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Hamuro Y. Interpretation of Hydrogen/Deuterium Exchange Mass Spectrometry. J Am Soc Mass Spectrom 2024; 35:819-828. [PMID: 38639434 PMCID: PMC11067899 DOI: 10.1021/jasms.4c00044] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/02/2024] [Revised: 03/29/2024] [Accepted: 04/02/2024] [Indexed: 04/20/2024]
Abstract
This paper sheds light on the meaning of hydrogen/deuterium exchange-mass spectrometry (HDX-MS) data. HDX-MS data provide not structural information but dynamic information on an analyte protein. First, the reaction mechanism of backbone amide HDX reaction is considered and the correlation between the parameters from an X-ray crystal structure and the protection factors of HDX reactions of cytochrome c is evaluated. The presence of H-bonds in a protein structure has a strong influence on HDX rates which represent protein dynamics, while the solvent accessibility only weakly affects the HDX rates. Second, the energy diagrams of the HDX reaction at each residue in the presence and absence of perturbation are described. Whereas the free energy change upon mutation can be directly measured by the HDX rates, the free energy change upon ligand binding may be complicated due to the presence of unbound analyte protein in the protein-ligand mixture. Third, the meanings of HDX and other biophysical techniques are explained using a hypothetical protein folding well. The shape of the protein folding well describes the protein dynamics and provides Boltzmann distribution of open and closed states which yield HDX protection factors, while a protein's crystal structure represents a snapshot near the bottom of the well. All biophysical data should be consistent yet provide different information because they monitor different parts of the same protein folding well.
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Kaur U, Kihn KC, Ke H, Kuo W, Gierasch LM, Hebert DN, Wintrode PL, Deredge D, Gershenson A. The conformational landscape of a serpin N-terminal subdomain facilitates folding and in-cell quality control. bioRxiv 2023:2023.04.24.537978. [PMID: 37163105 PMCID: PMC10168285 DOI: 10.1101/2023.04.24.537978] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/11/2023]
Abstract
Many multi-domain proteins including the serpin family of serine protease inhibitors contain non-sequential domains composed of regions that are far apart in sequence. Because proteins are translated vectorially from N- to C-terminus, such domains pose a particular challenge: how to balance the conformational lability necessary to form productive interactions between early and late translated regions while avoiding aggregation. This balance is mediated by the protein sequence properties and the interactions of the folding protein with the cellular quality control machinery. For serpins, particularly α 1 -antitrypsin (AAT), mutations often lead to polymer accumulation in cells and consequent disease suggesting that the lability/aggregation balance is especially precarious. Therefore, we investigated the properties of progressively longer AAT N-terminal fragments in solution and in cells. The N-terminal subdomain, residues 1-190 (AAT190), is monomeric in solution and efficiently degraded in cells. More β -rich fragments, 1-290 and 1-323, form small oligomers in solution, but are still efficiently degraded, and even the polymerization promoting Siiyama (S53F) mutation did not significantly affect fragment degradation. In vitro, the AAT190 region is among the last regions incorporated into the final structure. Hydrogen-deuterium exchange mass spectrometry and enhanced sampling molecular dynamics simulations show that AAT190 has a broad, dynamic conformational ensemble that helps protect one particularly aggregation prone β -strand from solvent. These AAT190 dynamics result in transient exposure of sequences that are buried in folded, full-length AAT, which may provide important recognition sites for the cellular quality control machinery and facilitate degradation and, under favorable conditions, reduce the likelihood of polymerization.
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Affiliation(s)
- Upneet Kaur
- Department of Biochemistry & Molecular Biology, University of Massachusetts, Amherst, MA 01003
| | - Kyle C. Kihn
- Department of Pharmaceutical Sciences, University of Maryland School of Pharmacy, Baltimore, MD 21201
| | - Haiping Ke
- Department of Biochemistry & Molecular Biology, University of Massachusetts, Amherst, MA 01003
| | - Weiwei Kuo
- Department of Biochemistry & Molecular Biology, University of Massachusetts, Amherst, MA 01003
| | - Lila M. Gierasch
- Department of Biochemistry & Molecular Biology, University of Massachusetts, Amherst, MA 01003
- Program in Molecular and Cellular Biology, University of Massachusetts, Amherst, MA 01003
- Department of Chemistry, University of Massachusetts, Amherst, MA 01003
| | - Daniel N. Hebert
- Department of Biochemistry & Molecular Biology, University of Massachusetts, Amherst, MA 01003
- Program in Molecular and Cellular Biology, University of Massachusetts, Amherst, MA 01003
| | - Patrick L. Wintrode
- Department of Pharmaceutical Sciences, University of Maryland School of Pharmacy, Baltimore, MD 21201
| | - Daniel Deredge
- Department of Pharmaceutical Sciences, University of Maryland School of Pharmacy, Baltimore, MD 21201
| | - Anne Gershenson
- Department of Biochemistry & Molecular Biology, University of Massachusetts, Amherst, MA 01003
- Program in Molecular and Cellular Biology, University of Massachusetts, Amherst, MA 01003
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Jia R, Bradshaw RT, Calvaresi V, Politis A. Integrating Hydrogen Deuterium Exchange-Mass Spectrometry with Molecular Simulations Enables Quantification of the Conformational Populations of the Sugar Transporter XylE. J Am Chem Soc 2023; 145:7768-7779. [PMID: 36976935 PMCID: PMC10103171 DOI: 10.1021/jacs.2c06148] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/30/2023]
Abstract
A yet unresolved challenge in structural biology is to quantify the conformational states of proteins underpinning function. This challenge is particularly acute for membrane proteins owing to the difficulties in stabilizing them for in vitro studies. To address this challenge, we present an integrative strategy that combines hydrogen deuterium exchange-mass spectrometry (HDX-MS) with ensemble modeling. We benchmark our strategy on wild-type and mutant conformers of XylE, a prototypical member of the ubiquitous Major Facilitator Superfamily (MFS) of transporters. Next, we apply our strategy to quantify conformational ensembles of XylE embedded in different lipid environments. Further application of our integrative strategy to substrate-bound and inhibitor-bound ensembles allowed us to unravel protein-ligand interactions contributing to the alternating access mechanism of secondary transport in atomistic detail. Overall, our study highlights the potential of integrative HDX-MS modeling to capture, accurately quantify, and subsequently visualize co-populated states of membrane proteins in association with mutations and diverse substrates and inhibitors.
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Affiliation(s)
- Ruyu Jia
- Department of Chemistry, King's College London, 7 Trinity Street, London SE1 1DB, U.K
| | - Richard T Bradshaw
- Department of Chemistry, King's College London, 7 Trinity Street, London SE1 1DB, U.K
| | - Valeria Calvaresi
- Department of Chemistry, King's College London, 7 Trinity Street, London SE1 1DB, U.K
| | - Argyris Politis
- Department of Chemistry, King's College London, 7 Trinity Street, London SE1 1DB, U.K
- Faculty of Biology, Medicine and Health, School of Biological Sciences, 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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Yadav R, Courouble VV, Dey SK, Harrison JJE, Timm J, Hopkins JB, Slack RL, Sarafianos SG, Ruiz FX, Griffin PR, Arnold E. Biochemical and structural insights into SARS-CoV-2 polyprotein processing by Mpro. Sci Adv 2022; 8:eadd2191. [PMID: 36490335 PMCID: PMC9733933 DOI: 10.1126/sciadv.add2191] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/27/2022] [Accepted: 11/02/2022] [Indexed: 06/17/2023]
Abstract
SARS-CoV-2, a human coronavirus, is the causative agent of the COVID-19 pandemic. Its genome is translated into two large polyproteins subsequently cleaved by viral papain-like protease and main protease (Mpro). Polyprotein processing is essential yet incompletely understood. We studied Mpro-mediated processing of the nsp7-11 polyprotein, whose mature products include cofactors of the viral replicase, and identified the order of cleavages. Integrative modeling based on mass spectrometry (including hydrogen-deuterium exchange and cross-linking) and x-ray scattering yielded a nsp7-11 structural ensemble, demonstrating shared secondary structural elements with individual nsps. The pattern of cross-links and HDX footprint of the C145A Mpro and nsp7-11 complex demonstrate preferential binding of the enzyme active site to the polyprotein junction sites and additional transient contacts to help orient the enzyme on its substrate for cleavage. Last, proteolysis assays were used to characterize the effect of inhibitors/binders on Mpro processing/inhibition using the nsp7-11 polyprotein as substrate.
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Affiliation(s)
- Ruchi Yadav
- Center for Advanced Biotechnology and Medicine (CABM), Rutgers University, Piscataway, NJ, USA
- Department of Chemistry and Chemical Biology, Rutgers University, Piscataway, NJ, USA
| | - Valentine V. Courouble
- Department of Molecular Medicine, The Scripps Research Institute, Jupiter, FL, USA
- Skaggs Graduate School of Chemical and Biological Sciences, The Scripps Research Institute, Jupiter, FL, USA
| | - Sanjay K. Dey
- Center for Advanced Biotechnology and Medicine (CABM), Rutgers University, Piscataway, NJ, USA
- Department of Chemistry and Chemical Biology, Rutgers University, Piscataway, NJ, USA
| | | | - Jennifer Timm
- Center for Advanced Biotechnology and Medicine (CABM), Rutgers University, Piscataway, NJ, USA
| | - Jesse B. Hopkins
- BioCAT, Department of Physics, Illinois Institute of Technology, Chicago, IL, USA
| | - Ryan L. Slack
- Division of Laboratory of Biochemical Pharmacology and Division of Infectious Diseases, Department of Pediatrics, Emory University School of Medicine, Atlanta, GA, USA
| | - Stefan G. Sarafianos
- Division of Laboratory of Biochemical Pharmacology and Division of Infectious Diseases, Department of Pediatrics, Emory University School of Medicine, Atlanta, GA, USA
- Children’s Healthcare of Atlanta, Atlanta, GA, USA
| | - Francesc X. Ruiz
- Center for Advanced Biotechnology and Medicine (CABM), Rutgers University, Piscataway, NJ, USA
- Department of Chemistry and Chemical Biology, Rutgers University, Piscataway, NJ, USA
| | - Patrick R. Griffin
- Department of Molecular Medicine, The Scripps Research Institute, Jupiter, FL, USA
- Skaggs Graduate School of Chemical and Biological Sciences, The Scripps Research Institute, Jupiter, FL, USA
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, Jupiter, FL, USA
- Department of Molecular Medicine, UF Scripps Biomedical Research, University of Florida, Jupiter, FL, USA
| | - Eddy Arnold
- Center for Advanced Biotechnology and Medicine (CABM), Rutgers University, Piscataway, NJ, USA
- Department of Chemistry and Chemical Biology, Rutgers University, Piscataway, NJ, USA
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Devaurs D, Antunes DA, Borysik AJ. Computational Modeling of Molecular Structures Guided by Hydrogen-Exchange Data. J Am Soc Mass Spectrom 2022; 33:215-237. [PMID: 35077179 DOI: 10.1021/jasms.1c00328] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [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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Komives EA. Achieving a realistic native protein ensemble by HDX-MS and computational modeling. Biophys J 2021; 120:5139-5140. [PMID: 34742401 DOI: 10.1016/j.bpj.2021.10.041] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2021] [Revised: 10/21/2021] [Accepted: 10/27/2021] [Indexed: 11/18/2022] Open
Affiliation(s)
- Elizabeth A Komives
- Department of Chemistry and Biochemistry, University of California, San Diego, California.
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Lee PS, Bradshaw RT, Marinelli F, Kihn K, Smith A, Wintrode PL, Deredge DJ, Faraldo-Gómez JD, Forrest LR. Interpreting Hydrogen-Deuterium Exchange Experiments with Molecular Simulations: Tutorials and Applications of the HDXer Ensemble Reweighting Software [Article v1.0]. Living J Comput Mol Sci 2021; 3:1521. [PMID: 36644498 PMCID: PMC9835200 DOI: 10.33011/livecoms.3.1.1521] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
Abstract
Hydrogen-deuterium exchange (HDX) is a comprehensive yet detailed probe of protein structure and dynamics and, coupled to mass spectrometry, has become a powerful tool for investigating an increasingly large array of systems. Computer simulations are often used to help rationalize experimental observations of exchange, but interpretations have frequently been limited to simple, subjective correlations between microscopic dynamical fluctuations and the observed macroscopic exchange behavior. With this in mind, we previously developed the HDX ensemble reweighting approach and associated software, HDXer, to aid the objective interpretation of HDX data using molecular simulations. HDXer has two main functions; first, to compute H-D exchange rates that describe each structure in a candidate ensemble of protein structures, for example from molecular simulations, and second, to objectively reweight the conformational populations present in a candidate ensemble to conform to experimental exchange data. In this article, we first describe the HDXer approach, theory, and implementation. We then guide users through a suite of tutorials that demonstrate the practical aspects of preparing experimental data, computing HDX levels from molecular simulations, and performing ensemble reweighting analyses. Finally we provide a practical discussion of the capabilities and limitations of the HDXer methods including recommendations for a user's own analyses. Overall, this article is intended to provide an up-to-date, pedagogical counterpart to the software, which is freely available at https://github.com/Lucy-Forrest-Lab/HDXer.
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Affiliation(s)
- Paul Suhwan Lee
- Computational Structural Biology Section, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, USA
| | - Richard T. Bradshaw
- Computational Structural Biology Section, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, USA,For correspondence: (RTB); (LRF)
| | - Fabrizio Marinelli
- Theoretical Molecular Biophysics Laboratory, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA
| | - Kyle Kihn
- Department of Pharmaceutical Sciences, School of Pharmacy, University of Maryland, Baltimore, MD, USA
| | - Ally Smith
- Department of Pharmaceutical Sciences, School of Pharmacy, University of Maryland, Baltimore, MD, USA
| | - Patrick L. Wintrode
- Department of Pharmaceutical Sciences, School of Pharmacy, University of Maryland, Baltimore, MD, USA
| | - Daniel J. Deredge
- Department of Pharmaceutical Sciences, School of Pharmacy, University of Maryland, Baltimore, MD, USA
| | - José D. Faraldo-Gómez
- Theoretical Molecular Biophysics Laboratory, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA
| | - Lucy R. Forrest
- Computational Structural Biology Section, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, USA,For correspondence: (RTB); (LRF)
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