1
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Rincon Pabon JP, Akbar Z, Politis A. MSe Collision Energy Optimization for the Analysis of Membrane Proteins Using HDX-cIMS. JOURNAL OF THE AMERICAN SOCIETY FOR MASS SPECTROMETRY 2024; 35:1383-1389. [PMID: 38842540 PMCID: PMC11228973 DOI: 10.1021/jasms.4c00093] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/13/2024] [Revised: 05/09/2024] [Accepted: 06/05/2024] [Indexed: 06/07/2024]
Abstract
Hydrogen/deuterium exchange mass spectrometry (HDX-MS) has evolved as an essential technique in structural proteomics. The use of ion mobility separation (IMS) coupled to HDX-MS has increased the applicability of the technique to more complex systems and has been shown to improve data quality and robustness. The first step when running any HDX-MS workflow is to confirm the sequence and retention time of the peptides resulting from the proteolytic digestion of the nondeuterated protein. Here, we optimized the collision energy ramp of HDMSE experiments for membrane proteins using a Waters SELECT SERIES cIMS-QTOF system following an HDX workflow using Phosphorylase B, XylE transporter, and Smoothened receptor (SMO) as model systems. Although collision energy (CE) ramp 10-50 eV gave the highest amount of positive identified peptides when using Phosphorylase B, XylE, and SMO, results suggest optimal CE ramps are protein specific, and different ramps can produce a unique set of peptides. We recommend cIMS users use different CE ramps in their HDMSE experiments and pool the results to ensure maximum peptide identifications. The results show how selecting an appropriate CE ramp can change the sequence coverage of proteins ranging from 4 to 94%.
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Affiliation(s)
- Juan Pablo Rincon Pabon
- Faculty of Biology, Medicine and Health, Division of Molecular and Cellular Function, The University of Manchester, Manchester M13 9PT, U.K
- Manchester Institute of Biotechnology, University of Manchester, Princess Street, Manchester M1 7DN, U.K
| | - Zulaikha Akbar
- Faculty of Biology, Medicine and Health, Division of Molecular and Cellular Function, The University of Manchester, Manchester M13 9PT, U.K
- Manchester Institute of Biotechnology, University of Manchester, Princess Street, Manchester M1 7DN, U.K
| | - Argyris Politis
- Faculty of Biology, Medicine and Health, Division of Molecular and Cellular Function, The University of Manchester, Manchester M13 9PT, U.K
- Manchester Institute of Biotechnology, University of Manchester, Princess Street, Manchester M1 7DN, U.K
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2
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Salmas R, Borysik AJ. Deep Learning Enables Automatic Correction of Experimental HDX-MS Data with Applications in Protein Modeling. JOURNAL OF THE AMERICAN SOCIETY FOR MASS SPECTROMETRY 2024; 35:197-204. [PMID: 38262924 PMCID: PMC10853964 DOI: 10.1021/jasms.3c00285] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/08/2023] [Revised: 12/24/2023] [Accepted: 01/04/2024] [Indexed: 01/25/2024]
Abstract
Observed mass shifts associated with deuterium incorporation in hydrogen-deuterium exchange mass spectrometry (HDX-MS) frequently deviate from the initial signals due to back and forward exchange. In typical HDX-MS experiments, the impact of these disparities on data interpretation is generally low because relative and not absolute mass changes are investigated. However, for more advanced data processing including optimization, experimental error correction is imperative for accurate results. Here the potential for automatic HDX-MS data correction using models generated by deep neural networks is demonstrated. A multilayer perceptron (MLP) is used to learn a mapping between uncorrected HDX-MS data and data with mass shifts corrected for back and forward exchange. The model is rigorously tested at various levels including peptide level mass changes, residue level protection factors following optimization, and ability to correctly identify native protein folds using HDX-MS guided protein modeling. AI is shown to demonstrate considerable potential for amending HDX-MS data and improving fidelity across all levels. With access to big data, online tools may eventually be able to predict corrected mass shifts in HDX-MS profiles. This should improve throughput in workflows that require the reporting of real mass changes as well as allow retrospective correction of historic profiles to facilitate new discoveries with these data.
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Affiliation(s)
| | - Antoni J. Borysik
- Department of Chemistry, King’s
College London, Britannia House, London SE1 1DB, U.K.
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3
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Salmas R, Harris MJ, Borysik AJ. Mapping HDX-MS Data to Protein Conformations through Training Ensemble-Based Models. JOURNAL OF THE AMERICAN SOCIETY FOR MASS SPECTROMETRY 2023; 34:1989-1997. [PMID: 37550799 PMCID: PMC10485923 DOI: 10.1021/jasms.3c00145] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/18/2023] [Revised: 07/27/2023] [Accepted: 08/01/2023] [Indexed: 08/09/2023]
Abstract
An original approach that adopts machine learning inference to predict protein structural information using hydrogen-deuterium exchange mass spectrometry (HDX-MS) is described. The method exploits an in-house optimization program that increases the resolution of HDX-MS data from peptides to amino acids. A system is trained using Gradient Tree Boosting as a type of machine learning ensemble technique to assign a protein secondary structure. Using limited training data we generate a discriminative model that uses optimized HDX-MS data to predict protein secondary structure with an accuracy of 75%. This research could form the basis for new methods exploiting artificial intelligence to model protein conformations by HDX-MS.
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Affiliation(s)
| | | | - Antoni J. Borysik
- Department of Chemistry,
Britannia House, King’s College London, London SE1 1DB, U.K.
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4
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Tran MH, Schoeder CT, Schey KL, Meiler J. Computational Structure Prediction for Antibody-Antigen Complexes From Hydrogen-Deuterium Exchange Mass Spectrometry: Challenges and Outlook. Front Immunol 2022; 13:859964. [PMID: 35720345 PMCID: PMC9204306 DOI: 10.3389/fimmu.2022.859964] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2022] [Accepted: 04/22/2022] [Indexed: 11/21/2022] Open
Abstract
Although computational structure prediction has had great successes in recent years, it regularly fails to predict the interactions of large protein complexes with residue-level accuracy, or even the correct orientation of the protein partners. The performance of computational docking can be notably enhanced by incorporating experimental data from structural biology techniques. A rapid method to probe protein-protein interactions is hydrogen-deuterium exchange mass spectrometry (HDX-MS). HDX-MS has been increasingly used for epitope-mapping of antibodies (Abs) to their respective antigens (Ags) in the past few years. In this paper, we review the current state of HDX-MS in studying protein interactions, specifically Ab-Ag interactions, and how it has been used to inform computational structure prediction calculations. Particularly, we address the limitations of HDX-MS in epitope mapping and techniques and protocols applied to overcome these barriers. Furthermore, we explore computational methods that leverage HDX-MS to aid structure prediction, including the computational simulation of HDX-MS data and the combination of HDX-MS and protein docking. We point out challenges in interpreting and incorporating HDX-MS data into Ab-Ag complex docking and highlight the opportunities they provide to build towards a more optimized hybrid method, allowing for more reliable, high throughput epitope identification.
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Affiliation(s)
- Minh H. Tran
- Chemical and Physical Biology Program, Vanderbilt University, Nashville, TN, United States
- Center of Structural Biology, Vanderbilt University, Nashville, TN, United States
- Mass Spectrometry Research Center, Department of Biochemistry, Vanderbilt University, Nashville, TN, United States
| | - Clara T. Schoeder
- Center of Structural Biology, Vanderbilt University, Nashville, TN, United States
- Department of Chemistry, Vanderbilt University, Nashville, TN, United States
- Institute for Drug Discovery, University Leipzig Medical School, Leipzig, Germany
| | - Kevin L. Schey
- Mass Spectrometry Research Center, Department of Biochemistry, Vanderbilt University, Nashville, TN, United States
| | - Jens Meiler
- Center of Structural Biology, Vanderbilt University, Nashville, TN, United States
- Department of Chemistry, Vanderbilt University, Nashville, TN, United States
- Institute for Drug Discovery, University Leipzig Medical School, Leipzig, Germany
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5
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Devaurs D, Antunes DA, Borysik AJ. Computational Modeling of Molecular Structures Guided by Hydrogen-Exchange Data. JOURNAL OF THE AMERICAN SOCIETY FOR MASS SPECTROMETRY 2022; 33:215-237. [PMID: 35077179 DOI: 10.1021/jasms.1c00328] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Data produced by hydrogen-exchange monitoring experiments have been used in structural studies of molecules for several decades. Despite uncertainties about the structural determinants of hydrogen exchange itself, such data have successfully helped guide the structural modeling of challenging molecular systems, such as membrane proteins or large macromolecular complexes. As hydrogen-exchange monitoring provides information on the dynamics of molecules in solution, it can complement other experimental techniques in so-called integrative modeling approaches. However, hydrogen-exchange data have often only been used to qualitatively assess molecular structures produced by computational modeling tools. In this paper, we look beyond qualitative approaches and survey the various paradigms under which hydrogen-exchange data have been used to quantitatively guide the computational modeling of molecular structures. Although numerous prediction models have been proposed to link molecular structure and hydrogen exchange, none of them has been widely accepted by the structural biology community. Here, we present as many hydrogen-exchange prediction models as we could find in the literature, with the aim of providing the first exhaustive list of its kind. From purely structure-based models to so-called fractional-population models or knowledge-based models, the field is quite vast. We aspire for this paper to become a resource for practitioners to gain a broader perspective on the field and guide research toward the definition of better prediction models. This will eventually improve synergies between hydrogen-exchange monitoring and molecular modeling.
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Affiliation(s)
- Didier Devaurs
- MRC Institute of Genetics and Cancer, University of Edinburgh, Edinburgh EH4 2XU, U.K
| | - Dinler A Antunes
- Department of Biology and Biochemistry, University of Houston, Houston, Texas 77005, United States
| | - Antoni J Borysik
- Department of Chemistry, King's College London, London SE1 1DB, U.K
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6
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Salmas RE, Borysik AJ. Exploiting the Propagation of Constrained Variables for Enhanced HDX-MS Data Optimization. Anal Chem 2021; 93:16417-16424. [PMID: 34860510 DOI: 10.1021/acs.analchem.1c03082] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
Abstract
Nonlinear programming has found useful applications in protein biophysics to help understand the microscopic exchange kinetics of data obtained using hydrogen-deuterium exchange mass spectrometry (HDX-MS). Finding a microscopic kinetic solution for HDX-MS data provides a window into local protein stability and energetics allowing them to be quantified and understood. Optimization of HDX-MS data is a significant challenge, however, due to the requirement to solve a large number of variables simultaneously with exceptionally large variable bounds. Modeled rates are frequently uncertain with an explicate dependency on the initial guess values. In order to enhance the search for a minimum solution in HDX-MS optimization, the ability of selected constrained variables to propagate throughout the data is considered. We reveal that locally bound constrained optimization induces a global effect on all variables. The global response to local constraints is large and surprisingly long-range, but the outcome is unpredictable, unexpectedly decreasing the overall accuracy of certain data sets depending on the stringency of the constraints. Utilizing previously described in-house validation criteria based on covariance matrices, a method is described that is able to accurately determine whether constraints benefit or impair the optimization of HDX-MS data. From this, we establish a new two-stage method for our online optimizer HDXmodeller that can effectively leverage locally bound variables to enhance HDX-MS data modeling.
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Affiliation(s)
- Ramin Ekhteiari Salmas
- Department of Chemistry, Britannia House, King's College London, London SE1 1DB, United Kingdom
| | - Antoni James Borysik
- Department of Chemistry, Britannia House, King's College London, London SE1 1DB, United Kingdom
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7
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James EI, Murphree TA, Vorauer C, Engen JR, Guttman M. Advances in Hydrogen/Deuterium Exchange Mass Spectrometry and the Pursuit of Challenging Biological Systems. Chem Rev 2021; 122:7562-7623. [PMID: 34493042 PMCID: PMC9053315 DOI: 10.1021/acs.chemrev.1c00279] [Citation(s) in RCA: 154] [Impact Index Per Article: 38.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
![]()
Solution-phase hydrogen/deuterium
exchange (HDX) coupled to mass
spectrometry (MS) is a widespread tool for structural analysis across
academia and the biopharmaceutical industry. By monitoring the exchangeability
of backbone amide protons, HDX-MS can reveal information about higher-order
structure and dynamics throughout a protein, can track protein folding
pathways, map interaction sites, and assess conformational states
of protein samples. The combination of the versatility of the hydrogen/deuterium
exchange reaction with the sensitivity of mass spectrometry has enabled
the study of extremely challenging protein systems, some of which
cannot be suitably studied using other techniques. Improvements over
the past three decades have continually increased throughput, robustness,
and expanded the limits of what is feasible for HDX-MS investigations.
To provide an overview for researchers seeking to utilize and derive
the most from HDX-MS for protein structural analysis, we summarize
the fundamental principles, basic methodology, strengths and weaknesses,
and the established applications of HDX-MS while highlighting new
developments and applications.
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Affiliation(s)
- Ellie I James
- Department of Medicinal Chemistry, University of Washington, Seattle, Washington 98195, United States
| | - Taylor A Murphree
- Department of Medicinal Chemistry, University of Washington, Seattle, Washington 98195, United States
| | - Clint Vorauer
- Department of Medicinal Chemistry, University of Washington, Seattle, Washington 98195, United States
| | - John R Engen
- Department of Chemistry & Chemical Biology, Northeastern University, Boston, Massachusetts 02115, United States
| | - Miklos Guttman
- Department of Medicinal Chemistry, University of Washington, Seattle, Washington 98195, United States
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8
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Abstract
Quantification of hydrogen deuterium exchange (HDX) kinetics can provide information on the stability of individual amino acids in proteins by finding the degree to which the local backbone environment corresponds to that of a random coil. When characterized by mass spectrometry, extraction of HDX kinetics is not possible because different residue exchange rates become merged depending on the peptides that are formed during proteolytic digestion. We have recently developed an advanced programming tool called HDXmodeller, which enables the exchange rates of individual amino acids to be understood by optimization of low-resolution HDX-mass spectrometry (MS) data. HDXmodeller is also uniquely able to appraise each optimization and quantify the accuracy of modeled exchange rates ab initio using a novel autovalidation method based on a covariance matrix. Here, we address the noise-handling capabilities of HDXmodeller and demonstrate the effectiveness of the algorithm on self-inconsistent datasets. Reference intervals for experimental HDX-MS data are also derived, and this information is presented in an updated online workflow for HDXmodeller, allowing users to evaluate the consistency of their data. The development of a modified version of HDXmodeller is also discussed with enhanced noise-handling capability brought about through loss function optimization. Changes in optimizer accuracy with different loss functions are also demonstrated along with the effectiveness of HDXmodeller to select the most effective optimizer for different data using currently embedded autovalidation criteria.
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9
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HDXmodeller: an online webserver for high-resolution HDX-MS with auto-validation. Commun Biol 2021; 4:199. [PMID: 33589746 PMCID: PMC7884430 DOI: 10.1038/s42003-021-01709-x] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2020] [Accepted: 12/29/2020] [Indexed: 12/16/2022] Open
Abstract
The extent to which proteins are protected from hydrogen deuterium exchange (HDX) provides valuable insight into their folding, dynamics and interactions. Characterised by mass spectrometry (MS), HDX benefits from negligible mass restrictions and exceptional throughput and sensitivity but at the expense of resolution. Exchange mechanisms which naturally transpire for individual residues cannot be accurately located or understood because amino acids are characterised in differently sized groups depending on the extent of proteolytic digestion. Here we report HDXmodeller, the world's first online webserver for high-resolution HDX-MS. HDXmodeller accepts low-resolution HDX-MS input data and returns high-resolution exchange rates quantified for each residue. Crucially, HDXmodeller also returns a set of unique statistics that can correctly validate exchange rate models to an accuracy of 99%. Remarkably, these statistics are derived without any prior knowledge of the individual exchange rates and facilitate unparallel user confidence and the capacity to evaluate different data optimisation strategies.
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10
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Seffernick JT, Lindert S. Hybrid methods for combined experimental and computational determination of protein structure. J Chem Phys 2020; 153:240901. [PMID: 33380110 PMCID: PMC7773420 DOI: 10.1063/5.0026025] [Citation(s) in RCA: 33] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2020] [Accepted: 11/10/2020] [Indexed: 02/04/2023] Open
Abstract
Knowledge of protein structure is paramount to the understanding of biological function, developing new therapeutics, and making detailed mechanistic hypotheses. Therefore, methods to accurately elucidate three-dimensional structures of proteins are in high demand. While there are a few experimental techniques that can routinely provide high-resolution structures, such as x-ray crystallography, nuclear magnetic resonance (NMR), and cryo-EM, which have been developed to determine the structures of proteins, these techniques each have shortcomings and thus cannot be used in all cases. However, additionally, a large number of experimental techniques that provide some structural information, but not enough to assign atomic positions with high certainty have been developed. These methods offer sparse experimental data, which can also be noisy and inaccurate in some instances. In cases where it is not possible to determine the structure of a protein experimentally, computational structure prediction methods can be used as an alternative. Although computational methods can be performed without any experimental data in a large number of studies, inclusion of sparse experimental data into these prediction methods has yielded significant improvement. In this Perspective, we cover many of the successes of integrative modeling, computational modeling with experimental data, specifically for protein folding, protein-protein docking, and molecular dynamics simulations. We describe methods that incorporate sparse data from cryo-EM, NMR, mass spectrometry, electron paramagnetic resonance, small-angle x-ray scattering, Förster resonance energy transfer, and genetic sequence covariation. Finally, we highlight some of the major challenges in the field as well as possible future directions.
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Affiliation(s)
- Justin T. Seffernick
- Department of Chemistry and Biochemistry, Ohio State University, Columbus, Ohio 43210, USA
| | - Steffen Lindert
- Department of Chemistry and Biochemistry, Ohio State University, Columbus, Ohio 43210, USA
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11
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Engen JR, Botzanowski T, Peterle D, Georgescauld F, Wales TE. Developments in Hydrogen/Deuterium Exchange Mass Spectrometry. Anal Chem 2020; 93:567-582. [DOI: 10.1021/acs.analchem.0c04281] [Citation(s) in RCA: 34] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Affiliation(s)
- John R. Engen
- Department of Chemistry and Chemical Biology, Northeastern University, Boston, Massachusetts 02115, United States
| | - Thomas Botzanowski
- Department of Chemistry and Chemical Biology, Northeastern University, Boston, Massachusetts 02115, United States
| | - Daniele Peterle
- Department of Chemistry and Chemical Biology, Northeastern University, Boston, Massachusetts 02115, United States
| | - Florian Georgescauld
- Department of Chemistry and Chemical Biology, Northeastern University, Boston, Massachusetts 02115, United States
| | - Thomas E. Wales
- Department of Chemistry and Chemical Biology, Northeastern University, Boston, Massachusetts 02115, United States
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12
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Martens C, Politis A. A glimpse into the molecular mechanism of integral membrane proteins through hydrogen-deuterium exchange mass spectrometry. Protein Sci 2020; 29:1285-1301. [PMID: 32170968 PMCID: PMC7255514 DOI: 10.1002/pro.3853] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2020] [Revised: 03/11/2020] [Accepted: 03/11/2020] [Indexed: 01/07/2023]
Abstract
Integral membrane proteins (IMPs) control countless fundamental biological processes and constitute the majority of drug targets. For this reason, uncovering their molecular mechanism of action has long been an intense field of research. They are, however, notoriously difficult to work with, mainly due to their localization within the heterogeneous of environment of the biological membrane and the instability once extracted from the lipid bilayer. High‐resolution structures have unveiled many mechanistic aspects of IMPs but also revealed that the elucidation of static pictures has limitations. Hydrogen–deuterium exchange coupled to mass spectrometry (HDX‐MS) has recently emerged as a powerful biophysical tool for interrogating the conformational dynamics of proteins and their interactions with ligands. Its versatility has proven particularly useful to reveal mechanistic aspects of challenging classes of proteins such as IMPs. This review recapitulates the accomplishments of HDX‐MS as it has matured into an essential tool for membrane protein structural biologists.
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Affiliation(s)
- Chloe Martens
- Laboratory for the Structure and Function of Biological Membranes, Center for Structural Biology and Bioinformatics, Université Libre de Bruxelles, Brussels, Belgium
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13
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Dülfer J, Kadek A, Kopicki JD, Krichel B, Uetrecht C. Structural mass spectrometry goes viral. Adv Virus Res 2019; 105:189-238. [PMID: 31522705 DOI: 10.1016/bs.aivir.2019.07.003] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Over the last 20 years, mass spectrometry (MS), with its ability to analyze small sample amounts with high speed and sensitivity, has more and more entered the field of structural virology, aiming to investigate the structure and dynamics of viral proteins as close to their native environment as possible. The use of non-perturbing labels in hydrogen-deuterium exchange MS allows for the analysis of interactions between viral proteins and host cell factors as well as their dynamic responses to the environment. Cross-linking MS, on the other hand, can analyze interactions in viral protein complexes and identify virus-host interactions in cells. Native MS allows transferring viral proteins, complexes and capsids into the gas phase and has broken boundaries to overcome size limitations, so that now even the analysis of intact virions is possible. Different MS approaches not only inform about size, stability, interactions and dynamics of virus assemblies, but also bridge the gap to other biophysical techniques, providing valuable constraints for integrative structural modeling of viral complex assemblies that are often inaccessible by single technique approaches. In this review, recent advances are highlighted, clearly showing that structural MS approaches in virology are moving towards systems biology and ever more experiments are performed on cellular level.
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Affiliation(s)
- Jasmin Dülfer
- Heinrich Pette Institute, Leibniz Institute for Experimental Virology, Hamburg, Germany
| | - Alan Kadek
- Heinrich Pette Institute, Leibniz Institute for Experimental Virology, Hamburg, Germany; European XFEL GmbH, Schenefeld, Germany
| | - Janine-Denise Kopicki
- Heinrich Pette Institute, Leibniz Institute for Experimental Virology, Hamburg, Germany
| | - Boris Krichel
- Heinrich Pette Institute, Leibniz Institute for Experimental Virology, Hamburg, Germany
| | - Charlotte Uetrecht
- Heinrich Pette Institute, Leibniz Institute for Experimental Virology, Hamburg, Germany; European XFEL GmbH, Schenefeld, Germany.
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