1
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Tang D, Gueto-Tettay C, Hjortswang E, Ströbaek J, Ekström S, Happonen L, Malmström L, Malmström J. Multimodal Mass Spectrometry Identifies a Conserved Protective Epitope in S. pyogenes Streptolysin O. Anal Chem 2024; 96:9060-9068. [PMID: 38701337 PMCID: PMC11154737 DOI: 10.1021/acs.analchem.4c00596] [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: 01/30/2024] [Revised: 04/16/2024] [Accepted: 04/24/2024] [Indexed: 05/05/2024]
Abstract
An important element of antibody-guided vaccine design is the use of neutralizing or opsonic monoclonal antibodies to define protective epitopes in their native three-dimensional conformation. Here, we demonstrate a multimodal mass spectrometry-based strategy for in-depth characterization of antigen-antibody complexes to enable the identification of protective epitopes using the cytolytic exotoxin Streptolysin O (SLO) from Streptococcus pyogenes as a showcase. We first discovered a monoclonal antibody with an undisclosed sequence capable of neutralizing SLO-mediated cytolysis. The amino acid sequence of both the antibody light and the heavy chain was determined using mass-spectrometry-based de novo sequencing, followed by chemical cross-linking mass spectrometry to generate distance constraints between the antibody fragment antigen-binding region and SLO. Subsequent integrative computational modeling revealed a discontinuous epitope located in domain 3 of SLO that was experimentally validated by hydrogen-deuterium exchange mass spectrometry and reverse engineering of the targeted epitope. The results show that the antibody inhibits SLO-mediated cytolysis by binding to a discontinuous epitope in domain 3, likely preventing oligomerization and subsequent secondary structure transitions critical for pore-formation. The epitope is highly conserved across >98% of the characterized S. pyogenes isolates, making it an attractive target for antibody-based therapy and vaccine design against severe streptococcal infections.
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Affiliation(s)
- Di Tang
- Division
of Infection Medicine, Department of Clinical Sciences, Faculty of
Medicine, Lund University, Klinikgatan 32, 222 42 Lund, Sweden
| | - Carlos Gueto-Tettay
- Division
of Infection Medicine, Department of Clinical Sciences, Faculty of
Medicine, Lund University, Klinikgatan 32, 222 42 Lund, Sweden
| | - Elisabeth Hjortswang
- Division
of Infection Medicine, Department of Clinical Sciences, Faculty of
Medicine, Lund University, Klinikgatan 32, 222 42 Lund, Sweden
| | - Joel Ströbaek
- Division
of Infection Medicine, Department of Clinical Sciences, Faculty of
Medicine, Lund University, Klinikgatan 32, 222 42 Lund, Sweden
| | - Simon Ekström
- SciLifeLab,
Integrated Structural Biology Platform, Structural Proteomics Unit
Sweden, Lund University, Klinikgatan 32, 222
42 Lund, Sweden
| | - Lotta Happonen
- Division
of Infection Medicine, Department of Clinical Sciences, Faculty of
Medicine, Lund University, Klinikgatan 32, 222 42 Lund, Sweden
| | - Lars Malmström
- Division
of Infection Medicine, Department of Clinical Sciences, Faculty of
Medicine, Lund University, Klinikgatan 32, 222 42 Lund, Sweden
| | - Johan Malmström
- Division
of Infection Medicine, Department of Clinical Sciences, Faculty of
Medicine, Lund University, Klinikgatan 32, 222 42 Lund, Sweden
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2
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Gil Zuluaga FH, D’Arminio N, Bardozzo F, Tagliaferri R, Marabotti A. An automated pipeline integrating AlphaFold 2 and MODELLER for protein structure prediction. Comput Struct Biotechnol J 2023; 21:5620-5629. [PMID: 38047234 PMCID: PMC10690423 DOI: 10.1016/j.csbj.2023.10.056] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2023] [Revised: 10/31/2023] [Accepted: 10/31/2023] [Indexed: 12/05/2023] Open
Abstract
The ability to predict a protein's three-dimensional conformation represents a crucial starting point for investigating evolutionary connections with other members of the corresponding protein family, examining interactions with other proteins, and potentially utilizing this knowledge for the purpose of rational drug design. In this work, we evaluated the feasibility of improving AlphaFold2's three-dimensional protein predictions by developing a novel pipeline (AlphaMod) that incorporates AlphaFold2 with MODELLER, a template-based modeling program. Additionally, our tool can drive a comprehensive quality assessment of the tertiary protein structure by incorporating and comparing a set of different quality assessment tools. The outcomes of selected tools are combined into a composite score (BORDASCORE) that exhibits a meaningful correlation with GDT_TS and facilitates the selection of optimal models in the absence of a reference structure. To validate AlphaMod's results, we conducted evaluations using two distinct datasets summing up to 72 targets, previously used to independently assess AlphaFold2's performance. The generated models underwent evaluation through two methods: i) averaging the GDT_TS scores across all produced structures for a single target sequence, and ii) a pairwise comparison of the best structures generated by AlphaFold2 and AlphaMod. The latter, within the unsupervised setups, shows a rising accuracy of approximately 34% over AlphaFold2. While, when considering the supervised setup, AlphaMod surpasses AlphaFold2 in 18% of the instances. Finally, there is an 11% correspondence in outcomes between the diverse methodologies. Consequently, AlphaMod's best-predicted tertiary structures in several cases exhibited a significant improvement in the accuracy of the predictions with respect to the best models obtained by AlphaFold2. This pipeline paves the way for the integration of additional data and AI-based algorithms to further improve the reliability of the predictions.
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Affiliation(s)
- Fabio Hernan Gil Zuluaga
- Department of Management & Innovation Systems, University of Salerno, Via Giovanni Paolo II, 132, 84084 Fisciano, SA, Italy
| | - Nancy D’Arminio
- Department of Chemistry and Biology “A. Zambelli”, University of Salerno, Via Giovanni Paolo II, 132, 84084 Fisciano, SA, Italy
| | - Francesco Bardozzo
- Department of Management & Innovation Systems, University of Salerno, Via Giovanni Paolo II, 132, 84084 Fisciano, SA, Italy
| | - Roberto Tagliaferri
- Department of Management & Innovation Systems, University of Salerno, Via Giovanni Paolo II, 132, 84084 Fisciano, SA, Italy
| | - Anna Marabotti
- Department of Chemistry and Biology “A. Zambelli”, University of Salerno, Via Giovanni Paolo II, 132, 84084 Fisciano, SA, Italy
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3
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Boulos I, Jabbour J, Khoury S, Mikhael N, Tishkova V, Candoni N, Ghadieh HE, Veesler S, Bassim Y, Azar S, Harb F. Exploring the World of Membrane Proteins: Techniques and Methods for Understanding Structure, Function, and Dynamics. Molecules 2023; 28:7176. [PMID: 37894653 PMCID: PMC10608922 DOI: 10.3390/molecules28207176] [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: 07/25/2023] [Revised: 09/13/2023] [Accepted: 10/04/2023] [Indexed: 10/29/2023] Open
Abstract
In eukaryotic cells, membrane proteins play a crucial role. They fall into three categories: intrinsic proteins, extrinsic proteins, and proteins that are essential to the human genome (30% of which is devoted to encoding them). Hydrophobic interactions inside the membrane serve to stabilize integral proteins, which span the lipid bilayer. This review investigates a number of computational and experimental methods used to study membrane proteins. It encompasses a variety of technologies, including electrophoresis, X-ray crystallography, cryogenic electron microscopy (cryo-EM), nuclear magnetic resonance spectroscopy (NMR), biophysical methods, computational methods, and artificial intelligence. The link between structure and function of membrane proteins has been better understood thanks to these approaches, which also hold great promise for future study in the field. The significance of fusing artificial intelligence with experimental data to improve our comprehension of membrane protein biology is also covered in this paper. This effort aims to shed light on the complexity of membrane protein biology by investigating a variety of experimental and computational methods. Overall, the goal of this review is to emphasize how crucial it is to understand the functions of membrane proteins in eukaryotic cells. It gives a general review of the numerous methods used to look into these crucial elements and highlights the demand for multidisciplinary approaches to advance our understanding.
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Affiliation(s)
- Imad Boulos
- Faculty of Medicine and Medical Sciences, University of Balamand, Tripoli P.O. Box 100, Lebanon; (I.B.); (J.J.); (S.K.); (N.M.); (H.E.G.); (Y.B.); (S.A.)
| | - Joy Jabbour
- Faculty of Medicine and Medical Sciences, University of Balamand, Tripoli P.O. Box 100, Lebanon; (I.B.); (J.J.); (S.K.); (N.M.); (H.E.G.); (Y.B.); (S.A.)
| | - Serena Khoury
- Faculty of Medicine and Medical Sciences, University of Balamand, Tripoli P.O. Box 100, Lebanon; (I.B.); (J.J.); (S.K.); (N.M.); (H.E.G.); (Y.B.); (S.A.)
| | - Nehme Mikhael
- Faculty of Medicine and Medical Sciences, University of Balamand, Tripoli P.O. Box 100, Lebanon; (I.B.); (J.J.); (S.K.); (N.M.); (H.E.G.); (Y.B.); (S.A.)
| | - Victoria Tishkova
- CNRS, CINaM (Centre Interdisciplinaire de Nanosciences de Marseille), Campus de Luminy, Case 913, Aix-Marseille University, CEDEX 09, F-13288 Marseille, France; (V.T.); (N.C.); (S.V.)
| | - Nadine Candoni
- CNRS, CINaM (Centre Interdisciplinaire de Nanosciences de Marseille), Campus de Luminy, Case 913, Aix-Marseille University, CEDEX 09, F-13288 Marseille, France; (V.T.); (N.C.); (S.V.)
| | - Hilda E. Ghadieh
- Faculty of Medicine and Medical Sciences, University of Balamand, Tripoli P.O. Box 100, Lebanon; (I.B.); (J.J.); (S.K.); (N.M.); (H.E.G.); (Y.B.); (S.A.)
| | - Stéphane Veesler
- CNRS, CINaM (Centre Interdisciplinaire de Nanosciences de Marseille), Campus de Luminy, Case 913, Aix-Marseille University, CEDEX 09, F-13288 Marseille, France; (V.T.); (N.C.); (S.V.)
| | - Youssef Bassim
- Faculty of Medicine and Medical Sciences, University of Balamand, Tripoli P.O. Box 100, Lebanon; (I.B.); (J.J.); (S.K.); (N.M.); (H.E.G.); (Y.B.); (S.A.)
| | - Sami Azar
- Faculty of Medicine and Medical Sciences, University of Balamand, Tripoli P.O. Box 100, Lebanon; (I.B.); (J.J.); (S.K.); (N.M.); (H.E.G.); (Y.B.); (S.A.)
| | - Frédéric Harb
- Faculty of Medicine and Medical Sciences, University of Balamand, Tripoli P.O. Box 100, Lebanon; (I.B.); (J.J.); (S.K.); (N.M.); (H.E.G.); (Y.B.); (S.A.)
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4
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Kosenko M, Onkhonova G, Susloparov I, Ryzhikov A. SARS-CoV-2 proteins structural studies using synchrotron radiation. Biophys Rev 2023; 15:1185-1194. [PMID: 37974992 PMCID: PMC10643813 DOI: 10.1007/s12551-023-01153-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2023] [Accepted: 09/20/2023] [Indexed: 11/19/2023] Open
Abstract
In the process of the development of structural biology, both the size and the complexity of the determined macromolecular structures have grown significantly. As a result, the range of application areas for the results of structural studies of biological macromolecules has expanded. Significant progress in the development of structural biology methods has been largely achieved through the use of synchrotron radiation. Modern sources of synchrotron radiation allow to conduct high-performance structural studies with high temporal and spatial resolution. Thus, modern techniques make it possible to obtain not only static structures, but also to study dynamic processes, which play a key role in understanding biological mechanisms. One of the key directions in the development of structural research is the drug design based on the structures of biomolecules. Synchrotron radiation offers insights into the three-dimensional time-resolved structure of individual viral proteins and their complexes at atomic resolution. The rapid and accurate determination of protein structures is crucial for understanding viral pathogenicity and designing targeted therapeutics. Through the application of experimental techniques, including X-ray crystallography and small-angle X-ray scattering (SAXS), it is possible to elucidate the structural details of SARS-CoV-2 virion containing 4 structural, 16 nonstructural proteins (nsp), and several accessory proteins. The most studied potential targets for vaccines and drugs are the structural spike (S) protein, which is responsible for entering the host cell, as well as nonstructural proteins essential for replication and transcription, such as main protease (Mpro), papain-like protease (PLpro), and RNA-dependent RNA polymerase (RdRp). This article provides a brief overview of structural analysis techniques, with focus on synchrotron radiation-based methods applied to the analysis of SARS-CoV-2 proteins.
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Affiliation(s)
- Maksim Kosenko
- Federal Budgetary Research Institution State Research Center of Virology and Biotechnology “Vector” Rospotrebnadzor, Koltsovo, 630559 Russia
| | - Galina Onkhonova
- Federal Budgetary Research Institution State Research Center of Virology and Biotechnology “Vector” Rospotrebnadzor, Koltsovo, 630559 Russia
| | - Ivan Susloparov
- Federal Budgetary Research Institution State Research Center of Virology and Biotechnology “Vector” Rospotrebnadzor, Koltsovo, 630559 Russia
| | - Alexander Ryzhikov
- Federal Budgetary Research Institution State Research Center of Virology and Biotechnology “Vector” Rospotrebnadzor, Koltsovo, 630559 Russia
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5
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Turzo SMBA, Seffernick JT, Lyskov S, Lindert S. Predicting ion mobility collision cross sections using projection approximation with ROSIE-PARCS webserver. Brief Bioinform 2023; 24:bbad308. [PMID: 37609950 PMCID: PMC10516336 DOI: 10.1093/bib/bbad308] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2023] [Revised: 07/03/2023] [Accepted: 08/08/2023] [Indexed: 08/24/2023] Open
Abstract
Ion mobility coupled to mass spectrometry informs on the shape and size of protein structures in the form of a collision cross section (CCSIM). Although there are several computational methods for predicting CCSIM based on protein structures, including our previously developed projection approximation using rough circular shapes (PARCS), the process usually requires prior experience with the command-line interface. To overcome this challenge, here we present a web application on the Rosetta Online Server that Includes Everyone (ROSIE) webserver to predict CCSIM from protein structure using projection approximation with PARCS. In this web interface, the user is only required to provide one or more PDB files as input. Results from our case studies suggest that CCSIM predictions (with ROSIE-PARCS) are highly accurate with an average error of 6.12%. Furthermore, the absolute difference between CCSIM and CCSPARCS can help in distinguishing accurate from inaccurate AlphaFold2 protein structure predictions. ROSIE-PARCS is designed with a user-friendly interface, is available publicly and is free to use. The ROSIE-PARCS web interface is supported by all major web browsers and can be accessed via this link (https://rosie.graylab.jhu.edu).
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Affiliation(s)
- S M Bargeen Alam Turzo
- Department of Chemistry and Biochemistry and Resource for Native Mass Spectrometry Guided Structural Biology, Ohio State University, Columbus, OH 43210, USA
| | - Justin T Seffernick
- Department of Chemistry and Biochemistry and Resource for Native Mass Spectrometry Guided Structural Biology, Ohio State University, Columbus, OH 43210, USA
| | - Sergey Lyskov
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Steffen Lindert
- Department of Chemistry and Biochemistry and Resource for Native Mass Spectrometry Guided Structural Biology, Ohio State University, Columbus, OH 43210, USA
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6
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Computational insight into stability-enhanced systems of anthocyanin with protein/peptide. FOOD CHEMISTRY. MOLECULAR SCIENCES 2023; 6:100168. [PMID: 36923156 PMCID: PMC10009195 DOI: 10.1016/j.fochms.2023.100168] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/12/2022] [Revised: 12/24/2022] [Accepted: 02/18/2023] [Indexed: 02/24/2023]
Abstract
Anthocyanins, which belong to the flavonoid group, are commonly found in the organs of plants native to South and Central America. However, these pigments are unstable under conditions of varying pH, heat, etc., which limits their potential applications. One method for preserving the stability of anthocyanins is through encapsulation using proteins or peptides. Nevertheless, the complex and diverse structure of these molecules, as well as the limitation of experimental technologies, have hindered a comprehensive understanding of the encapsulation processes and the mechanisms by which stability is enhanced. To address these challenges, computational methods, such as molecular docking and molecular dynamics simulation have been used to study the binding affinity and dynamics of interactions between proteins/peptides and anthocyanins. This review summarizes the mechanisms of interaction between these systems, based on computational approaches, and highlights the role of proteins and peptides in the stability enhancement of anthocyanins. It also discusses the current limitations of these methods and suggests possible solutions.
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7
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Conesa P, Fonseca YC, Jiménez de la Morena J, Sharov G, de la Rosa-Trevín JM, Cuervo A, García Mena A, Rodríguez de Francisco B, del Hoyo D, Herreros D, Marchan D, Strelak D, Fernández-Giménez E, Ramírez-Aportela E, de Isidro-Gómez FP, Sánchez I, Krieger J, Vilas JL, del Cano L, Gragera M, Iceta M, Martínez M, Losana P, Melero R, Marabini R, Carazo JM, Sorzano COS. Scipion3: A workflow engine for cryo-electron microscopy image processing and structural biology. BIOLOGICAL IMAGING 2023; 3:e13. [PMID: 38510163 PMCID: PMC10951921 DOI: 10.1017/s2633903x23000132] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/10/2023] [Revised: 05/29/2023] [Accepted: 06/15/2023] [Indexed: 03/22/2024]
Abstract
Image-processing pipelines require the design of complex workflows combining many different steps that bring the raw acquired data to a final result with biological meaning. In the image-processing domain of cryo-electron microscopy single-particle analysis (cryo-EM SPA), hundreds of steps must be performed to obtain the three-dimensional structure of a biological macromolecule by integrating data spread over thousands of micrographs containing millions of copies of allegedly the same macromolecule. The execution of such complicated workflows demands a specific tool to keep track of all these steps performed. Additionally, due to the extremely low signal-to-noise ratio (SNR), the estimation of any image parameter is heavily affected by noise resulting in a significant fraction of incorrect estimates. Although low SNR and processing millions of images by hundreds of sequential steps requiring substantial computational resources are specific to cryo-EM, these characteristics may be shared by other biological imaging domains. Here, we present Scipion, a Python generic open-source workflow engine specifically adapted for image processing. Its main characteristics are: (a) interoperability, (b) smart object model, (c) gluing operations, (d) comparison operations, (e) wide set of domain-specific operations, (f) execution in streaming, (g) smooth integration in high-performance computing environments, (h) execution with and without graphical capabilities, (i) flexible visualization, (j) user authentication and private access to private data, (k) scripting capabilities, (l) high performance, (m) traceability, (n) reproducibility, (o) self-reporting, (p) reusability, (q) extensibility, (r) software updates, and (s) non-restrictive software licensing.
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Affiliation(s)
- Pablo Conesa
- National Center of Biotechnology (CNB-CSIC), Madrid, Spain
| | | | | | - Grigory Sharov
- Structural Studies Division, MRC Laboratory of Molecular Biology, Cambridge, United Kingdom
| | | | - Ana Cuervo
- National Center of Biotechnology (CNB-CSIC), Madrid, Spain
| | | | | | | | - David Herreros
- National Center of Biotechnology (CNB-CSIC), Madrid, Spain
| | - Daniel Marchan
- National Center of Biotechnology (CNB-CSIC), Madrid, Spain
| | - David Strelak
- National Center of Biotechnology (CNB-CSIC), Madrid, Spain
- Masaryk University, Brno, Czech Republic
| | | | | | | | - Irene Sánchez
- National Center of Biotechnology (CNB-CSIC), Madrid, Spain
| | - James Krieger
- National Center of Biotechnology (CNB-CSIC), Madrid, Spain
| | | | - Laura del Cano
- National Center of Biotechnology (CNB-CSIC), Madrid, Spain
| | - Marcos Gragera
- National Center of Biotechnology (CNB-CSIC), Madrid, Spain
| | - Mikel Iceta
- National Center of Biotechnology (CNB-CSIC), Madrid, Spain
| | - Marta Martínez
- National Center of Biotechnology (CNB-CSIC), Madrid, Spain
| | | | - Roberto Melero
- National Center of Biotechnology (CNB-CSIC), Madrid, Spain
| | - Roberto Marabini
- National Center of Biotechnology (CNB-CSIC), Madrid, Spain
- Superior Polytechnic School, Autonomous University of Madrid, Madrid, Spain
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8
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Choudhary P, Anyango S, Berrisford J, Tolchard J, Varadi M, Velankar S. Unified access to up-to-date residue-level annotations from UniProtKB and other biological databases for PDB data. Sci Data 2023; 10:204. [PMID: 37045837 PMCID: PMC10097656 DOI: 10.1038/s41597-023-02101-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2022] [Accepted: 03/23/2023] [Indexed: 04/14/2023] Open
Abstract
More than 61,000 proteins have up-to-date correspondence between their amino acid sequence (UniProtKB) and their 3D structures (PDB), enabled by the Structure Integration with Function, Taxonomy and Sequences (SIFTS) resource. SIFTS incorporates residue-level annotations from many other biological resources. SIFTS data is available in various formats like XML, CSV and TSV format or also accessible via the PDBe REST API but always maintained separately from the structure data (PDBx/mmCIF file) in the PDB archive. Here, we extended the wwPDB PDBx/mmCIF data dictionary with additional categories to accommodate SIFTS data and added the UniProtKB, Pfam, SCOP2, and CATH residue-level annotations directly into the PDBx/mmCIF files from the PDB archive. With the integrated UniProtKB annotations, these files now provide consistent numbering of residues in different PDB entries allowing easy comparison of structure models. The extended dictionary yields a more consistent, standardised metadata description without altering the core PDB information. This development enables up-to-date cross-reference information at the residue level resulting in better data interoperability, supporting improved data analysis and visualisation.
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Grants
- BB/V004247/1, PI:Sameer Velankar RCUK | Biotechnology and Biological Sciences Research Council (BBSRC)
- BB/V004247/1, PI:Sameer Velankar RCUK | Biotechnology and Biological Sciences Research Council (BBSRC)
- BB/V004247/1, PI:Sameer Velankar RCUK | Biotechnology and Biological Sciences Research Council (BBSRC)
- BB/V004247/1, PI:Sameer Velankar RCUK | Biotechnology and Biological Sciences Research Council (BBSRC)
- BB/V004247/1, PI:Sameer Velankar RCUK | Biotechnology and Biological Sciences Research Council (BBSRC)
- BB/V004247/1, PI:Sameer Velankar RCUK | Biotechnology and Biological Sciences Research Council (BBSRC)
- DBI-2019297, PI: S.K. Burley National Science Foundation (NSF)
- DBI-2019297, PI: S.K. Burley National Science Foundation (NSF)
- DBI-2019297, PI: S.K. Burley) National Science Foundation (NSF)
- DBI-2019297, PI: S.K. Burley National Science Foundation (NSF)
- DBI-2019297, PI: S.K. Burley National Science Foundation (NSF)
- DBI-2019297, PI: S.K. Burley NSF | National Science Board (NSB)
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Affiliation(s)
- Preeti Choudhary
- Protein Data Bank in Europe, European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SD, UK.
| | - Stephen Anyango
- Protein Data Bank in Europe, European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SD, UK
| | - John Berrisford
- Protein Data Bank in Europe, European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SD, UK
- AstraZeneca, Biomedical Campus, 1 Francis Crick Ave, Trumpington, Cambridge, CB2 0AA, UK
| | - James Tolchard
- Protein Data Bank in Europe, European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SD, UK
- Claude Bernard University, Villeurbanne, Lyon, 69100, France
| | - Mihaly Varadi
- Protein Data Bank in Europe, European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SD, UK
| | - Sameer Velankar
- Protein Data Bank in Europe, European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SD, UK
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9
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Tolkatchev D, Smith GE, Kostyukova AS. Nuclear Magnetic Resonance-Guided Structural Analysis of Moderate-Affinity Protein Complexes with Intrinsically Disordered Polypeptides. Methods Mol Biol 2023; 2652:405-437. [PMID: 37093489 PMCID: PMC11273159 DOI: 10.1007/978-1-0716-3147-8_23] [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] [Indexed: 04/25/2023]
Abstract
Binding affinity of an individual binding site of an intrinsically disordered protein for its folded partner may be moderate. In such cases, a straightforward determination of the structure of the binding interface is difficult. We offer a hybrid protocol combining NMR chemical shift information, NMR spectral data on amino acid residue sequence substitution effects, residual dipolar coupling, and molecular dynamics simulation that allowed us to determine the structure of a complex between the intrinsically disordered tropomyosin-binding site of leiomodin and a coiled-coil peptide modeling the N-terminal fragment of tropomyosin. The protocol can be used for other moderate-affinity complexes composed of an intrinsically disordered peptide bound to a structured protein partner.
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Affiliation(s)
- Dmitri Tolkatchev
- Voiland School of Chemical Engineering and Bioengineering, Washington State University, Pullman, WA, USA.
| | - Garry E Smith
- Voiland School of Chemical Engineering and Bioengineering, Washington State University, Pullman, WA, USA
| | - Alla S Kostyukova
- Voiland School of Chemical Engineering and Bioengineering, Washington State University, Pullman, WA, USA
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10
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Drake ZC, Seffernick JT, Lindert S. Protein complex prediction using Rosetta, AlphaFold, and mass spectrometry covalent labeling. Nat Commun 2022; 13:7846. [PMID: 36543826 PMCID: PMC9772387 DOI: 10.1038/s41467-022-35593-8] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2022] [Accepted: 12/09/2022] [Indexed: 12/24/2022] Open
Abstract
Covalent labeling (CL) in combination with mass spectrometry can be used as an analytical tool to study and determine structural properties of protein-protein complexes. However, data from these experiments is sparse and does not unambiguously elucidate protein structure. Thus, computational algorithms are needed to deduce structure from the CL data. In this work, we present a hybrid method that combines models of protein complex subunits generated with AlphaFold with differential CL data via a CL-guided protein-protein docking in Rosetta. In a benchmark set, the RMSD (root-mean-square deviation) of the best-scoring models was below 3.6 Å for 5/5 complexes with inclusion of CL data, whereas the same quality was only achieved for 1/5 complexes without CL data. This study suggests that our integrated approach can successfully use data obtained from CL experiments to distinguish between nativelike and non-nativelike models.
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Affiliation(s)
- Zachary C Drake
- Department of Chemistry and Biochemistry, Ohio State University, Columbus, OH, 43210, US
| | - Justin T Seffernick
- Department of Chemistry and Biochemistry, Ohio State University, Columbus, OH, 43210, US
| | - Steffen Lindert
- Department of Chemistry and Biochemistry, Ohio State University, Columbus, OH, 43210, US.
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11
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Lenard AJ, Mulder FAA, Madl T. Solvent paramagnetic relaxation enhancement as a versatile method for studying structure and dynamics of biomolecular systems. PROGRESS IN NUCLEAR MAGNETIC RESONANCE SPECTROSCOPY 2022; 132-133:113-139. [PMID: 36496256 DOI: 10.1016/j.pnmrs.2022.09.001] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/12/2022] [Revised: 09/14/2022] [Accepted: 09/15/2022] [Indexed: 06/17/2023]
Abstract
Solvent paramagnetic relaxation enhancement (sPRE) is a versatile nuclear magnetic resonance (NMR)-based method that allows characterization of the structure and dynamics of biomolecular systems through providing quantitative experimental information on solvent accessibility of NMR-active nuclei. Addition of soluble paramagnetic probes to the solution of a biomolecule leads to paramagnetic relaxation enhancement in a concentration-dependent manner. Here we review recent progress in the sPRE-based characterization of structural and dynamic properties of biomolecules and their complexes, and aim to deliver a comprehensive illustration of a growing number of applications of the method to various biological systems. We discuss the physical principles of sPRE measurements and provide an overview of available co-solute paramagnetic probes. We then explore how sPRE, in combination with complementary biophysical techniques, can further advance biomolecular structure determination, identification of interaction surfaces within protein complexes, and probing of conformational changes and low-population transient states, as well as deliver insights into weak, nonspecific, and transient interactions between proteins and co-solutes. In addition, we present examples of how the incorporation of solvent paramagnetic probes can improve the sensitivity of NMR experiments and discuss the prospects of applying sPRE to NMR metabolomics, drug discovery, and the study of intrinsically disordered proteins.
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Affiliation(s)
- Aneta J Lenard
- Gottfried Schatz Research Center for Cell Signaling, Metabolism and Ageing, Molecular Biology and Biochemistry, Research Unit Integrative Structural Biology, Medical University of Graz, 8010 Graz, Austria.
| | - Frans A A Mulder
- Interdisciplinary Nanoscience Center and Department of Chemistry, University of Aarhus, DK-8000 Aarhus, Denmark; Institute of Biochemistry, Johannes Kepler Universität Linz, 4040 Linz, Austria.
| | - Tobias Madl
- Gottfried Schatz Research Center for Cell Signaling, Metabolism and Ageing, Molecular Biology and Biochemistry, Research Unit Integrative Structural Biology, Medical University of Graz, 8010 Graz, Austria; BioTechMed-Graz, 8010 Graz, Austria.
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12
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Vymětal J, Vondrášek J. Iterative Landmark-Based Umbrella Sampling (ILBUS) Protocol for Sampling of Conformational Space of Biomolecules. J Chem Inf Model 2022; 62:4783-4798. [PMID: 36122323 DOI: 10.1021/acs.jcim.2c00370] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Computer simulations of biomolecules such as molecular dynamics often suffer from insufficient sampling. Due to limited computational resources, insufficient sampling prevents obtaining proper equilibrium distributions of observed properties. To deal with this problem, we proposed a simulation protocol for efficient resampling of collected off-equilibrium trajectories. These trajectories are utilized for the initial mapping of the conformational space, which is later properly resampled by the introduced Iterative Landmark-Based Umbrella Sampling (ILBUS) method. Reconstruction of static equilibrium properties is achieved by the multistate Bennett acceptance ratio (MBAR) method, which enables efficient use of simulated data. The ILBUS protocol is geometry-based and does not demand any additional collective variable or a dimensional-reduction technique. The only requirement is a set of suitably spaced reference conformations, which serve as landmarks in the mapped conformational space. Additionally, the ILBUS protocol encompasses an iterative process that optimizes the force constant used in the umbrella sampling simulation. Such tuning is an inherent feature of the protocol and does not need to be performed by the user in advance. Furthermore, even the simulations with suboptimal force constants can be used in estimates by MBAR. We demonstrate the feasibility and the performance of this approach in the study of the conformational landscape of the alanine dipeptide, met-enkephalin, and adenylate kinase.
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Affiliation(s)
- Jiří Vymětal
- Institute of Organic Chemistry and Biochemistry of the Czech Academy of Sciences, Flemingovo náměstí 542/2, 160 00 Praha 6, Czech Republic
| | - Jiří Vondrášek
- Institute of Organic Chemistry and Biochemistry of the Czech Academy of Sciences, Flemingovo náměstí 542/2, 160 00 Praha 6, Czech Republic
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13
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Protein Function Analysis through Machine Learning. Biomolecules 2022; 12:biom12091246. [PMID: 36139085 PMCID: PMC9496392 DOI: 10.3390/biom12091246] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2022] [Revised: 08/22/2022] [Accepted: 08/31/2022] [Indexed: 11/16/2022] Open
Abstract
Machine learning (ML) has been an important arsenal in computational biology used to elucidate protein function for decades. With the recent burgeoning of novel ML methods and applications, new ML approaches have been incorporated into many areas of computational biology dealing with protein function. We examine how ML has been integrated into a wide range of computational models to improve prediction accuracy and gain a better understanding of protein function. The applications discussed are protein structure prediction, protein engineering using sequence modifications to achieve stability and druggability characteristics, molecular docking in terms of protein–ligand binding, including allosteric effects, protein–protein interactions and protein-centric drug discovery. To quantify the mechanisms underlying protein function, a holistic approach that takes structure, flexibility, stability, and dynamics into account is required, as these aspects become inseparable through their interdependence. Another key component of protein function is conformational dynamics, which often manifest as protein kinetics. Computational methods that use ML to generate representative conformational ensembles and quantify differences in conformational ensembles important for function are included in this review. Future opportunities are highlighted for each of these topics.
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14
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Turzo SMBA, Seffernick JT, Rolland AD, Donor MT, Heinze S, Prell JS, Wysocki VH, Lindert S. Protein shape sampled by ion mobility mass spectrometry consistently improves protein structure prediction. Nat Commun 2022; 13:4377. [PMID: 35902583 PMCID: PMC9334640 DOI: 10.1038/s41467-022-32075-9] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2021] [Accepted: 07/14/2022] [Indexed: 11/09/2022] Open
Abstract
Ion mobility (IM) mass spectrometry provides structural information about protein shape and size in the form of an orientationally-averaged collision cross-section (CCSIM). While IM data have been used with various computational methods, they have not yet been utilized to predict monomeric protein structure from sequence. Here, we show that IM data can significantly improve protein structure determination using the modelling suite Rosetta. We develop the Rosetta Projection Approximation using Rough Circular Shapes (PARCS) algorithm that allows for fast and accurate prediction of CCSIM from structure. Following successful testing of the PARCS algorithm, we use an integrative modelling approach to utilize IM data for protein structure prediction. Additionally, we propose a confidence metric that identifies near native models in the absence of a known structure. The results of this study demonstrate the ability of IM data to consistently improve protein structure prediction. Collision cross sections (CCS) from ion mobility mass spectrometry provide information about protein shape and size. Here, the authors develop an algorithm to predict CCS and integrate experimental ion mobility data into Rosetta-based molecular modelling to predict protein structures from sequence.
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Affiliation(s)
- S M Bargeen Alam Turzo
- Department of Chemistry and Biochemistry and Resource for Native Mass Spectrometry Guided Structural Biology, Ohio State University, Columbus, OH, 43210, USA
| | - Justin T Seffernick
- Department of Chemistry and Biochemistry and Resource for Native Mass Spectrometry Guided Structural Biology, Ohio State University, Columbus, OH, 43210, USA
| | - Amber D Rolland
- Department of Chemistry and Biochemistry and Materials Science Institute, University of Oregon, Eugene, OR, 97403, USA
| | - Micah T Donor
- Department of Chemistry and Biochemistry and Materials Science Institute, University of Oregon, Eugene, OR, 97403, USA
| | - Sten Heinze
- Department of Chemistry and Biochemistry and Resource for Native Mass Spectrometry Guided Structural Biology, Ohio State University, Columbus, OH, 43210, USA
| | - James S Prell
- Department of Chemistry and Biochemistry and Materials Science Institute, University of Oregon, Eugene, OR, 97403, USA
| | - Vicki H Wysocki
- Department of Chemistry and Biochemistry and Resource for Native Mass Spectrometry Guided Structural Biology, Ohio State University, Columbus, OH, 43210, USA
| | - Steffen Lindert
- Department of Chemistry and Biochemistry and Resource for Native Mass Spectrometry Guided Structural Biology, Ohio State University, Columbus, OH, 43210, USA.
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15
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Seffernick JT, Turzo SMBA, Harvey SR, Kim Y, Somogyi Á, Marciano S, Wysocki VH, Lindert S. Simulation of Energy-Resolved Mass Spectrometry Distributions from Surface-Induced Dissociation. Anal Chem 2022; 94:10506-10514. [PMID: 35834801 PMCID: PMC9672976 DOI: 10.1021/acs.analchem.2c01869] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Abstract
Understanding the relationship between protein structure and experimental data is crucial for utilizing experiments to solve biochemical problems and optimizing the use of sparse experimental data for structural interpretation. Tandem mass spectrometry (MS/MS) can be used with a variety of methods to collect structural data for proteins. One example is surface-induced dissociation (SID), which is used to break apart protein complexes (via a surface collision) into intact subcomplexes and can be performed at multiple laboratory frame SID collision energies. These energy-resolved MS/MS experiments have shown that the profile of the breakages depends on the acceleration energy of the collision. It is possible to extract an appearance energy (AE) from energy-resolved mass spectrometry (ERMS) data, which shows the relative intensity of each type of subcomplex as a function of SID acceleration energy. We previously determined that these AE values for specific interfaces correlated with structural features related to interface strength. In this study, we further examined the structural relationships by developing a method to predict the full ERMS plot from the structure, rather than extracting a single value. First, we noted that for proteins with multiple interface types, we could reproduce the correct shapes of breakdown curves, further confirming previous structural hypotheses. Next, we demonstrated that interface size and energy density (measured using Rosetta) correlated with data derived from the ERMS plot (R2 = 0.71). Furthermore, based on this trend, we used native crystal structures to predict ERMS. The majority of predictions resulted in good agreement, and the average root-mean-square error was 0.20 for the 20 complexes in our data set. We also show that if additional information on cleavage as a function of collision energy could be obtained, the accuracy of predictions improved further. Finally, we demonstrated that ERMS prediction results were better for the native than for inaccurate models in 17/20 cases. An application to run this simulation has been developed in Rosetta, which is freely available for use.
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Affiliation(s)
- Justin T. Seffernick
- Department of Chemistry and Biochemistry, Ohio State University, Columbus, OH 43210, United States,Resource for Native Mass Spectrometry Guided Structural Biology, Ohio State University, Columbus, OH 43210, United States
| | - SM Bargeen Alam Turzo
- Department of Chemistry and Biochemistry, Ohio State University, Columbus, OH 43210, United States,Resource for Native Mass Spectrometry Guided Structural Biology, Ohio State University, Columbus, OH 43210, United States
| | - Sophie R. Harvey
- Department of Chemistry and Biochemistry, Ohio State University, Columbus, OH 43210, United States,Resource for Native Mass Spectrometry Guided Structural Biology, Ohio State University, Columbus, OH 43210, United States
| | - Yongseok Kim
- Department of Chemistry and Biochemistry, Ohio State University, Columbus, OH 43210, United States
| | - Árpád Somogyi
- Department of Chemistry and Biochemistry, Ohio State University, Columbus, OH 43210, United States,Resource for Native Mass Spectrometry Guided Structural Biology, Ohio State University, Columbus, OH 43210, United States
| | - Shir Marciano
- Department of Biomolecular Sciences, Weizmann Institute of Science, Rehovot 76273, Israel
| | - Vicki H. Wysocki
- Department of Chemistry and Biochemistry, Ohio State University, Columbus, OH 43210, United States,Resource for Native Mass Spectrometry Guided Structural Biology, Ohio State University, Columbus, OH 43210, United States
| | - Steffen Lindert
- Department of Chemistry and Biochemistry, Ohio State University, Columbus, OH 43210, United States,Resource for Native Mass Spectrometry Guided Structural Biology, Ohio State University, Columbus, OH 43210, United States,Correspondence to: Department of Chemistry and Biochemistry, Ohio State University, 2114 Newman & Wolfrom Laboratory, 100 W. 18th Avenue, Columbus, OH 43210, 614-292-8284 (office), 614-292-1685 (fax),
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16
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Batista PR, Oliveira Neto M, Perahia D. Editorial: Integrative Structural Biology of Proteins and Macromolecular Assemblies: Bridging Experiments and Simulations. Front Mol Biosci 2022; 9:947582. [PMID: 35832739 PMCID: PMC9272401 DOI: 10.3389/fmolb.2022.947582] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2022] [Accepted: 06/10/2022] [Indexed: 11/13/2022] Open
Affiliation(s)
- Paulo Ricardo Batista
- Programa de Computação Científica, Fundação Oswaldo Cruz, Rio de Janeiro, Brazil
- *Correspondence: Paulo Ricardo Batista,
| | - Mario Oliveira Neto
- Departamento de Biofísica e Farmacologia, Instituto de Biociências de Botucatu, Universidade Estadual Paulista Júlio de Mesquita Filho, Botucatu, Brazil
| | - David Perahia
- Laboratoire de Biologie et Pharmacologie Appliquée, École Normale Supérieure Paris-Saclay, Centre National de la Recherche Scientifique, Gif-sur-Yvette, France
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17
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Huang J, Chu X, Luo Y, Wang Y, Zhang Y, Zhang Y, Li H. Insights into Phosphorylation-Induced Protein Allostery and Conformational Dynamics of Glycogen Phosphorylase via Integrative Structural Mass Spectrometry and In Silico Modeling. ACS Chem Biol 2022; 17:1951-1962. [PMID: 35675581 DOI: 10.1021/acschembio.2c00393] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Allosteric regulation plays a fundamental role in innumerable biological processes. Understanding its dynamic mechanism and impact at the molecular level is of great importance in disease diagnosis and drug discovery. Glycogen phosphorylase (GP) is a phosphoprotein responding to allosteric regulation and has significant biological importance to glycogen metabolism. Although the atomic structures of GP have been previously solved, the conformational dynamics of GP related to allostery regulation remain largely elusive due to its macromolecular size (∼196 kDa). Here, we integrated native top-down mass spectrometry (nTD-MS), hydrogen-deuterium exchange MS (HDX-MS), protection factor (PF) analysis, molecular dynamics (MD) simulations, and allostery signaling analysis to examine the structural basis and dynamics for the allosteric regulation of GP by phosphorylation. nTD-MS reveals differences in structural stability as well as oligomeric state between the unphosphorylated (GPb) and phosphorylated (GPa) forms. HDX-MS, PF analysis, and MD simulations further pinpoint the structural differences between GPb and GPa involving the binding interfaces (the N-terminal and tower-tower helices), catalytic site, and PLP-binding region. More importantly, it also allowed us to complete the missing link of the long-range communication process from the N-terminal tail to the catalytic site caused by phosphorylation. This integrative MS and in silico-based platform is highly complementary to biophysical approaches and yields valuable insights into protein structures and dynamic regulation.
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Affiliation(s)
- Jing Huang
- School of Pharmaceutical Sciences, Sun Yat-sen University, Guangzhou Higher Education Mega Center, No. 132 Wai Huan Dong Lu, Guangzhou 510006, China
| | - Xiakun Chu
- Advanced Materials Thrust, The Hong Kong University of Science and Technology (Guangzhou), Nansha, Guangzhou, Guangdong 511400, China
| | - Yuxiang Luo
- School of Pharmaceutical Sciences, Sun Yat-sen University, Guangzhou Higher Education Mega Center, No. 132 Wai Huan Dong Lu, Guangzhou 510006, China
| | - Yong Wang
- The Provincial International Science and Technology Cooperation Base on Engineering Biology, International Campus of Zhejiang University, College of Life Sciences, Shanghai Institute for Advanced Study, Institute of Quantitative Biology, Zhejiang University, Haining 314400, China
| | - Ying Zhang
- School of Pharmaceutical Sciences, Sun Yat-sen University, Guangzhou Higher Education Mega Center, No. 132 Wai Huan Dong Lu, Guangzhou 510006, China
| | - Yu Zhang
- State Key Laboratory of Crop Stress Adaptation and Improvement, Key Laboratory of Plant Stress Biology, School of Life Sciences, Henan University, Kaifeng 475004, China
| | - Huilin Li
- School of Pharmaceutical Sciences, Sun Yat-sen University, Guangzhou Higher Education Mega Center, No. 132 Wai Huan Dong Lu, Guangzhou 510006, China.,Guangdong Key Laboratory of Chiral Molecule and Drug Discovery, School of Pharmaceutical Sciences, Sun Yat-sen University, Guangzhou, Guangdong 510006, China
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18
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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: 0.5] [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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19
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Sorzano COS, Carazo JM. Cryo-Electron Microscopy: the field of 1,000 + methods. J Struct Biol 2022; 214:107861. [PMID: 35568276 DOI: 10.1016/j.jsb.2022.107861] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2021] [Revised: 03/21/2022] [Accepted: 04/21/2022] [Indexed: 01/18/2023]
Abstract
Cryo-Electron Microscopy (CryoEM) is currently a well-established method to elucidate a biological macromolecule's three-dimensional (3D) structure. Its success is due to technological and methodological advances in several fronts: sample preparation, electron optics and detection, image acquisition, image processing, and map interpretation. The first methods started in the late 1960s and, since then, new methods on all fronts have continuously been published, maturating the field as we know it now. In terms of publications, we can distinguish several periods, witnessing a substantial acceleration of methodological publications in recent years, pointing out to an increased interest in the domain. On the other hand, this accelerated increase of methods development may confuse practitioners about which method they should be using (and how) and highlight the importance of paying attention to establishing best practices for methods reporting and usage. In this paper, we analyze the trends identified in over 1,000 methodological papers. Our focus is primarily on computational image processing methods. However, our list also covers some aspects of sample preparation and image acquisition. Several interesting ideas stem out from this study: 1) Single Particle Analysis (SPA) has largely accelerated in the last decade and sample preparation methods in the last five years; 2) Electron Tomography is not yet in a rapidly growing phase, but it is foreseeable that it will soon be; 3) the work horses of SPA are 3D classification, 3D reconstruction, and 3D alignment, and there have been many papers on these topics, which are not considered to be solved yet, but ever improving; and 4) since the resolution revolution, atomic modelling has also caught on as a hot topic.
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Affiliation(s)
- C O S Sorzano
- Natl. Center of Biotechnology, CSIC. c/Darwin, 3. Campus Univ. Autónoma de Madrid. 28049 Madrid, Spain
| | - J M Carazo
- Natl. Center of Biotechnology, CSIC. c/Darwin, 3. Campus Univ. Autónoma de Madrid. 28049 Madrid, Spain
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20
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Biehn SE, Picarello DM, Pan X, Vachet RW, Lindert S. Accounting for Neighboring Residue Hydrophobicity in Diethylpyrocarbonate Labeling Mass Spectrometry Improves Rosetta Protein Structure Prediction. JOURNAL OF THE AMERICAN SOCIETY FOR MASS SPECTROMETRY 2022; 33:584-591. [PMID: 35147431 PMCID: PMC8988852 DOI: 10.1021/jasms.1c00373] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/02/2023]
Abstract
Covalent labeling mass spectrometry allows for protein structure elucidation via covalent modification and identification of exposed residues. Diethylpyrocarbonate (DEPC) is a commonly used covalent labeling reagent that provides insight into structure through the labeling of lysine, histidine, serine, threonine, and tyrosine residues. We recently implemented a Rosetta algorithm that used binary DEPC labeling data to improve protein structure prediction efforts. In this work, we improved on our modeling efforts by accounting for the level of hydrophobicity of neighboring residues in the microenvironment of serine, threonine, and tyrosine residues to obtain a more accurate estimate of the hydrophobic neighbor count. This was incorporated into Rosetta functionality, along with considerations for solvent-exposed histidine and lysine residues. Overall, our new Rosetta score term successfully identified best scoring models with less than 2 Å root-mean-squared deviations (RMSDs) for five of the seven benchmark proteins tested. We additionally developed a confidence metric to measure prediction success for situations in which a native structure is unavailable.
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Affiliation(s)
- Sarah E. Biehn
- Department of Chemistry and Biochemistry, Ohio State University, Columbus, OH, 43210, United States
| | - Danielle M. Picarello
- Department of Bioengineering, Lehigh University, Bethlehem, PA 18015, United States and Rosetta Commons Research Experience for Undergraduates, Rosetta Commons
| | - Xiao Pan
- Department of Chemistry, University of Massachusetts, Amherst, Amherst, MA 01003, United States
| | - Richard W. Vachet
- Department of Chemistry, University of Massachusetts, Amherst, Amherst, MA 01003, United States
| | - Steffen Lindert
- Department of Chemistry and Biochemistry, Ohio State University, Columbus, OH, 43210, United States
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21
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Abstract
Abstract
Machine learning (ML) has revolutionised the field of structure-based drug design (SBDD) in recent years. During the training stage, ML techniques typically analyse large amounts of experimentally determined data to create predictive models in order to inform the drug discovery process. Deep learning (DL) is a subfield of ML, that relies on multiple layers of a neural network to extract significantly more complex patterns from experimental data, and has recently become a popular choice in SBDD. This review provides a thorough summary of the recent DL trends in SBDD with a particular focus on de novo drug design, binding site prediction, and binding affinity prediction of small molecules.
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22
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Dey D, Dhar D, Fortunato H, Obata D, Tanaka A, Tanaka R, Basu S, Ito H. Insights into the structure and function of the rate-limiting enzyme of chlorophyll degradation through analysis of a bacterial Mg-dechelatase homolog. Comput Struct Biotechnol J 2021; 19:5333-5347. [PMID: 34745453 PMCID: PMC8531759 DOI: 10.1016/j.csbj.2021.09.023] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2021] [Revised: 09/20/2021] [Accepted: 09/21/2021] [Indexed: 12/13/2022] Open
Abstract
The Mg-dechelatase enzyme encoded by the Stay-Green (SGR) gene catalyzes Mg2+ dechelation from chlorophyll a. This reaction is the first committed step of chlorophyll degradation pathway in plants and is thus indispensable for the process of leaf senescence. There is no structural information available for this or its related enzymes. This study aims to provide insights into the structure and reaction mechanism of the enzyme through biochemical and computational analysis of an SGR homolog from the Chloroflexi Anaerolineae (AbSGR-h). Recombinant AbSGR-h with its intact sequence and those with mutations were overexpressed in Escherichia coli and their Mg-dechelatase activity were compared. Two aspartates - D34 and D62 were found to be essential for catalysis, while R26, Y28, T29 and D114 were responsible for structural maintenance. Gel filtration analysis of the recombinant AbSGR-h indicates that it forms a homo-oligomer. The three-dimensional structure of AbSGR-h was predicted by a deep learning-based method, which was evaluated by protein structure quality evaluation programs while structural stability of wild-type and mutant forms were investigated through molecular dynamics simulations. Furthermore, in concordance with the results of enzyme assay, molecular docking concluded the significance of D34 in ligand interaction. By combining biochemical analysis and computational prediction, this study unveils the detailed structural characteristics of the enzyme, including the probable pocket of interaction and the residues of structural and functional importance. It also serves as a basis for further studies on Mg-dechelatase such as elucidation of its reaction mechanism or inhibitor screening.
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Affiliation(s)
- Debayan Dey
- Graduate School of Life Science, Hokkaido University, Sapporo 060-0810, Japan.,Institute of Low Temperature Science, Hokkaido University, Sapporo 060-0819, Japan
| | - Dipanjana Dhar
- Graduate School of Science, Hokkaido University, Sapporo 060-0810, Japan.,Department of Natural History Sciences, Hokkaido University, Sapporo 060-0810, Japan
| | - Helena Fortunato
- Department of Natural History Sciences, Hokkaido University, Sapporo 060-0810, Japan
| | - Daichi Obata
- Institute of Low Temperature Science, Hokkaido University, Sapporo 060-0819, Japan
| | - Ayumi Tanaka
- Institute of Low Temperature Science, Hokkaido University, Sapporo 060-0819, Japan
| | - Ryouichi Tanaka
- Institute of Low Temperature Science, Hokkaido University, Sapporo 060-0819, Japan
| | - Soumalee Basu
- Department of Microbiology, University of Calcutta, Kolkata 700019, India
| | - Hisashi Ito
- Institute of Low Temperature Science, Hokkaido University, Sapporo 060-0819, Japan
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23
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Nguyen TT, Marzolf DR, Seffernick JT, Heinze S, Lindert S. Protein structure prediction using residue-resolved protection factors from hydrogen-deuterium exchange NMR. Structure 2021; 30:313-320.e3. [PMID: 34739840 DOI: 10.1016/j.str.2021.10.006] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2021] [Revised: 08/04/2021] [Accepted: 10/15/2021] [Indexed: 11/17/2022]
Abstract
Hydrogen-deuterium exchange (HDX) measured by nuclear magnetic resonance (NMR) provides structural information for proteins relating to solvent accessibility and flexibility. While this structural information is beneficial, the data cannot be used exclusively to elucidate structures. However, the structural information provided by the HDX-NMR data can be supplemented by computational methods. In previous work, we developed an algorithm in Rosetta to predict structures using qualitative HDX-NMR data (categories of exchange rate). Here we expand on the effort, and utilize quantitative protection factors (PFs) from HDX-NMR for structure prediction. From observed correlations between PFs and solvent accessibility/flexibility measures, we present a scoring function to quantify the agreement with HDX data. Using a benchmark set of 10 proteins, an average improvement of 5.13 Å in root-mean-square deviation (RMSD) is observed for cases of inaccurate Rosetta predictions. Ultimately, seven out of 10 predictions are accurate without including HDX data, and nine out of 10 are accurate when using our PF-based HDX score.
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Affiliation(s)
- Tung T Nguyen
- Department of Chemistry and Biochemistry, Denison University, Granville, OH 43023, USA
| | - Daniel R Marzolf
- Department of Chemistry and Biochemistry, Ohio State University, 2114 Newman & Wolfrom Laboratory, 100 W. 18(th) Avenue, Columbus, OH 43210, USA
| | - Justin T Seffernick
- Department of Chemistry and Biochemistry, Ohio State University, 2114 Newman & Wolfrom Laboratory, 100 W. 18(th) Avenue, Columbus, OH 43210, USA
| | - Sten Heinze
- Department of Chemistry and Biochemistry, Ohio State University, 2114 Newman & Wolfrom Laboratory, 100 W. 18(th) Avenue, Columbus, OH 43210, USA
| | - Steffen Lindert
- Department of Chemistry and Biochemistry, Ohio State University, 2114 Newman & Wolfrom Laboratory, 100 W. 18(th) Avenue, Columbus, OH 43210, USA.
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24
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Abstract
Knowledge of protein structure is crucial to our understanding of biological function and is routinely used in drug discovery. High-resolution techniques to determine the three-dimensional atomic coordinates of proteins are available. However, such methods are frequently limited by experimental challenges such as sample quantity, target size, and efficiency. Structural mass spectrometry (MS) is a technique in which structural features of proteins are elucidated quickly and relatively easily. Computational techniques that convert sparse MS data into protein models that demonstrate agreement with the data are needed. This review features cutting-edge computational methods that predict protein structure from MS data such as chemical cross-linking, hydrogen-deuterium exchange, hydroxyl radical protein footprinting, limited proteolysis, ion mobility, and surface-induced dissociation. Additionally, we address future directions for protein structure prediction with sparse MS data. Expected final online publication date for the Annual Review of Physical Chemistry, Volume 73 is April 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.
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Affiliation(s)
- Sarah E Biehn
- 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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25
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Olenginski LT, Taiwo KM, LeBlanc RM, Dayie TK. Isotope-Labeled RNA Building Blocks for NMR Structure and Dynamics Studies. Molecules 2021; 26:5581. [PMID: 34577051 PMCID: PMC8466439 DOI: 10.3390/molecules26185581] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2021] [Revised: 09/05/2021] [Accepted: 09/07/2021] [Indexed: 01/19/2023] Open
Abstract
RNA structural research lags behind that of proteins, preventing a robust understanding of RNA functions. NMR spectroscopy is an apt technique for probing the structures and dynamics of RNA molecules in solution at atomic resolution. Still, RNA analysis by NMR suffers from spectral overlap and line broadening, both of which worsen for larger RNAs. Incorporation of stable isotope labels into RNA has provided several solutions to these challenges. In this review, we summarize the benefits and limitations of various methods used to obtain isotope-labeled RNA building blocks and how they are used to prepare isotope-labeled RNA for NMR structure and dynamics studies.
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Affiliation(s)
- Lukasz T. Olenginski
- Center for Biomolecular Structure and Organization, Department of Chemistry and Biochemistry, University of Maryland, College Park, MD 20742, USA; (L.T.O.); (K.M.T.); (R.M.L.)
| | - Kehinde M. Taiwo
- Center for Biomolecular Structure and Organization, Department of Chemistry and Biochemistry, University of Maryland, College Park, MD 20742, USA; (L.T.O.); (K.M.T.); (R.M.L.)
| | - Regan M. LeBlanc
- Center for Biomolecular Structure and Organization, Department of Chemistry and Biochemistry, University of Maryland, College Park, MD 20742, USA; (L.T.O.); (K.M.T.); (R.M.L.)
- Vertex Pharmaceuticals, 50 Northern Avenue, Boston, MA 02210, USA
| | - Theodore K. Dayie
- Center for Biomolecular Structure and Organization, Department of Chemistry and Biochemistry, University of Maryland, College Park, MD 20742, USA; (L.T.O.); (K.M.T.); (R.M.L.)
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26
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Abstract
Native mass spectrometry (MS) is aimed at preserving and determining the native structure, composition, and stoichiometry of biomolecules and their complexes from solution after they are transferred into the gas phase. Major improvements in native MS instrumentation and experimental methods over the past few decades have led to a concomitant increase in the complexity and heterogeneity of samples that can be analyzed, including protein-ligand complexes, protein complexes with multiple coexisting stoichiometries, and membrane protein-lipid assemblies. Heterogeneous features of these biomolecular samples can be important for understanding structure and function. However, sample heterogeneity can make assignment of ion mass, charge, composition, and structure very challenging due to the overlap of tens or even hundreds of peaks in the mass spectrum. In this review, we cover data analysis, experimental, and instrumental advances and strategies aimed at solving this problem, with an in-depth discussion of theoretical and practical aspects of the use of available deconvolution algorithms and tools. We also reflect upon current challenges and provide a view of the future of this exciting field.
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Affiliation(s)
- Amber D Rolland
- Department of Chemistry and Biochemistry, University of Oregon, Eugene, Oregon 97403-1253, United States
| | - James S Prell
- Department of Chemistry and Biochemistry, University of Oregon, Eugene, Oregon 97403-1253, United States.,Materials Science Institute, University of Oregon, Eugene, Oregon 97403-1252, United States
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27
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Lin X, Qi Y, Latham AP, Zhang B. Multiscale modeling of genome organization with maximum entropy optimization. J Chem Phys 2021; 155:010901. [PMID: 34241389 PMCID: PMC8253599 DOI: 10.1063/5.0044150] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2021] [Accepted: 04/28/2021] [Indexed: 12/15/2022] Open
Abstract
Three-dimensional (3D) organization of the human genome plays an essential role in all DNA-templated processes, including gene transcription, gene regulation, and DNA replication. Computational modeling can be an effective way of building high-resolution genome structures and improving our understanding of these molecular processes. However, it faces significant challenges as the human genome consists of over 6 × 109 base pairs, a system size that exceeds the capacity of traditional modeling approaches. In this perspective, we review the progress that has been made in modeling the human genome. Coarse-grained models parameterized to reproduce experimental data via the maximum entropy optimization algorithm serve as effective means to study genome organization at various length scales. They have provided insight into the principles of whole-genome organization and enabled de novo predictions of chromosome structures from epigenetic modifications. Applications of these models at a near-atomistic resolution further revealed physicochemical interactions that drive the phase separation of disordered proteins and dictate chromatin stability in situ. We conclude with an outlook on the opportunities and challenges in studying chromosome dynamics.
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Affiliation(s)
- Xingcheng Lin
- Department of Chemistry, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA
| | - Yifeng Qi
- Department of Chemistry, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA
| | - Andrew P. Latham
- Department of Chemistry, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA
| | - Bin Zhang
- Department of Chemistry, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA
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28
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Biehn SE, Limpikirati P, Vachet RW, Lindert S. Utilization of Hydrophobic Microenvironment Sensitivity in Diethylpyrocarbonate Labeling for Protein Structure Prediction. Anal Chem 2021; 93:8188-8195. [PMID: 34061512 DOI: 10.1021/acs.analchem.1c00395] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
Diethylpyrocarbonate (DEPC) labeling analyzed with mass spectrometry can provide important insights into higher order protein structures. It has been previously shown that neighboring hydrophobic residues promote a local increase in DEPC concentration such that serine, threonine, and tyrosine residues are more likely to be labeled despite low solvent exposure. In this work, we developed a Rosetta algorithm that used the knowledge of labeled and unlabeled serine, threonine, and tyrosine residues and assessed their local hydrophobic environment to improve protein structure prediction. Additionally, DEPC-labeled histidine and lysine residues with higher relative solvent accessible surface area values (i.e., more exposed) were scored favorably. Application of our score term led to reductions of the root-mean-square deviations (RMSDs) of the lowest scoring models. Additionally, models that scored well tended to have lower RMSDs. A detailed tutorial describing our protocol and required command lines is included. Our work demonstrated the considerable potential of DEPC covalent labeling data to be used for accurate higher order structure determination.
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Affiliation(s)
- Sarah E Biehn
- Department of Chemistry and Biochemistry, Ohio State University, Columbus, Ohio 43210, United States
| | - Patanachai Limpikirati
- Department of Food and Pharmaceutical Chemistry, Faculty of Pharmaceutical Sciences, Chulalongkorn University, Bangkok 10330, Thailand
| | - Richard W Vachet
- Department of Chemistry, University of Massachusetts, Amherst, Massachusetts 01003, United States
| | - Steffen Lindert
- Department of Chemistry and Biochemistry, Ohio State University, Columbus, Ohio 43210, United States
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29
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Seffernick JT, Canfield SM, Harvey SR, Wysocki VH, Lindert S. Prediction of Protein Complex Structure Using Surface-Induced Dissociation and Cryo-Electron Microscopy. Anal Chem 2021; 93:7596-7605. [PMID: 33999617 DOI: 10.1021/acs.analchem.0c05468] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Abstract
A variety of techniques involving the use of mass spectrometry (MS) have been developed to obtain structural information on proteins and protein complexes. One example of these techniques, surface-induced dissociation (SID), has been used to study the oligomeric state and connectivity of protein complexes. Recently, we demonstrated that appearance energies (AE) could be extracted from SID experiments and that they correlate with structural features of specific protein-protein interfaces. While SID AE provides some structural information, the AE data alone are not sufficient to determine the structures of the complexes. For this reason, we sought to supplement the data with computational modeling, through protein-protein docking. In a previous study, we demonstrated that the scoring of structures generated from protein-protein docking could be improved with the inclusion of SID data; however, this work relied on knowledge of the correct tertiary structure and only built full complexes for a few cases. Here, we performed docking using input structures that require less prior knowledge, using homology models, unbound crystal structures, and bound+perturbed crystal structures. Using flexible ensemble docking (to build primarily subcomplexes from an ensemble of backbone structures), the RMSD100 of all (15/15) predicted structures using the combined Rosetta, cryo-electron microscopy (cryo-EM), and SID score was less than 4 Å, compared to only 7/15 without SID and cryo-EM. Symmetric docking (which used symmetry to build full complexes) resulted in predicted structures with RMSD100 less than 4 Å for 14/15 cases with experimental data, compared to only 5/15 without SID and cryo-EM. Finally, we also developed a confidence metric for which all (26/26) proteins flagged as high confidence were accurately predicted.
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Affiliation(s)
- Justin T Seffernick
- Department of Chemistry and Biochemistry, Ohio State University, 2114 Newman & Wolfrom Laboratory, 100 West 18th Avenue, Columbus, Ohio 43210, United States
| | - Shane M Canfield
- Department of Chemistry, Kenyon College, Gambier, Ohio 43022, United States
| | - Sophie R Harvey
- Department of Chemistry and Biochemistry, Ohio State University, 2114 Newman & Wolfrom Laboratory, 100 West 18th Avenue, Columbus, Ohio 43210, United States
| | - Vicki H Wysocki
- Department of Chemistry and Biochemistry, Ohio State University, 2114 Newman & Wolfrom Laboratory, 100 West 18th Avenue, Columbus, Ohio 43210, United States
| | - Steffen Lindert
- Department of Chemistry and Biochemistry, Ohio State University, 2114 Newman & Wolfrom Laboratory, 100 West 18th Avenue, Columbus, Ohio 43210, United States
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30
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Marzolf DR, Seffernick JT, Lindert S. Protein Structure Prediction from NMR Hydrogen-Deuterium Exchange Data. J Chem Theory Comput 2021; 17:2619-2629. [PMID: 33780620 DOI: 10.1021/acs.jctc.1c00077] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
Amide hydrogen-deuterium exchange (HDX) has long been used to determine regional flexibility and binding sites in proteins; however, the data are too sparse for full structural characterization. Experiments that measure HDX rates, such as HDX-NMR, have far higher throughput compared to structure determination via X-ray crystallography, cryo-EM, or a full suite of NMR experiments. Data from HDX-NMR experiments encode information on the protein structure, making HDX a prime candidate to be supplemented by computational algorithms for protein structure prediction. We have developed a methodology to incorporate HDX-NMR data into ab initio protein structure prediction using the Rosetta software framework to predict structures based on experimental agreement. To demonstrate the efficacy of our algorithm, we examined 38 proteins with HDX-NMR data available, comparing the predicted model with and without the incorporation of HDX data into scoring. The root-mean-square deviation (rmsd, a measure of the average atomic distance between superimposed models) of the predicted model improved by 1.42 Å on average after incorporating the HDX-NMR data into scoring. The average rmsd improvement for the proteins where the selected model rmsd changed after incorporating HDX data was 3.63 Å, including one improvement of more than 11 Å and seven proteins improving by greater than 4 Å, with 12/15 proteins improving overall. Additionally, for independent verification, two proteins that were not part of the original benchmark were scored including HDX data, with a dramatic improvement of the selected model rmsd of nearly 9 Å for one of the proteins. Moreover, we have developed a confidence metric allowing us to successfully identify near-native models in the absence of a native structure. Improvement in model selection with a strong confidence measure demonstrates that protein structure prediction with HDX-NMR is a powerful tool which can be performed with minimal additional computational strain and expense.
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Affiliation(s)
- Daniel R Marzolf
- Department of Chemistry and Biochemistry, Ohio State University, Columbus, Ohio 43210, United States
| | - Justin T Seffernick
- Department of Chemistry and Biochemistry, Ohio State University, Columbus, Ohio 43210, United States
| | - Steffen Lindert
- Department of Chemistry and Biochemistry, Ohio State University, Columbus, Ohio 43210, United States
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