Zhang L, Li C. Progress, Challenges and Opportunities of NMR and XL-MS for Cellular Structural Biology. JACS AU 2024; 4:369-383. [PMID: 38425916 PMCID: PMC10900494 DOI: 10.1021/jacsau.3c00712] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/14/2023] [Revised: 01/05/2024] [Accepted: 01/16/2024] [Indexed: 03/02/2024]
Abstract
The validity of protein structures and interactions, whether determined under ideal laboratory conditions or predicted by AI tools such as Alphafold2, to precisely reflect those found in living cells remains to be examined. Moreover, understanding the changes in protein structures and interactions in response to stimuli within living cells, under both normal and disease conditions, is key to grasping proteins' functionality and cellular processes. Nevertheless, achieving high-resolution identification of these protein structures and interactions within living cells presents a technical challenge. In this Perspective, we summarize the recent advancements in in-cell nuclear magnetic resonance (NMR) and in vivo cross-linking mass spectrometry (XL-MS) for studying protein structures and interactions within a cellular context. Additionally, we discuss the challenges, opportunities, and potential benefits of integrating in-cell NMR and in vivo XL-MS in future research to offer an exhaustive approach to studying proteins in their natural habitat.
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Affiliation(s)
- Zeting Zhang
- Key
Laboratory of Magnetic Resonance in Biological Systems, State Key
Laboratory of Magnetic Resonance and Atomic and Molecular Physics,
National Center for Magnetic Resonance in Wuhan, Wuhan Institute of
Physics and Mathematics, Innovation Academy of Precision Measurement, Chinese Academy of Sciences, Wuhan 430071, China
| | - Qun Zhao
- CAS
Key Laboratory of Separation Science for Analytical Chemistry, National
Chromatographic R. & A. Center, State Key Laboratory of Medical
Proteomics, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian, Liaoning 116023, China
| | - Zhou Gong
- Key
Laboratory of Magnetic Resonance in Biological Systems, State Key
Laboratory of Magnetic Resonance and Atomic and Molecular Physics,
National Center for Magnetic Resonance in Wuhan, Wuhan Institute of
Physics and Mathematics, Innovation Academy of Precision Measurement, Chinese Academy of Sciences, Wuhan 430071, China
| | - Ruichen Du
- Key
Laboratory of Magnetic Resonance in Biological Systems, State Key
Laboratory of Magnetic Resonance and Atomic and Molecular Physics,
National Center for Magnetic Resonance in Wuhan, Wuhan Institute of
Physics and Mathematics, Innovation Academy of Precision Measurement, Chinese Academy of Sciences, Wuhan 430071, China
- University
of Chinese Academy of Sciences, Beijing 10049, China
| | - Maili Liu
- Key
Laboratory of Magnetic Resonance in Biological Systems, State Key
Laboratory of Magnetic Resonance and Atomic and Molecular Physics,
National Center for Magnetic Resonance in Wuhan, Wuhan Institute of
Physics and Mathematics, Innovation Academy of Precision Measurement, Chinese Academy of Sciences, Wuhan 430071, China
| | - Yukui Zhang
- CAS
Key Laboratory of Separation Science for Analytical Chemistry, National
Chromatographic R. & A. Center, State Key Laboratory of Medical
Proteomics, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian, Liaoning 116023, China
| | - Lihua Zhang
- CAS
Key Laboratory of Separation Science for Analytical Chemistry, National
Chromatographic R. & A. Center, State Key Laboratory of Medical
Proteomics, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian, Liaoning 116023, China
| | - Conggang Li
- Key
Laboratory of Magnetic Resonance in Biological Systems, State Key
Laboratory of Magnetic Resonance and Atomic and Molecular Physics,
National Center for Magnetic Resonance in Wuhan, Wuhan Institute of
Physics and Mathematics, Innovation Academy of Precision Measurement, Chinese Academy of Sciences, Wuhan 430071, China
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5
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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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6
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Birklbauer MJ, Matzinger M, Müller F, Mechtler K, Dorfer V. MS Annika 2.0 Identifies Cross-Linked Peptides in MS2-MS3-Based Workflows at High Sensitivity and Specificity. J Proteome Res 2023; 22:3009-3021. [PMID: 37566781 PMCID: PMC10476269 DOI: 10.1021/acs.jproteome.3c00325] [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: 05/31/2023] [Indexed: 08/13/2023]
Abstract
Cross-linking mass spectrometry has become a powerful tool for the identification of protein-protein interactions and for gaining insight into the structures of proteins. We previously published MS Annika, a cross-linking search engine which can accurately identify cross-linked peptides in MS2 spectra from a variety of different MS-cleavable cross-linkers. In this publication, we present MS Annika 2.0, an updated version implementing a new search algorithm that, in addition to MS2 level, only supports the processing of data from MS2-MS3-based approaches for the identification of peptides from MS3 spectra, and introduces a novel scoring function for peptides identified across multiple MS stages. Detected cross-links are validated by estimating the false discovery rate (FDR) using a target-decoy approach. We evaluated the MS3-search-capabilities of MS Annika 2.0 on five different datasets covering a variety of experimental approaches and compared it to XlinkX and MaXLinker, two other cross-linking search engines. We show that MS Annika detects up to 4 times more true unique cross-links while simultaneously yielding less false positive hits and therefore a more accurate FDR estimation than the other two search engines. All mass spectrometry proteomics data along with result files have been deposited to the ProteomeXchange consortium via the PRIDE partner repository with the dataset identifier PXD041955.
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Affiliation(s)
- Micha J. Birklbauer
- Bioinformatics
Research Group, University of Applied Sciences
Upper Austria, Softwarepark
11, 4232 Hagenberg, Austria
| | - Manuel Matzinger
- Research
Institute of Molecular Pathology (IMP), Vienna BioCenter (VBC), Campus-Vienna-Biocenter 1, 1030 Vienna, Austria
| | - Fränze Müller
- Research
Institute of Molecular Pathology (IMP), Vienna BioCenter (VBC), Campus-Vienna-Biocenter 1, 1030 Vienna, Austria
| | - Karl Mechtler
- Research
Institute of Molecular Pathology (IMP), Vienna BioCenter (VBC), Campus-Vienna-Biocenter 1, 1030 Vienna, Austria
- Institute
of Molecular Biotechnology (IMBA), Austrian Academy of Sciences, Vienna
BioCenter (VBC), Dr.
Bohr-Gasse 3, 1030 Vienna, Austria
- Gregor
Mendel Institute (GMI), Austrian Academy of Sciences, Vienna BioCenter
(VBC), Dr. Bohr-Gasse
3, 1030 Vienna, Austria
| | - Viktoria Dorfer
- Bioinformatics
Research Group, University of Applied Sciences
Upper Austria, Softwarepark
11, 4232 Hagenberg, Austria
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7
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Faustino AM, Sharma P, Manriquez-Sandoval E, Yadav D, Fried SD. Progress toward Proteome-Wide Photo-Cross-Linking to Enable Residue-Level Visualization of Protein Structures and Networks In Vivo. Anal Chem 2023; 95:10670-10685. [PMID: 37341467 DOI: 10.1021/acs.analchem.3c01369] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/22/2023]
Abstract
Cross-linking mass spectrometry (XL-MS) is emerging as a method at the crossroads of structural and cellular biology, uniquely capable of identifying protein-protein interactions with residue-level resolution and on the proteome-wide scale. With the development of cross-linkers that can form linkages inside cells and easily cleave during fragmentation on the mass spectrometer (MS-cleavable cross-links), it has become increasingly facile to identify contacts between any two proteins in complex samples, including in live cells or tissues. Photo-cross-linkers possess the advantages of high temporal resolution and high reactivity, thereby engaging all residue-types (rather than just lysine); nevertheless, photo-cross-linkers have not enjoyed widespread use and are yet to be employed for proteome-wide studies because their products are challenging to identify. Here, we demonstrate the synthesis and application of two heterobifunctional photo-cross-linkers that feature diazirines and N-hydroxy-succinimidyl carbamate groups, the latter of which unveil doubly fissile MS-cleavable linkages upon acyl transfer to protein targets. Moreover, these cross-linkers demonstrate high water-solubility and cell-permeability. Using these compounds, we demonstrate the feasibility of proteome-wide photo-cross-linking in cellulo. These studies elucidate a small portion of Escherichia coli's interaction network, albeit with residue-level resolution. With further optimization, these methods will enable the detection of protein quinary interaction networks in their native environment at residue-level resolution, and we expect that they will prove useful toward the effort to explore the molecular sociology of the cell.
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Affiliation(s)
- Anneliese M Faustino
- Department of Chemistry, Johns Hopkins University, Baltimore, Maryland 21218, United States
| | - Piyoosh Sharma
- Department of Chemistry, Johns Hopkins University, Baltimore, Maryland 21218, United States
| | - Edgar Manriquez-Sandoval
- Thomas C. Jenkins Department of Biophysics, Johns Hopkins University, Baltimore, Maryland 21218, United States
| | - Divya Yadav
- Department of Chemistry, Johns Hopkins University, Baltimore, Maryland 21218, United States
| | - Stephen D Fried
- Department of Chemistry, Johns Hopkins University, Baltimore, Maryland 21218, United States
- Thomas C. Jenkins Department of Biophysics, Johns Hopkins University, Baltimore, Maryland 21218, United States
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8
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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: 5] [Impact Index Per Article: 5.0] [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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9
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Lee K, O'Reilly FJ. Cross-linking mass spectrometry for mapping protein complex topologies in situ. Essays Biochem 2023; 67:215-228. [PMID: 36734207 PMCID: PMC10070479 DOI: 10.1042/ebc20220168] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2022] [Revised: 01/03/2023] [Accepted: 01/05/2023] [Indexed: 02/04/2023]
Abstract
Cross-linking mass spectrometry has become an established technology to provide structural information on the topology and dynamics of protein complexes. Readily accessible workflows can provide detailed data on simplified systems, such as purified complexes. However, using this technology to study the structure of protein complexes in situ, such as in organelles, cells, and even tissues, is still a technological frontier. The complexity of these systems remains a considerable challenge, but there have been dramatic improvements in sample handling, data acquisition, and data processing. Here, we summarise these developments and describe the paths towards comprehensive and comparative structural interactomes by cross-linking mass spectrometry.
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Affiliation(s)
- Kitaik Lee
- Center for Structural Biology, Center for Cancer Research, National Cancer Institute (NCI), Frederick, MD 21702-1201, U.S.A
| | - Francis J O'Reilly
- Center for Structural Biology, Center for Cancer Research, National Cancer Institute (NCI), Frederick, MD 21702-1201, U.S.A
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10
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Zhang W, Shan Y, Zhao L, Liang Z, Liu C, Zhang L, Zhang Y. ComMap: a software to perform large-scale structure-based mapping for cross-linking mass spectrometry. Bioinformatics 2023; 39:7048661. [PMID: 36804670 PMCID: PMC9960907 DOI: 10.1093/bioinformatics/btad077] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2022] [Revised: 01/24/2023] [Indexed: 02/22/2023] Open
Abstract
MOTIVATION Chemical cross-linking combined with mass spectrometry (CXMS) is now a well-established method for profiling existing protein-protein interactions (PPIs) with partially known structures. It is expected to map the results of CXMS with existing structure databases to study the protein dynamic profile in the structure analysis. However, currently available structure-based analysis software suffers from the difficulty of achieving large-scale analysis. Besides, it is infeasible for structure analysis and data mining on a large scale, since of lacking global measurement of dynamic structure mapping results. RESULTS ComMap (protein complex structure mapping) is a software designed to perform large-scale structure-based mapping by integrating CXMS data with existing structures. It allows complete the distance calculation of PPIs with existing structures in batch within minutes and provides scores for different PPI-structure pairs of testable hypothetical structural dynamism via a global view. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Weijie Zhang
- CAS Key Laboratory of Separation Science for Analytical Chemistry, National Chromatographic R. & A. Center, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian, Liaoning 116023, China.,University of Chinese Academy of Sciences, Beijing 100039, China
| | - Yichu Shan
- CAS Key Laboratory of Separation Science for Analytical Chemistry, National Chromatographic R. & A. Center, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian, Liaoning 116023, China
| | - Lili Zhao
- CAS Key Laboratory of Separation Science for Analytical Chemistry, National Chromatographic R. & A. Center, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian, Liaoning 116023, China.,University of Chinese Academy of Sciences, Beijing 100039, China
| | - Zhen Liang
- CAS Key Laboratory of Separation Science for Analytical Chemistry, National Chromatographic R. & A. Center, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian, Liaoning 116023, China
| | - Chao Liu
- School of Engineering Medicine and School of Biological Science and Medical Engineering, Beihang University, Beijing 100191, China
| | - Lihua Zhang
- CAS Key Laboratory of Separation Science for Analytical Chemistry, National Chromatographic R. & A. Center, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian, Liaoning 116023, China
| | - Yukui Zhang
- CAS Key Laboratory of Separation Science for Analytical Chemistry, National Chromatographic R. & A. Center, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian, Liaoning 116023, China
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11
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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: 9] [Impact Index Per Article: 4.5] [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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12
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Matzinger M, Vasiu A, Madalinski M, Müller F, Stanek F, Mechtler K. Mimicked synthetic ribosomal protein complex for benchmarking crosslinking mass spectrometry workflows. Nat Commun 2022; 13:3975. [PMID: 35803948 PMCID: PMC9270371 DOI: 10.1038/s41467-022-31701-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2021] [Accepted: 06/21/2022] [Indexed: 11/09/2022] Open
Abstract
Cross-linking mass spectrometry has matured to a frequently used tool for the investigation of protein structures as well as interactome studies up to a system-wide level. The growing community generated a broad spectrum of applications, linker types, acquisition strategies and specialized data analysis tools, which makes it challenging to decide for an appropriate analysis workflow. Here, we report a large and flexible synthetic peptide library as reliable instrument to benchmark crosslink workflows. Additionally, we provide a tool, IMP-X-FDR, that calculates the real, experimentally validated, FDR, compares results across search engine platforms and analyses crosslink properties in an automated manner. We apply the library with 6 commonly used linker reagents and analyse the data with 6 established search engines. We thereby show that the correct algorithm and search setting choice is highly important to improve identification rate and reliability. We reach identification rates of up to ~70 % of the theoretical maximum (i.e. 700 unique lysine-lysine cross-links) while maintaining a real false-discovery-rate of <3 % at cross-link level with high reproducibility, representatively showing that our test system delivers valuable and statistically solid results. Cross-linking mass spectrometry is widely used to elucidate protein structures and interactions. Here, the authors generate an extensive peptide library to benchmark the most common cross-link search engines with frequently used cross-linking reagents in low and high complex sample systems.
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Affiliation(s)
- Manuel Matzinger
- Institute of Molecular Pathology (IMP), Vienna BioCenter (VBC), Vienna, Austria.
| | - Adrian Vasiu
- Institute of Molecular Pathology (IMP), Vienna BioCenter (VBC), Vienna, Austria
| | - Mathias Madalinski
- Institute of Molecular Pathology (IMP), Vienna BioCenter (VBC), Vienna, Austria
| | - Fränze Müller
- Institute of Molecular Pathology (IMP), Vienna BioCenter (VBC), Vienna, Austria
| | - Florian Stanek
- Institute of Molecular Pathology (IMP), Vienna BioCenter (VBC), Vienna, Austria
| | - Karl Mechtler
- Institute of Molecular Pathology (IMP), Vienna BioCenter (VBC), Vienna, Austria. .,Institute of Molecular Biotechnology, Austrian Academy of Sciences, Vienna BioCenter (VBC), Vienna, Austria.
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13
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Graziadei A, Rappsilber J. Leveraging crosslinking mass spectrometry in structural and cell biology. Structure 2021; 30:37-54. [PMID: 34895473 DOI: 10.1016/j.str.2021.11.007] [Citation(s) in RCA: 47] [Impact Index Per Article: 15.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2021] [Revised: 10/11/2021] [Accepted: 11/17/2021] [Indexed: 12/18/2022]
Abstract
Crosslinking mass spectrometry (crosslinking-MS) is a versatile tool providing structural insights into protein conformation and protein-protein interactions. Its medium-resolution residue-residue distance restraints have been used to validate protein structures proposed by other methods and have helped derive models of protein complexes by integrative structural biology approaches. The use of crosslinking-MS in integrative approaches is underpinned by progress in estimating error rates in crosslinking-MS data and in combining these data with other information. The flexible and high-throughput nature of crosslinking-MS has allowed it to complement the ongoing resolution revolution in electron microscopy by providing system-wide residue-residue distance restraints, especially for flexible regions or systems. Here, we review how crosslinking-MS information has been leveraged in structural model validation and integrative modeling. Crosslinking-MS has also been a key technology for cell biology studies and structural systems biology where, in conjunction with cryoelectron tomography, it can provide structural and mechanistic insights directly in situ.
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Affiliation(s)
- 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, University of Edinburgh, Max Born Crescent, Edinburgh EH9 3BF, UK.
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14
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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: 110] [Impact Index Per Article: 36.7] [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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15
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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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16
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Huang R, Zhu W, Xu Z, Chen J, Jiang B, Chen H, Chen W. Accurate Retention Time Prediction Based on Monolinked Peptide Information to Confidently Identify Cross-Linked Peptides. JOURNAL OF THE AMERICAN SOCIETY FOR MASS SPECTROMETRY 2021; 32:2410-2416. [PMID: 34320809 DOI: 10.1021/jasms.1c00120] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Cross-linking mass spectrometry methods have not been successfully applied to protein-protein interaction discovery at a proteome-wide level mainly due to the computation complexity (O (n2)) issue. In a previous report, we proposed a decision tree searching strategy (DTSS), which can reduce complexity by orders of magnitude. In this study, we further found that the monolinked peptides carry out the information on the retention time of the corresponding cross-linked pairs; therefore, the retention time of cross-linked peptide pairs can be predicted accurately. By utilizing the retention time as an extra filter, the false positive rate can be reduced by around 86% with a sensitivity loss of 10%. The method combined with DTSS (T-DTSS) not only benefits improving identification confidence but also leads to lower cutoff scores and facilitates substantially increasing inter-cross-link identification. T-DTSS was successfully applied to the identification of inter-cross-links obtained from Escherichia coli cell lysate cross-linked by a newly synthesized enrichable cross-linker, pDSBE. The approach can be applicable to both cleavable and noncleavable methods.
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Affiliation(s)
- Rong Huang
- Shanghai Institute for Advanced Immunochemical Studies, ShanghaiTech University, 393 Middle Huaxia Road, Pudong, Shanghai 201210, China
- University of Chinese Academy of Sciences, 19A Yuquan Road, Shijingshan District, Beijing 100049, China
| | - Wei Zhu
- Shanghai Institute for Advanced Immunochemical Studies, ShanghaiTech University, 393 Middle Huaxia Road, Pudong, Shanghai 201210, China
| | - Zili Xu
- Shanghai Institute for Advanced Immunochemical Studies, ShanghaiTech University, 393 Middle Huaxia Road, Pudong, Shanghai 201210, China
| | - Jiakang Chen
- Shanghai Institute for Advanced Immunochemical Studies, ShanghaiTech University, 393 Middle Huaxia Road, Pudong, Shanghai 201210, China
| | - Biao Jiang
- Shanghai Institute for Advanced Immunochemical Studies, ShanghaiTech University, 393 Middle Huaxia Road, Pudong, Shanghai 201210, China
| | - Hongli Chen
- Shanghai Institute for Advanced Immunochemical Studies, ShanghaiTech University, 393 Middle Huaxia Road, Pudong, Shanghai 201210, China
| | - Wenzhang Chen
- Shanghai Institute for Advanced Immunochemical Studies, ShanghaiTech University, 393 Middle Huaxia Road, Pudong, Shanghai 201210, China
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17
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Lenz S, Sinn LR, O'Reilly FJ, Fischer L, Wegner F, Rappsilber J. Reliable identification of protein-protein interactions by crosslinking mass spectrometry. Nat Commun 2021; 12:3564. [PMID: 34117231 PMCID: PMC8196013 DOI: 10.1038/s41467-021-23666-z] [Citation(s) in RCA: 69] [Impact Index Per Article: 23.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2020] [Accepted: 04/28/2021] [Indexed: 11/09/2022] Open
Abstract
Protein-protein interactions govern most cellular pathways and processes, and multiple technologies have emerged to systematically map them. Assessing the error of interaction networks has been a challenge. Crosslinking mass spectrometry is currently widening its scope from structural analyses of purified multi-protein complexes towards systems-wide analyses of protein-protein interactions (PPIs). Using a carefully controlled large-scale analysis of Escherichia coli cell lysate, we demonstrate that false-discovery rates (FDR) for PPIs identified by crosslinking mass spectrometry can be reliably estimated. We present an interaction network comprising 590 PPIs at 1% decoy-based PPI-FDR. The structural information included in this network localises the binding site of the hitherto uncharacterised protein YacL to near the DNA exit tunnel on the RNA polymerase.
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Affiliation(s)
- Swantje Lenz
- Bioanalytics, Institute of Biotechnology, Technische Universität Berlin, Berlin, Germany
| | - Ludwig R Sinn
- Bioanalytics, Institute of Biotechnology, Technische Universität Berlin, Berlin, Germany
| | - Francis J O'Reilly
- Bioanalytics, Institute of Biotechnology, Technische Universität Berlin, Berlin, Germany
| | - Lutz Fischer
- Bioanalytics, Institute of Biotechnology, Technische Universität Berlin, Berlin, Germany
| | - Fritz Wegner
- Bioanalytics, Institute of Biotechnology, Technische Universität Berlin, Berlin, Germany
| | - Juri Rappsilber
- Bioanalytics, Institute of Biotechnology, Technische Universität Berlin, Berlin, Germany. .,Wellcome Centre for Cell Biology, University of Edinburgh, Edinburgh, UK.
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18
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Giese SH, Sinn LR, Wegner F, Rappsilber J. Retention time prediction using neural networks increases identifications in crosslinking mass spectrometry. Nat Commun 2021; 12:3237. [PMID: 34050149 PMCID: PMC8163845 DOI: 10.1038/s41467-021-23441-0] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2020] [Accepted: 04/26/2021] [Indexed: 12/13/2022] Open
Abstract
Crosslinking mass spectrometry has developed into a robust technique that is increasingly used to investigate the interactomes of organelles and cells. However, the incomplete and noisy information in the mass spectra of crosslinked peptides limits the numbers of protein-protein interactions that can be confidently identified. Here, we leverage chromatographic retention time information to aid the identification of crosslinked peptides from mass spectra. Our Siamese machine learning model xiRT achieves highly accurate retention time predictions of crosslinked peptides in a multi-dimensional separation of crosslinked E. coli lysate. Importantly, supplementing the search engine score with retention time features leads to a substantial increase in protein-protein interactions without affecting confidence. This approach is not limited to cell lysates and multi-dimensional separation but also improves considerably the analysis of crosslinked multiprotein complexes with a single chromatographic dimension. Retention times are a powerful complement to mass spectrometric information to increase the sensitivity of crosslinking mass spectrometry analyses.
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Affiliation(s)
- Sven H Giese
- Bioanalytics, Institute of Biotechnology, Technische Universität Berlin, Berlin, Germany
- Data Analytics and Computational Statistics, Hasso Plattner Institute for Digital Engineering, Potsdam, Germany
- Digital Engineering Faculty, University of Potsdam, Potsdam, Germany
| | - Ludwig R Sinn
- Bioanalytics, Institute of Biotechnology, Technische Universität Berlin, Berlin, Germany
| | - Fritz Wegner
- Bioanalytics, Institute of Biotechnology, Technische Universität Berlin, Berlin, Germany
| | - Juri Rappsilber
- Bioanalytics, Institute of Biotechnology, Technische Universität Berlin, Berlin, Germany.
- Wellcome Centre for Cell Biology, School of Biological Sciences, University of Edinburgh, Edinburgh, UK.
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19
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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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20
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de Jong L, Roseboom W, Kramer G. Towards low false discovery rate estimation for protein-protein interactions detected by chemical cross-linking. BIOCHIMICA ET BIOPHYSICA ACTA-PROTEINS AND PROTEOMICS 2021; 1869:140655. [PMID: 33812047 DOI: 10.1016/j.bbapap.2021.140655] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Subscribe] [Scholar Register] [Received: 12/15/2020] [Revised: 03/28/2021] [Accepted: 03/29/2021] [Indexed: 01/16/2023]
Abstract
Chemical cross-linking (CX) of proteins in vivo or in cell free extracts followed by mass spectrometric (MS) identification of linked peptide pairs (CXMS) can reveal protein-protein interactions (PPIs) both at a proteome wide scale and the level of cross-linked amino acid residues. However, error estimation at the level of PPI remains challenging in large scale datasets. Here we discuss recent advances in the recognition of spurious inter-protein peptide pairs and in diminishing the FDR for these PPI-signaling cross-links, such as the use of chromatographic retention time prediction, in order to come to a more reliable reporting of PPIs.
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Affiliation(s)
- Luitzen de Jong
- Swammerdam Institute for Life Sciences, Mass Spectrometry of Biomolecules, University of Amsterdam, Science Park 904, 1098 HX Amsterdam, the Netherlands.
| | - Winfried Roseboom
- Swammerdam Institute for Life Sciences, Mass Spectrometry of Biomolecules, University of Amsterdam, Science Park 904, 1098 HX Amsterdam, the Netherlands
| | - Gertjan Kramer
- Swammerdam Institute for Life Sciences, Mass Spectrometry of Biomolecules, University of Amsterdam, Science Park 904, 1098 HX Amsterdam, the Netherlands
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21
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Shi D, Sheng A, Chi L. Glycosaminoglycan-Protein Interactions and Their Roles in Human Disease. Front Mol Biosci 2021; 8:639666. [PMID: 33768117 PMCID: PMC7985165 DOI: 10.3389/fmolb.2021.639666] [Citation(s) in RCA: 63] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2020] [Accepted: 01/27/2021] [Indexed: 12/14/2022] Open
Abstract
Glycosaminoglycans (GAGs) are a family of linear and negatively charged polysaccharides that exist ubiquitously on the human cell surface as well as in the extracellular matrix. GAGs interact with a wide range of proteins, including proteases, growth factors, cytokines, chemokines and adhesion molecules, enabling them to mediate many physiological processes, such as protein function, cellular adhesion and signaling. GAG-protein interactions participate in and intervene in a variety of human diseases, including cardiovascular disease, infectious disease, neurodegenerative diseases and tumors. The breakthrough in analytical tools and approaches during the last two decades has facilitated a greater understanding of the importance of GAG-protein interactions and their roles in human diseases. This review focuses on aspects of the molecular basis and mechanisms of GAG-protein interactions involved in human disease. The most recent advances in analytical tools, especially mass spectrometry-based GAG sequencing and binding motif characterization methods, are introduced. An update of selected families of GAG binding proteins is presented. Perspectives on development of novel therapeutics targeting specific GAG-protein interactions are also covered in this review.
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Affiliation(s)
- Deling Shi
- National Glycoengineering Research Center, Shandong University, Qingdao, China
| | - Anran Sheng
- National Glycoengineering Research Center, Shandong University, Qingdao, China
| | - Lianli Chi
- National Glycoengineering Research Center, Shandong University, Qingdao, China
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22
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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: 48] [Impact Index Per Article: 16.0] [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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