1
|
Savva CG, Sobhy MA, De Biasio A, Hamdan SM. Structure of Aquifex aeolicus lumazine synthase by cryo-electron microscopy to 1.42 Å resolution. IUCRJ 2024; 11:723-729. [PMID: 38965901 PMCID: PMC11364023 DOI: 10.1107/s2052252524005530] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/02/2024] [Accepted: 06/10/2024] [Indexed: 07/06/2024]
Abstract
Single-particle cryo-electron microscopy (cryo-EM) has become an essential structural determination technique with recent hardware developments making it possible to reach atomic resolution, at which individual atoms, including hydrogen atoms, can be resolved. In this study, we used the enzyme involved in the penultimate step of riboflavin biosynthesis as a test specimen to benchmark a recently installed microscope and determine if other protein complexes could reach a resolution of 1.5 Å or better, which so far has only been achieved for the iron carrier ferritin. Using state-of-the-art microscope and detector hardware as well as the latest software techniques to overcome microscope and sample limitations, a 1.42 Å map of Aquifex aeolicus lumazine synthase (AaLS) was obtained from a 48 h microscope session. In addition to water molecules and ligands involved in the function of AaLS, we can observe positive density for ∼50% of the hydrogen atoms. A small improvement in the resolution was achieved by Ewald sphere correction which was expected to limit the resolution to ∼1.5 Å for a molecule of this diameter. Our study confirms that other protein complexes can be solved to near-atomic resolution. Future improvements in specimen preparation and protein complex stabilization may allow more flexible macromolecules to reach this level of resolution and should become a priority of study in the field.
Collapse
Affiliation(s)
- Christos G. Savva
- Biological and Environmental Science and EngineeringKing Abdullah University of Science and Technology4700 KAUSTThuwal23955Saudi Arabia
| | - Mohamed A. Sobhy
- Biological and Environmental Science and EngineeringKing Abdullah University of Science and Technology4700 KAUSTThuwal23955Saudi Arabia
| | - Alfredo De Biasio
- Biological and Environmental Science and EngineeringKing Abdullah University of Science and Technology4700 KAUSTThuwal23955Saudi Arabia
| | - Samir M. Hamdan
- Biological and Environmental Science and EngineeringKing Abdullah University of Science and Technology4700 KAUSTThuwal23955Saudi Arabia
| |
Collapse
|
2
|
Gucwa M, Bijak V, Zheng H, Murzyn K, Minor W. CheckMyMetal (CMM): validating metal-binding sites in X-ray and cryo-EM data. IUCRJ 2024; 11:871-877. [PMID: 39141478 PMCID: PMC11364027 DOI: 10.1107/s2052252524007073] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/10/2024] [Accepted: 07/18/2024] [Indexed: 08/16/2024]
Abstract
Identifying and characterizing metal-binding sites (MBS) within macromolecular structures is imperative for elucidating their biological functions. CheckMyMetal (CMM) is a web based tool that facilitates the interactive validation of MBS in structures determined through X-ray crystallography and cryo-electron microscopy (cryo-EM). Recent updates to CMM have significantly enhanced its capability to efficiently handle large datasets generated from cryo-EM structural analyses. In this study, we address various challenges inherent in validating MBS within both X-ray and cryo-EM structures. Specifically, we examine the difficulties associated with accurately identifying metals and modeling their coordination environments by considering the ongoing reproducibility challenges in structural biology and the critical importance of well annotated, high-quality experimental data. CMM employs a sophisticated framework of rules rooted in the valence bond theory for MBS validation. We explore how CMM validation parameters correlate with the resolution of experimentally derived structures of macromolecules and their complexes. Additionally, we showcase the practical utility of CMM by analyzing a representative cryo-EM structure. Through a comprehensive examination of experimental data, we demonstrate the capability of CMM to advance MBS characterization and identify potential instances of metal misassignment.
Collapse
Affiliation(s)
- Michal Gucwa
- Department of Molecular Physiology and Biological PhysicsUniversity of VirginiaCharlottesville22908USA
- Department of Computational Biophysics and BioinformaticsJagiellonian UniversityKrakowPoland
- Doctoral School of Exact and Natural SciencesJagiellonian UniversityKrakowPoland
| | - Vanessa Bijak
- Department of Molecular Physiology and Biological PhysicsUniversity of VirginiaCharlottesville22908USA
| | - Heping Zheng
- Bioinformatics CenterHunan University College of BiologyChangshaHunan410082People’s Republic of China
| | - Krzysztof Murzyn
- Department of Computational Biophysics and BioinformaticsJagiellonian UniversityKrakowPoland
| | - Wladek Minor
- Department of Molecular Physiology and Biological PhysicsUniversity of VirginiaCharlottesville22908USA
| |
Collapse
|
3
|
Toubdji S, Thullier Q, Kilz LM, Marchand V, Yuan Y, Sudol C, Goyenvalle C, Jean-Jean O, Rose S, Douthwaite S, Hardy L, Baharoglu Z, de Crécy-Lagard V, Helm M, Motorin Y, Hamdane D, Brégeon D. Exploring a unique class of flavoenzymes: Identification and biochemical characterization of ribosomal RNA dihydrouridine synthase. Proc Natl Acad Sci U S A 2024; 121:e2401981121. [PMID: 39078675 PMCID: PMC11317573 DOI: 10.1073/pnas.2401981121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2024] [Accepted: 06/20/2024] [Indexed: 07/31/2024] Open
Abstract
Dihydrouridine (D), a prevalent and evolutionarily conserved base in the transcriptome, primarily resides in tRNAs and, to a lesser extent, in mRNAs. Notably, this modification is found at position 2449 in the Escherichia coli 23S rRNA, strategically positioned near the ribosome's peptidyl transferase site. Despite the prior identification, in E. coli genome, of three dihydrouridine synthases (DUS), a set of NADPH and FMN-dependent enzymes known for introducing D in tRNAs and mRNAs, characterization of the enzyme responsible for D2449 deposition has remained elusive. This study introduces a rapid method for detecting D in rRNA, involving reverse transcriptase-blockage at the rhodamine-labeled D2449 site, followed by PCR amplification (RhoRT-PCR). Through analysis of rRNA from diverse E. coli strains, harboring chromosomal or single-gene deletions, we pinpoint the yhiN gene as the ribosomal dihydrouridine synthase, now designated as RdsA. Biochemical characterizations uncovered RdsA as a unique class of flavoenzymes, dependent on FAD and NADH, with a complex structural topology. In vitro assays demonstrated that RdsA dihydrouridylates a short rRNA transcript mimicking the local structure of the peptidyl transferase site. This suggests an early introduction of this modification before ribosome assembly. Phylogenetic studies unveiled the widespread distribution of the yhiN gene in the bacterial kingdom, emphasizing the conservation of rRNA dihydrouridylation. In a broader context, these findings underscore nature's preference for utilizing reduced flavin in the reduction of uridines and their derivatives.
Collapse
Affiliation(s)
- Sabrine Toubdji
- Sorbonne Université, CNRS, Institut de Biologie Paris Seine, Biology of Aging and Adaptation, Institut de Biologie Paris-Seine, F-75252Paris Cedex 05, France
- Collège De France, Sorbonne Université, CNRS, Laboratoire de Chimie des Processus Biologiques, F-75231, Paris Cedex 05, France
| | - Quentin Thullier
- Université de Lorraine, CNRS, Institut National de la Santé et de la Recherche Médicale, Ingénierie-Biologie-Santé en Lorraine, Epitranscriptomique et Séquençage Core Facility, F-54000Nancy, France
- Université de Lorraine, CNRS, Ingénierie Moléculaire, Cellulaire et Physiopathologie, F-54000Nancy, France
| | - Lea-Marie Kilz
- Institut für Pharmazeutische und Biomedizinische Wissenschaften, Johannes Gutenberg-Universität, MainzD-55128, Germany
| | - Virginie Marchand
- Université de Lorraine, CNRS, Institut National de la Santé et de la Recherche Médicale, Ingénierie-Biologie-Santé en Lorraine, Epitranscriptomique et Séquençage Core Facility, F-54000Nancy, France
- Université de Lorraine, CNRS, Ingénierie Moléculaire, Cellulaire et Physiopathologie, F-54000Nancy, France
| | - Yifeng Yuan
- Department of Microbiology and Cell Science, University of Florida, Gainesville, FL32611
| | - Claudia Sudol
- Sorbonne Université, CNRS, Institut de Biologie Paris Seine, Biology of Aging and Adaptation, Institut de Biologie Paris-Seine, F-75252Paris Cedex 05, France
- Collège De France, Sorbonne Université, CNRS, Laboratoire de Chimie des Processus Biologiques, F-75231, Paris Cedex 05, France
| | - Catherine Goyenvalle
- Sorbonne Université, CNRS, Institut de Biologie Paris Seine, Biology of Aging and Adaptation, Institut de Biologie Paris-Seine, F-75252Paris Cedex 05, France
| | - Olivier Jean-Jean
- Sorbonne Université, CNRS, Institut de Biologie Paris Seine, Biology of Aging and Adaptation, Institut de Biologie Paris-Seine, F-75252Paris Cedex 05, France
| | - Simon Rose
- Department of Biochemistry and Molecular Biology, University of Southern Denmark, DK-5230Odense M, Denmark
| | - Stephen Douthwaite
- Department of Biochemistry and Molecular Biology, University of Southern Denmark, DK-5230Odense M, Denmark
| | - Léo Hardy
- Institut Pasteur, Université Paris Cité, CNRS UMR3525, Unité Plasticité du Génome Bactérien, F-75015 Paris, France
| | - Zeynep Baharoglu
- Institut Pasteur, Université Paris Cité, CNRS UMR3525, Unité Plasticité du Génome Bactérien, F-75015 Paris, France
| | - Valérie de Crécy-Lagard
- Department of Microbiology and Cell Science, University of Florida, Gainesville, FL32611
- Genetics Institute, University of Florida, Gainesville, FL32610
| | - Mark Helm
- Institut für Pharmazeutische und Biomedizinische Wissenschaften, Johannes Gutenberg-Universität, MainzD-55128, Germany
| | - Yuri Motorin
- Université de Lorraine, CNRS, Institut National de la Santé et de la Recherche Médicale, Ingénierie-Biologie-Santé en Lorraine, Epitranscriptomique et Séquençage Core Facility, F-54000Nancy, France
- Université de Lorraine, CNRS, Ingénierie Moléculaire, Cellulaire et Physiopathologie, F-54000Nancy, France
| | - Djemel Hamdane
- Collège De France, Sorbonne Université, CNRS, Laboratoire de Chimie des Processus Biologiques, F-75231, Paris Cedex 05, France
| | - Damien Brégeon
- Sorbonne Université, CNRS, Institut de Biologie Paris Seine, Biology of Aging and Adaptation, Institut de Biologie Paris-Seine, F-75252Paris Cedex 05, France
| |
Collapse
|
4
|
Powell BM, Davis JH. Learning structural heterogeneity from cryo-electron sub-tomograms with tomoDRGN. Nat Methods 2024; 21:1525-1536. [PMID: 38459385 DOI: 10.1038/s41592-024-02210-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Accepted: 02/13/2024] [Indexed: 03/10/2024]
Abstract
Cryo-electron tomography (cryo-ET) enables observation of macromolecular complexes in their native, spatially contextualized cellular environment. Cryo-ET processing software to visualize such complexes at nanometer resolution via iterative alignment and averaging are well developed but rely upon assumptions of structural homogeneity among the complexes of interest. Recently developed tools allow for some assessment of structural diversity but have limited capacity to represent highly heterogeneous structures, including those undergoing continuous conformational changes. Here we extend the highly expressive cryoDRGN (Deep Reconstructing Generative Networks) deep learning architecture, originally created for single-particle cryo-electron microscopy analysis, to cryo-ET. Our new tool, tomoDRGN, learns a continuous low-dimensional representation of structural heterogeneity in cryo-ET datasets while also learning to reconstruct heterogeneous structural ensembles supported by the underlying data. Using simulated and experimental data, we describe and benchmark architectural choices within tomoDRGN that are uniquely necessitated and enabled by cryo-ET. We additionally illustrate tomoDRGN's efficacy in analyzing diverse datasets, using it to reveal high-level organization of human immunodeficiency virus (HIV) capsid complexes assembled in virus-like particles and to resolve extensive structural heterogeneity among ribosomes imaged in situ.
Collapse
Affiliation(s)
- Barrett M Powell
- Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, USA.
| | - Joseph H Davis
- Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, USA.
- Program in Computational and Systems Biology, Massachusetts Institute of Technology, Cambridge, MA, USA.
| |
Collapse
|
5
|
Wankowicz SA, Ravikumar A, Sharma S, Riley B, Raju A, Hogan DW, Flowers J, van den Bedem H, Keedy DA, Fraser JS. Automated multiconformer model building for X-ray crystallography and cryo-EM. eLife 2024; 12:RP90606. [PMID: 38904665 PMCID: PMC11192534 DOI: 10.7554/elife.90606] [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: 06/22/2024] Open
Abstract
In their folded state, biomolecules exchange between multiple conformational states that are crucial for their function. Traditional structural biology methods, such as X-ray crystallography and cryogenic electron microscopy (cryo-EM), produce density maps that are ensemble averages, reflecting molecules in various conformations. Yet, most models derived from these maps explicitly represent only a single conformation, overlooking the complexity of biomolecular structures. To accurately reflect the diversity of biomolecular forms, there is a pressing need to shift toward modeling structural ensembles that mirror the experimental data. However, the challenge of distinguishing signal from noise complicates manual efforts to create these models. In response, we introduce the latest enhancements to qFit, an automated computational strategy designed to incorporate protein conformational heterogeneity into models built into density maps. These algorithmic improvements in qFit are substantiated by superior Rfree and geometry metrics across a wide range of proteins. Importantly, unlike more complex multicopy ensemble models, the multiconformer models produced by qFit can be manually modified in most major model building software (e.g., Coot) and fit can be further improved by refinement using standard pipelines (e.g., Phenix, Refmac, Buster). By reducing the barrier of creating multiconformer models, qFit can foster the development of new hypotheses about the relationship between macromolecular conformational dynamics and function.
Collapse
Affiliation(s)
- Stephanie A Wankowicz
- Department of Bioengineering and Therapeutic Sciences, University of California, San FranciscoSan FranciscoUnited States
| | - Ashraya Ravikumar
- Department of Bioengineering and Therapeutic Sciences, University of California, San FranciscoSan FranciscoUnited States
| | - Shivani Sharma
- Structural Biology Initiative, CUNY Advanced Science Research CenterNew YorkUnited States
- Ph.D. Program in Biology, The Graduate Center, City University of New YorkNew YorkUnited States
| | - Blake Riley
- Structural Biology Initiative, CUNY Advanced Science Research CenterNew YorkUnited States
| | - Akshay Raju
- Structural Biology Initiative, CUNY Advanced Science Research CenterNew YorkUnited States
| | - Daniel W Hogan
- Department of Bioengineering and Therapeutic Sciences, University of California, San FranciscoSan FranciscoUnited States
| | - Jessica Flowers
- Department of Bioengineering and Therapeutic Sciences, University of California, San FranciscoSan FranciscoUnited States
| | - Henry van den Bedem
- Department of Bioengineering and Therapeutic Sciences, University of California, San FranciscoSan FranciscoUnited States
- Atomwise IncSan FranciscoUnited States
| | - Daniel A Keedy
- Structural Biology Initiative, CUNY Advanced Science Research CenterNew YorkUnited States
- Department of Chemistry and Biochemistry, City College of New YorkNew YorkUnited States
- Ph.D. Programs in Biochemistry, Biology and Chemistry, The Graduate Center, City University of New YorkNew YorkUnited States
| | - James S Fraser
- Department of Bioengineering and Therapeutic Sciences, University of California, San FranciscoSan FranciscoUnited States
| |
Collapse
|
6
|
Wankowicz SA, Ravikumar A, Sharma S, Riley BT, Raju A, Flowers J, Hogan D, van den Bedem H, Keedy DA, Fraser JS. Uncovering Protein Ensembles: Automated Multiconformer Model Building for X-ray Crystallography and Cryo-EM. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.06.28.546963. [PMID: 37425870 PMCID: PMC10327213 DOI: 10.1101/2023.06.28.546963] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/11/2023]
Abstract
In their folded state, biomolecules exchange between multiple conformational states that are crucial for their function. Traditional structural biology methods, such as X-ray crystallography and cryogenic electron microscopy (cryo-EM), produce density maps that are ensemble averages, reflecting molecules in various conformations. Yet, most models derived from these maps explicitly represent only a single conformation, overlooking the complexity of biomolecular structures. To accurately reflect the diversity of biomolecular forms, there is a pressing need to shift towards modeling structural ensembles that mirror the experimental data. However, the challenge of distinguishing signal from noise complicates manual efforts to create these models. In response, we introduce the latest enhancements to qFit, an automated computational strategy designed to incorporate protein conformational heterogeneity into models built into density maps. These algorithmic improvements in qFit are substantiated by superior R f r e e and geometry metrics across a wide range of proteins. Importantly, unlike more complex multicopy ensemble models, the multiconformer models produced by qFit can be manually modified in most major model building software (e.g. Coot) and fit can be further improved by refinement using standard pipelines (e.g. Phenix, Refmac, Buster). By reducing the barrier of creating multiconformer models, qFit can foster the development of new hypotheses about the relationship between macromolecular conformational dynamics and function.
Collapse
Affiliation(s)
- Stephanie A. Wankowicz
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, USA
| | - Ashraya Ravikumar
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, USA
| | - Shivani Sharma
- Structural Biology Initiative, CUNY Advanced Science Research Center, New York, NY 10031
- Ph.D. Program in Biology, The Graduate Center – City University of New York, New York, NY 10016
| | - Blake T. Riley
- Structural Biology Initiative, CUNY Advanced Science Research Center, New York, NY 10031
| | - Akshay Raju
- Structural Biology Initiative, CUNY Advanced Science Research Center, New York, NY 10031
| | - Jessica Flowers
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, USA
| | - Daniel Hogan
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, USA
| | - Henry van den Bedem
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, USA
- Atomwise, Inc., San Francisco, CA, United States
| | - Daniel A. Keedy
- Structural Biology Initiative, CUNY Advanced Science Research Center, New York, NY 10031
- Department of Chemistry and Biochemistry, City College of New York, New York, NY 10031
- Ph.D. Programs in Biochemistry, Biology, and Chemistry, The Graduate Center – City University of New York, New York, NY 10016
| | - James S. Fraser
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, USA
| |
Collapse
|
7
|
Liu YT, Fan H, Hu JJ, Zhou ZH. Overcoming the preferred orientation problem in cryoEM with self-supervised deep-learning. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.11.588921. [PMID: 38645074 PMCID: PMC11030451 DOI: 10.1101/2024.04.11.588921] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/23/2024]
Abstract
While advances in single-particle cryoEM have enabled the structural determination of macromolecular complexes at atomic resolution, particle orientation bias (the so-called "preferred" orientation problem) remains a complication for most specimens. Existing solutions have relied on biochemical and physical strategies applied to the specimen and are often complex and challenging. Here, we develop spIsoNet, an end-to-end self-supervised deep-learning-based software to address the preferred orientation problem. Using preferred-orientation views to recover molecular information in under-sampled views, spIsoNet improves both angular isotropy and particle alignment accuracy during 3D reconstruction. We demonstrate spIsoNet's capability of generating near-isotropic reconstructions from representative biological systems with limited views, including ribosomes, β-galactosidases, and a previously intractable hemagglutinin trimer dataset. spIsoNet can also be generalized to improve map isotropy and particle alignment of preferentially oriented molecules in subtomogram averaging. Therefore, without additional specimen-preparation procedures, spIsoNet provides a general computational solution to the preferred orientation problem.
Collapse
Affiliation(s)
- Yun-Tao Liu
- Department of Microbiology, Immunology, and Molecular Genetics, University of California, Los Angeles, CA, USA
- California NanoSystems Institute, University of California, Los Angeles, CA, USA
| | - Hongcheng Fan
- Department of Microbiology, Immunology, and Molecular Genetics, University of California, Los Angeles, CA, USA
- California NanoSystems Institute, University of California, Los Angeles, CA, USA
| | - Jason J. Hu
- Department of Microbiology, Immunology, and Molecular Genetics, University of California, Los Angeles, CA, USA
- California NanoSystems Institute, University of California, Los Angeles, CA, USA
- Current address: Department of Molecular and Cell Biology, University of California, Berkeley, CA, USA
| | - Z. Hong Zhou
- Department of Microbiology, Immunology, and Molecular Genetics, University of California, Los Angeles, CA, USA
- California NanoSystems Institute, University of California, Los Angeles, CA, USA
| |
Collapse
|
8
|
Sauer PV, Cupellini L, Sutter M, Bondanza M, Domínguez Martin MA, Kirst H, Bína D, Koh AF, Kotecha A, Greber BJ, Nogales E, Polívka7 T, Mennucci B, Kerfeld CA. Structural and quantum chemical basis for OCP-mediated quenching of phycobilisomes. SCIENCE ADVANCES 2024; 10:eadk7535. [PMID: 38578996 PMCID: PMC10997198 DOI: 10.1126/sciadv.adk7535] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/08/2023] [Accepted: 03/04/2024] [Indexed: 04/07/2024]
Abstract
Cyanobacteria use large antenna complexes called phycobilisomes (PBSs) for light harvesting. However, intense light triggers non-photochemical quenching, where the orange carotenoid protein (OCP) binds to PBS, dissipating excess energy as heat. The mechanism of efficiently transferring energy from phycocyanobilins in PBS to canthaxanthin in OCP remains insufficiently understood. Using cryo-electron microscopy, we unveiled the OCP-PBS complex structure at 1.6- to 2.1-angstrom resolution, showcasing its inherent flexibility. Using multiscale quantum chemistry, we disclosed the quenching mechanism. Identifying key protein residues, we clarified how canthaxanthin's transition dipole moment in its lowest-energy dark state becomes large enough for efficient energy transfer from phycocyanobilins. Our energy transfer model offers a detailed understanding of the atomic determinants of light harvesting regulation and antenna architecture in cyanobacteria.
Collapse
Affiliation(s)
- Paul V. Sauer
- California Institute for Quantitative Biosciences (QB3), University of California, Berkeley, CA 94720, USA
- Howard Hughes Medical Institute, University of California, Berkeley, CA 94720, USA
| | - Lorenzo Cupellini
- Dipartimento di Chimica e Chimica Industriale, Università di Pisa, Via G. Moruzzi 13, I-56124 Pisa, Italy
| | - Markus Sutter
- MSU-DOE Plant Research Laboratory, Michigan State University, East Lansing, MI 48824, USA
- Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
- Molecular Biophysics and Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - Mattia Bondanza
- Dipartimento di Chimica e Chimica Industriale, Università di Pisa, Via G. Moruzzi 13, I-56124 Pisa, Italy
| | - María Agustina Domínguez Martin
- MSU-DOE Plant Research Laboratory, Michigan State University, East Lansing, MI 48824, USA
- Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
- Molecular Biophysics and Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - Henning Kirst
- Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
- Molecular Biophysics and Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - David Bína
- Faculty of Science, University of South Bohemia, Ceske Budejovice, Czech Republic
- Biology Centre of the Czech Academy of Sciences, Ceske Budejovice, Czech Republic
| | | | | | - Basil J. Greber
- Division of Structural Biology, The Institute of Cancer Research, London SW7 3RP, UK
| | - Eva Nogales
- California Institute for Quantitative Biosciences (QB3), University of California, Berkeley, CA 94720, USA
- Howard Hughes Medical Institute, University of California, Berkeley, CA 94720, USA
- Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
- Department of Molecular and Cellular Biology, University of California, Berkeley, CA 94720, USA
| | - Tomáš Polívka7
- California Institute for Quantitative Biosciences (QB3), University of California, Berkeley, CA 94720, USA
- Howard Hughes Medical Institute, University of California, Berkeley, CA 94720, USA
- Dipartimento di Chimica e Chimica Industriale, Università di Pisa, Via G. Moruzzi 13, I-56124 Pisa, Italy
- MSU-DOE Plant Research Laboratory, Michigan State University, East Lansing, MI 48824, USA
- Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
- Molecular Biophysics and Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
- Faculty of Science, University of South Bohemia, Ceske Budejovice, Czech Republic
- Biology Centre of the Czech Academy of Sciences, Ceske Budejovice, Czech Republic
- Thermo Fisher Scientific, Eindhoven, Netherlands
- Division of Structural Biology, The Institute of Cancer Research, London SW7 3RP, UK
- Department of Molecular and Cellular Biology, University of California, Berkeley, CA 94720, USA
- Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, MI 48824, USA
| | - Benedetta Mennucci
- Dipartimento di Chimica e Chimica Industriale, Università di Pisa, Via G. Moruzzi 13, I-56124 Pisa, Italy
| | - Cheryl A. Kerfeld
- MSU-DOE Plant Research Laboratory, Michigan State University, East Lansing, MI 48824, USA
- Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
- Molecular Biophysics and Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
- Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, MI 48824, USA
| |
Collapse
|
9
|
Premageetha GT, Vinothkumar KR, Bose S. Exploring advances in single particle CryoEM with apoferritin: From blobs to true atomic resolution. Int J Biochem Cell Biol 2024; 169:106536. [PMID: 38307321 DOI: 10.1016/j.biocel.2024.106536] [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: 11/01/2023] [Revised: 01/21/2024] [Accepted: 01/23/2024] [Indexed: 02/04/2024]
Abstract
Deciphering the three-dimensional structures of macromolecules is of paramount importance for gaining insights into their functions and roles in human health and disease. Single particle cryoEM has emerged as a powerful technique that enables direct visualization of macromolecules and their complexes, and through subsequent averaging, achieve near atomic-level resolution. A major breakthrough was recently achieved with the determination of the apoferritin structure at true atomic resolution. In this review, we discuss the latest technological innovations across the entire single-particle workflow, which have been instrumental in driving the resolution revolution and in transforming cryoEM as a mainstream technique in structural biology. We illustrate these advancements using apoferritin as an example that has served as an excellent benchmark sample for assessing emerging technologies. We further explore whether the existing technology can routinely generate atomic structures of dynamic macromolecules that more accurately represent real-world samples, the limitations in the workflow, and the current approaches employed to overcome them.
Collapse
Affiliation(s)
- Gowtham ThambraRajan Premageetha
- Institute for Stem Cell Science and Regenerative Medicine, GKVK Post, Bangalore 560065, India; Manipal Academy of Higher Education, Tiger Circle Road, Manipal, Karnataka 576104, India.
| | - Kutti R Vinothkumar
- National Centre for Biological Sciences, Tata Institute of Fundamental Research, GKVK Post, Bangalore 560065, India
| | - Sucharita Bose
- Institute for Stem Cell Science and Regenerative Medicine, GKVK Post, Bangalore 560065, India.
| |
Collapse
|
10
|
Nogales E, Mahamid J. Bridging structural and cell biology with cryo-electron microscopy. Nature 2024; 628:47-56. [PMID: 38570716 PMCID: PMC11211576 DOI: 10.1038/s41586-024-07198-2] [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: 07/18/2023] [Accepted: 02/13/2024] [Indexed: 04/05/2024]
Abstract
Most life scientists would agree that understanding how cellular processes work requires structural knowledge about the macromolecules involved. For example, deciphering the double-helical nature of DNA revealed essential aspects of how genetic information is stored, copied and repaired. Yet, being reductionist in nature, structural biology requires the purification of large amounts of macromolecules, often trimmed off larger functional units. The advent of cryogenic electron microscopy (cryo-EM) greatly facilitated the study of large, functional complexes and generally of samples that are hard to express, purify and/or crystallize. Nevertheless, cryo-EM still requires purification and thus visualization outside of the natural context in which macromolecules operate and coexist. Conversely, cell biologists have been imaging cells using a number of fast-evolving techniques that keep expanding their spatial and temporal reach, but always far from the resolution at which chemistry can be understood. Thus, structural and cell biology provide complementary, yet unconnected visions of the inner workings of cells. Here we discuss how the interplay between cryo-EM and cryo-electron tomography, as a connecting bridge to visualize macromolecules in situ, holds great promise to create comprehensive structural depictions of macromolecules as they interact in complex mixtures or, ultimately, inside the cell itself.
Collapse
Affiliation(s)
- Eva Nogales
- Molecular and Cell Biology Department, Institute for Quantitative Biomedicine, University of California, Berkeley, CA, USA.
- Molecular Biophysics and Integrated Bioimaging, Lawrence Berkeley National Laboratory, Berkeley, CA, USA.
- Howard Hughes Medical Institute, Berkeley, CA, USA.
| | - Julia Mahamid
- Structural and Computational Biology Unit, European Molecular Biology Laboratory (EMBL), Heidelberg, Germany.
- Cell Biology and Biophysics Unit, European Molecular Biology Laboratory (EMBL), Heidelberg, Germany.
| |
Collapse
|
11
|
Cebi E, Lee J, Subramani VK, Bak N, Oh C, Kim KK. Cryo-electron microscopy-based drug design. Front Mol Biosci 2024; 11:1342179. [PMID: 38501110 PMCID: PMC10945328 DOI: 10.3389/fmolb.2024.1342179] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2023] [Accepted: 01/31/2024] [Indexed: 03/20/2024] Open
Abstract
Structure-based drug design (SBDD) has gained popularity owing to its ability to develop more potent drugs compared to conventional drug-discovery methods. The success of SBDD relies heavily on obtaining the three-dimensional structures of drug targets. X-ray crystallography is the primary method used for solving structures and aiding the SBDD workflow; however, it is not suitable for all targets. With the resolution revolution, enabling routine high-resolution reconstruction of structures, cryogenic electron microscopy (cryo-EM) has emerged as a promising alternative and has attracted increasing attention in SBDD. Cryo-EM offers various advantages over X-ray crystallography and can potentially replace X-ray crystallography in SBDD. To fully utilize cryo-EM in drug discovery, understanding the strengths and weaknesses of this technique and noting the key advancements in the field are crucial. This review provides an overview of the general workflow of cryo-EM in SBDD and highlights technical innovations that enable its application in drug design. Furthermore, the most recent achievements in the cryo-EM methodology for drug discovery are discussed, demonstrating the potential of this technique for advancing drug development. By understanding the capabilities and advancements of cryo-EM, researchers can leverage the benefits of designing more effective drugs. This review concludes with a discussion of the future perspectives of cryo-EM-based SBDD, emphasizing the role of this technique in driving innovations in drug discovery and development. The integration of cryo-EM into the drug design process holds great promise for accelerating the discovery of new and improved therapeutic agents to combat various diseases.
Collapse
Affiliation(s)
| | | | | | | | - Changsuk Oh
- Department of Precision Medicine, Sungkyunkwan University School of Medicine, Suwon, Republic of Korea
| | - Kyeong Kyu Kim
- Department of Precision Medicine, Sungkyunkwan University School of Medicine, Suwon, Republic of Korea
| |
Collapse
|
12
|
Lawson CL, Berman H, Chen L, Vallat B, Zirbel C. The Nucleic Acid Knowledgebase: a new portal for 3D structural information about nucleic acids. Nucleic Acids Res 2024; 52:D245-D254. [PMID: 37953312 PMCID: PMC10767938 DOI: 10.1093/nar/gkad957] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2023] [Revised: 10/02/2023] [Accepted: 10/16/2023] [Indexed: 11/14/2023] Open
Abstract
The Nucleic Acid Knowledgebase (nakb.org) is a new data resource, updated weekly, for experimentally determined 3D structures containing DNA and/or RNA nucleic acid polymers and their biological assemblies. NAKB indexes nucleic acid-containing structures derived from all major structure determination methods (X-ray, NMR and EM), including all held by the Protein Data Bank (PDB). As the planned successor to the Nucleic Acid Database (NDB), NAKB's design preserves all functionality of the NDB and provides novel nucleic acid-centric content, including structural and functional annotations, as well as annotations from and links to external resources. A variety of custom interactive tools have been developed to enable rapid exploration and drill-down of NAKB's content.
Collapse
Affiliation(s)
- Catherine L Lawson
- Institute for Quantitative Biomedicine, Rutgers, State University of New Jersey, 174 Frelinghuysen Road, Piscataway, NJ 08854, USA
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Helen M Berman
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Department of Chemistry and Chemical Biology, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA 90089, USA
| | - Li Chen
- Institute for Quantitative Biomedicine, Rutgers, State University of New Jersey, 174 Frelinghuysen Road, Piscataway, NJ 08854, USA
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Brinda Vallat
- Institute for Quantitative Biomedicine, Rutgers, State University of New Jersey, 174 Frelinghuysen Road, Piscataway, NJ 08854, USA
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Craig L Zirbel
- Department of Mathematics and Statistics, Bowling Green State University, Bowling Green, OH 43403, USA
| |
Collapse
|
13
|
Yang Q, Wu C, Zhu D, Li J, Cheng J, Zhang X. The reduction of FIB damage on cryo-lamella by lowering energy of ion beam revealed by a quantitative analysis. Structure 2023; 31:1275-1281.e4. [PMID: 37527655 DOI: 10.1016/j.str.2023.07.002] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2023] [Revised: 06/08/2023] [Accepted: 07/05/2023] [Indexed: 08/03/2023]
Abstract
Focused ion beam (FIB) is widely used for thinning frozen cells to produce lamellae for cryo-electron microscopy imaging and for protein structures study in vivo. However, FIB damages the lamellae and a quantitative experimental analysis of the damage is lacking. We used a 30-keV gallium FIB to prepare lamellae of a highly concentrated icosahedral virus sample. The viruses were grouped according to their distance from the surface of lamellae and reconstructed. Damage to the approximately 20-nm-thick outermost lamella surface was similar to that from exposure to 16 e-/Å2 in a 300-kV cryo-electron microscope at high-resolution range. The damage was negligible at a depth beyond 50 nm, which was reduced to 30 nm if 8-keV Ga+ was used during polishing. We designed extra steps in the reconstruction refinement to maximize undamaged signals and increase the resolution. The results demonstrated that low-energy beam polishing was essential for high-quality thinner lamellae.
Collapse
Affiliation(s)
- Qi Yang
- National Laboratory of Biomacromolecules, CAS Center for Excellence in Biomacromolecules, Institute of Biophysics, Chinese Academy of Sciences (CAS), Beijing 100101, P.R. China; University of Chinese Academy of Sciences, Beijing 100049, P.R. China
| | - Chunling Wu
- National Laboratory of Biomacromolecules, CAS Center for Excellence in Biomacromolecules, Institute of Biophysics, Chinese Academy of Sciences (CAS), Beijing 100101, P.R. China
| | - Dongjie Zhu
- National Laboratory of Biomacromolecules, CAS Center for Excellence in Biomacromolecules, Institute of Biophysics, Chinese Academy of Sciences (CAS), Beijing 100101, P.R. China
| | - Junxi Li
- National Laboratory of Biomacromolecules, CAS Center for Excellence in Biomacromolecules, Institute of Biophysics, Chinese Academy of Sciences (CAS), Beijing 100101, P.R. China; University of Chinese Academy of Sciences, Beijing 100049, P.R. China
| | - Jing Cheng
- National Laboratory of Biomacromolecules, CAS Center for Excellence in Biomacromolecules, Institute of Biophysics, Chinese Academy of Sciences (CAS), Beijing 100101, P.R. China
| | - Xinzheng Zhang
- National Laboratory of Biomacromolecules, CAS Center for Excellence in Biomacromolecules, Institute of Biophysics, Chinese Academy of Sciences (CAS), Beijing 100101, P.R. China.
| |
Collapse
|
14
|
Westhof E, Watson ZL, Zirbel CL, Cate JHD. Anionic G•U pairs in bacterial ribosomal rRNAs. RNA (NEW YORK, N.Y.) 2023; 29:1069-1076. [PMID: 37068913 DOI: 10.1261/rna.079583.123] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/08/2023] [Accepted: 04/05/2023] [Indexed: 06/18/2023]
Abstract
Wobble GU pairs (or G•U) occur frequently within double-stranded RNA helices interspersed between standard G=C and A-U Watson-Crick pairs. Another type of G•U pair interacting via their Watson-Crick edges has been observed in the A site of ribosome structures between a modified U34 in the tRNA anticodon triplet and G + 3 in the mRNA. In such pairs, the electronic structure of the U is changed with a negative charge on N3(U), resulting in two H-bonds between N1(G)…O4(U) and N2(G)…N3(U). Here, we report that such pairs occur in other highly conserved positions in ribosomal RNAs of bacteria in the absence of U modification. An anionic cis Watson-Crick G•G pair is also observed and well conserved in the small subunit. These pairs are observed in tightly folded regions.
Collapse
Affiliation(s)
- Eric Westhof
- Architecture et Réactivité de l'ARN, Université de Strasbourg, Institut de biologie moléculaire et cellulaire du CNRS, F-67084 Strasbourg, France
| | - Zoe L Watson
- Department of Chemistry, University of California, Berkeley, California 94720, USA
- California Institute for Quantitative Biosciences (QB3), University of California, Berkeley, California 94720, USA
| | - Craig L Zirbel
- Department of Mathematics and Statistics, Bowling Green State University, Bowling Green, Ohio 43403, USA
| | - Jamie H D Cate
- Department of Chemistry, University of California, Berkeley, California 94720, USA
- Department of Molecular and Cell Biology, University of California, Berkeley, California 94720, USA
- Molecular Biophysics and Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, California 94720, USA
- Innovative Genomics Institute, University of California, Berkeley, California 94720, USA
| |
Collapse
|
15
|
Powell BM, Davis JH. Learning structural heterogeneity from cryo-electron sub-tomograms with tomoDRGN. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.05.31.542975. [PMID: 37398315 PMCID: PMC10312494 DOI: 10.1101/2023.05.31.542975] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/04/2023]
Abstract
Cryo-electron tomography (cryo-ET) allows one to observe macromolecular complexes in their native, spatially contextualized environment. Tools to visualize such complexes at nanometer resolution via iterative alignment and averaging are well-developed but rely on assumptions of structural homogeneity among the complexes under consideration. Recently developed downstream analysis tools allow for some assessment of macromolecular diversity but have limited capacity to represent highly heterogeneous macromolecules, including those undergoing continuous conformational changes. Here, we extend the highly expressive cryoDRGN deep learning architecture, originally created for cryo-electron microscopy single particle analysis, to sub-tomograms. Our new tool, tomoDRGN, learns a continuous low-dimensional representation of structural heterogeneity in cryo-ET datasets while also learning to reconstruct a large, heterogeneous ensemble of structures supported by the underlying data. Using simulated and experimental data, we describe and benchmark architectural choices within tomoDRGN that are uniquely necessitated and enabled by cryo-ET data. We additionally illustrate tomoDRGN's efficacy in analyzing an exemplar dataset, using it to reveal extensive structural heterogeneity among ribosomes imaged in situ.
Collapse
Affiliation(s)
- Barrett M. Powell
- Department of Biology, Massachusetts Institute of Technology, Cambridge, MA 02139
| | - Joseph H. Davis
- Department of Biology, Massachusetts Institute of Technology, Cambridge, MA 02139
- Program in Computational and Systems Biology, Massachusetts Institute of Technology, Cambridge, MA 02139
| |
Collapse
|