1
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Dahmani Z, Scott AL, Vénien-Bryan C, Perahia D, Costa MG. MDFF_NM: Improved Molecular Dynamics Flexible Fitting into Cryo-EM Density Maps with a Multireplica Normal Mode-Based Search. J Chem Inf Model 2024; 64:5151-5160. [PMID: 38907694 PMCID: PMC11234365 DOI: 10.1021/acs.jcim.3c02007] [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: 01/03/2024] [Revised: 06/17/2024] [Accepted: 06/17/2024] [Indexed: 06/24/2024]
Abstract
Molecular Dynamics Flexible Fitting (MDFF) is a widely used tool to refine high-resolution structures into cryo-EM density maps. Despite many successful applications, MDFF is still limited by its high computational cost, overfitting, accuracy, and performance issues due to entrapment within wrong local minima. Modern ensemble-based MDFF tools have generated promising results in the past decade. In line with these studies, we present MDFF_NM, a stochastic hybrid flexible fitting algorithm combining Normal Mode Analysis (NMA) and simulation-based flexible fitting. Initial tests reveal that, besides accelerating the fitting process, MDFF_NM increases the diversity of fitting routes leading to the target, uncovering ensembles of conformations in closer agreement with experimental data. The potential integration of MDFF_NM with other existing methods and integrative modeling pipelines is also discussed.
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Affiliation(s)
- Zakaria
L. Dahmani
- School
of Medicine, Department of Computational and Systems Biology, University of Pittsburgh, 800 Murdoch I Bldg, 3420 Forbes Avenue, Pittsburgh, Pennsylvania 15260, United States
- UMR
7590, CNRS, Museum National d’Histoire Naturelle, Institut
de Minéralogie, Physique des Matériaux et Cosmochimie,
IMPMC, Sorbonne Université, 4 place Jussieu, Paris 75005, France
| | - Ana Ligia Scott
- CMCC,
Computational Biophysics and Biology, Universidade Federal do ABC, Avenida dos Estados 5001, São Paulo, Santo André 09210-580, Brazil
- Université
de Strasbourg—IGBMC—Departament de Biologie structurale
integrative, 1 rue Laurent
Fries BP, Illkirch 10142
67404, CEDEX, France
| | - Catherine Vénien-Bryan
- UMR
7590, CNRS, Museum National d’Histoire Naturelle, Institut
de Minéralogie, Physique des Matériaux et Cosmochimie,
IMPMC, Sorbonne Université, 4 place Jussieu, Paris 75005, France
| | - David Perahia
- Laboratoire
de Biologie et Pharmacologie Appliquée, UMR 8113, École
Normale Supérieure Paris-Saclay, Gif-sur-Yvette 91190, France
| | - Mauricio G.S Costa
- UMR
7590, CNRS, Museum National d’Histoire Naturelle, Institut
de Minéralogie, Physique des Matériaux et Cosmochimie,
IMPMC, Sorbonne Université, 4 place Jussieu, Paris 75005, France
- Laboratoire
de Biologie et Pharmacologie Appliquée, UMR 8113, École
Normale Supérieure Paris-Saclay, Gif-sur-Yvette 91190, France
- Programa de Computação Científica,
Vice-Presidência de Educação, Informação
e Comunicação, Fundação Oswaldo Cruz, Av.Brasil 4365, Residência
Oficial, Manguinhos, Rio de Janeiro 21040-900, Brazil
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2
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Lawson CL, Kryshtafovych A, Pintilie GD, Burley SK, Černý J, Chen VB, Emsley P, Gobbi A, Joachimiak A, Noreng S, Prisant MG, Read RJ, Richardson JS, Rohou AL, Schneider B, Sellers BD, Shao C, Sourial E, Williams CI, Williams CJ, Yang Y, Abbaraju V, Afonine PV, Baker ML, Bond PS, Blundell TL, Burnley T, Campbell A, Cao R, Cheng J, Chojnowski G, Cowtan KD, DiMaio F, Esmaeeli R, Giri N, Grubmüller H, Hoh SW, Hou J, Hryc CF, Hunte C, Igaev M, Joseph AP, Kao WC, Kihara D, Kumar D, Lang L, Lin S, Maddhuri Venkata Subramaniya SR, Mittal S, Mondal A, Moriarty NW, Muenks A, Murshudov GN, Nicholls RA, Olek M, Palmer CM, Perez A, Pohjolainen E, Pothula KR, Rowley CN, Sarkar D, Schäfer LU, Schlicksup CJ, Schröder GF, Shekhar M, Si D, Singharoy A, Sobolev OV, Terashi G, Vaiana AC, Vedithi SC, Verburgt J, Wang X, Warshamanage R, Winn MD, Weyand S, Yamashita K, Zhao M, Schmid MF, Berman HM, Chiu W. Outcomes of the EMDataResource cryo-EM Ligand Modeling Challenge. Nat Methods 2024; 21:1340-1348. [PMID: 38918604 DOI: 10.1038/s41592-024-02321-7] [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: 01/14/2024] [Accepted: 05/24/2024] [Indexed: 06/27/2024]
Abstract
The EMDataResource Ligand Model Challenge aimed to assess the reliability and reproducibility of modeling ligands bound to protein and protein-nucleic acid complexes in cryogenic electron microscopy (cryo-EM) maps determined at near-atomic (1.9-2.5 Å) resolution. Three published maps were selected as targets: Escherichia coli beta-galactosidase with inhibitor, SARS-CoV-2 virus RNA-dependent RNA polymerase with covalently bound nucleotide analog and SARS-CoV-2 virus ion channel ORF3a with bound lipid. Sixty-one models were submitted from 17 independent research groups, each with supporting workflow details. The quality of submitted ligand models and surrounding atoms were analyzed by visual inspection and quantification of local map quality, model-to-map fit, geometry, energetics and contact scores. A composite rather than a single score was needed to assess macromolecule+ligand model quality. These observations lead us to recommend best practices for assessing cryo-EM structures of liganded macromolecules reported at near-atomic resolution.
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Affiliation(s)
- Catherine L Lawson
- RCSB Protein Data Bank and Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ, USA.
| | | | - Grigore D Pintilie
- Departments of Bioengineering and of Microbiology and Immunology, Stanford University, Stanford, CA, USA
| | - Stephen K Burley
- RCSB Protein Data Bank and Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ, USA
- Department of Chemistry and Chemical Biology, Rutgers, The State University of New Jersey, Piscataway, NJ, USA
- Rutgers Cancer Institute of New Jersey, Rutgers, The State University of New Jersey, New Brunswick, NJ, USA
- RCSB Protein Data Bank and San Diego Supercomputer Center, University of California San Diego, La Jolla, CA, USA
| | - Jiří Černý
- Institute of Biotechnology, Czech Academy of Sciences, Vestec, Czech Republic
| | - Vincent B Chen
- Department of Biochemistry, Duke University, Durham, NC, USA
| | - Paul Emsley
- MRC Laboratory of Molecular Biology, Cambridge, UK
| | - Alberto Gobbi
- Discovery Chemistry, Genentech Inc., San Francisco, CA, USA
- , Berlin, Germany
| | - Andrzej Joachimiak
- Structural Biology Center, X-ray Science Division, Argonne National Laboratory, Argonne, IL, USA
- Department of Biochemistry and Molecular Biology, University of Chicago, Chicago, IL, USA
| | - Sigrid Noreng
- Structural Biology, Genentech Inc., South San Francisco, CA, USA
- Protein Science, Septerna, South San Francisco, CA, USA
| | | | - Randy J Read
- Department of Haematology, Cambridge Institute for Medical Research, University of Cambridge, Cambridge, UK
| | | | - Alexis L Rohou
- Structural Biology, Genentech Inc., South San Francisco, CA, USA
| | - Bohdan Schneider
- Institute of Biotechnology, Czech Academy of Sciences, Vestec, Czech Republic
| | - Benjamin D Sellers
- Discovery Chemistry, Genentech Inc., San Francisco, CA, USA
- Computational Chemistry, Vilya, South San Francisco, CA, USA
| | - Chenghua Shao
- RCSB Protein Data Bank and Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ, USA
| | | | | | | | - Ying Yang
- Structural Biology, Genentech Inc., South San Francisco, CA, USA
| | - Venkat Abbaraju
- RCSB Protein Data Bank and Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ, USA
| | - Pavel V Afonine
- Molecular Biophysics and Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Matthew L Baker
- Department of Biochemistry and Molecular Biology, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Paul S Bond
- York Structural Biology Laboratory, Department of Chemistry, University of York, York, UK
| | - Tom L Blundell
- Department of Biochemistry, University of Cambridge, Cambridge, UK
| | - Tom Burnley
- Scientific Computing Department, UKRI Science and Technology Facilities Council, Research Complex at Harwell, Didcot, UK
| | - Arthur Campbell
- Center for Development of Therapeutics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Renzhi Cao
- Department of Computer Science, Pacific Lutheran University, Tacoma, WA, USA
| | - Jianlin Cheng
- Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO, USA
| | | | - K D Cowtan
- York Structural Biology Laboratory, Department of Chemistry, University of York, York, UK
| | - Frank DiMaio
- Department of Biochemistry and Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Reza Esmaeeli
- Department of Chemistry and Quantum Theory Project, University of Florida, Gainesville, FL, USA
| | - Nabin Giri
- Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO, USA
| | - Helmut Grubmüller
- Theoretical and Computational Biophysics Department, Max Planck Institute for Multidisciplinary Sciences, Göttingen, Germany
| | - Soon Wen Hoh
- York Structural Biology Laboratory, Department of Chemistry, University of York, York, UK
| | - Jie Hou
- Department of Computer Science, Saint Louis University, St. Louis, MO, USA
| | - Corey F Hryc
- Department of Biochemistry and Molecular Biology, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Carola Hunte
- Institute of Biochemistry and Molecular Biology, ZBMZ, Faculty of Medicine and CIBSS-Centre for Integrative Biological Signalling Studies, University of Freiburg, Freiburg, Germany
| | - Maxim Igaev
- Theoretical and Computational Biophysics Department, Max Planck Institute for Multidisciplinary Sciences, Göttingen, Germany
| | - Agnel P Joseph
- Scientific Computing Department, UKRI Science and Technology Facilities Council, Research Complex at Harwell, Didcot, UK
| | - Wei-Chun Kao
- Institute of Biochemistry and Molecular Biology, ZBMZ, Faculty of Medicine and CIBSS-Centre for Integrative Biological Signalling Studies, University of Freiburg, Freiburg, Germany
| | - Daisuke Kihara
- Department of Biological Sciences, Purdue University, West Lafayette, IN, USA
- Department of Computer Science, Purdue University, West Lafayette, IN, USA
| | - Dilip Kumar
- Verna and Marrs McLean Department of Biochemistry and Molecular Biology, Baylor College of Medicine, Houston, TX, USA
- Trivedi School of Biosciences, Ashoka University, Sonipat, India
| | - Lijun Lang
- Department of Chemistry and Quantum Theory Project, University of Florida, Gainesville, FL, USA
- The Chinese University of Hong Kong, Hong Kong, China
| | - Sean Lin
- Division of Computing & Software Systems, University of Washington, Bothell, WA, USA
| | | | - Sumit Mittal
- Biodesign Institute, Arizona State University, Tempe, AZ, USA
- School of Advanced Sciences and Languages, VIT Bhopal University, Bhopal, India
| | - Arup Mondal
- Department of Chemistry and Quantum Theory Project, University of Florida, Gainesville, FL, USA
- National Renewable Energy Laboratory (NREL), Golden, CO, USA
| | - Nigel W Moriarty
- Molecular Biophysics and Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Andrew Muenks
- Department of Biochemistry and Institute for Protein Design, University of Washington, Seattle, WA, USA
| | | | - Robert A Nicholls
- MRC Laboratory of Molecular Biology, Cambridge, UK
- Scientific Computing Department, UKRI Science and Technology Facilities Council, Research Complex at Harwell, Didcot, UK
| | - Mateusz Olek
- York Structural Biology Laboratory, Department of Chemistry, University of York, York, UK
- Electron Bio-Imaging Centre, Diamond Light Source, Harwell Science and Innovation Campus, Didcot, UK
| | - Colin M Palmer
- Scientific Computing Department, UKRI Science and Technology Facilities Council, Research Complex at Harwell, Didcot, UK
| | - Alberto Perez
- Department of Chemistry and Quantum Theory Project, University of Florida, Gainesville, FL, USA
| | - Emmi Pohjolainen
- Theoretical and Computational Biophysics Department, Max Planck Institute for Multidisciplinary Sciences, Göttingen, Germany
| | - Karunakar R Pothula
- Institute of Biological Information Processing (IBI-7, Structural Biochemistry) and Jülich Centre for Structural Biology (JuStruct), Forschungszentrum Jülich, Jülich, Germany
| | | | - Daipayan Sarkar
- Department of Biological Sciences, Purdue University, West Lafayette, IN, USA
- Biodesign Institute, Arizona State University, Tempe, AZ, USA
- MSU-DOE Plant Research Laboratory, East Lansing, MI, USA
- School of Molecular Sciences, Arizona State University, Tempe, AZ, USA
| | - Luisa U Schäfer
- Institute of Biological Information Processing (IBI-7, Structural Biochemistry) and Jülich Centre for Structural Biology (JuStruct), Forschungszentrum Jülich, Jülich, Germany
| | - Christopher J Schlicksup
- Molecular Biophysics and Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Gunnar F Schröder
- Institute of Biological Information Processing (IBI-7, Structural Biochemistry) and Jülich Centre for Structural Biology (JuStruct), Forschungszentrum Jülich, Jülich, Germany
- Physics Department, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Mrinal Shekhar
- Center for Development of Therapeutics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Biodesign Institute, Arizona State University, Tempe, AZ, USA
| | - Dong Si
- Division of Computing & Software Systems, University of Washington, Bothell, WA, USA
| | | | - Oleg V Sobolev
- Molecular Biophysics and Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Genki Terashi
- Department of Biological Sciences, Purdue University, West Lafayette, IN, USA
| | - Andrea C Vaiana
- Theoretical and Computational Biophysics Department, Max Planck Institute for Multidisciplinary Sciences, Göttingen, Germany
- Nature's Toolbox (NTx), Rio Rancho, NM, USA
| | | | - Jacob Verburgt
- Department of Biological Sciences, Purdue University, West Lafayette, IN, USA
| | - Xiao Wang
- Department of Computer Science, Purdue University, West Lafayette, IN, USA
| | | | - Martyn D Winn
- Scientific Computing Department, UKRI Science and Technology Facilities Council, Research Complex at Harwell, Didcot, UK
| | - Simone Weyand
- Department of Biochemistry, University of Cambridge, Cambridge, UK
| | | | - Minglei Zhao
- Department of Biochemistry and Molecular Biology, University of Chicago, Chicago, IL, USA
| | - Michael F Schmid
- Division of Cryo-EM and Bioimaging, SSRL, SLAC National Accelerator Laboratory, Menlo Park, CA, USA
| | - Helen M Berman
- Department of Chemistry and Chemical Biology, Rutgers, The State University of New Jersey, Piscataway, NJ, USA
- Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA, USA
| | - Wah Chiu
- Departments of Bioengineering and of Microbiology and Immunology, Stanford University, Stanford, CA, USA.
- Division of Cryo-EM and Bioimaging, SSRL, SLAC National Accelerator Laboratory, Menlo Park, CA, USA.
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3
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Bock LV, Igaev M, Grubmüller H. Single-particle Cryo-EM and molecular dynamics simulations: A perfect match. Curr Opin Struct Biol 2024; 86:102825. [PMID: 38723560 DOI: 10.1016/j.sbi.2024.102825] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2024] [Revised: 04/08/2024] [Accepted: 04/09/2024] [Indexed: 05/19/2024]
Abstract
Knowledge of the structure and dynamics of biomolecules is key to understanding the mechanisms underlying their biological functions. Single-particle cryo-electron microscopy (cryo-EM) is a powerful structural biology technique to characterize complex biomolecular systems. Here, we review recent advances of how Molecular Dynamics (MD) simulations are being used to increase and enhance the information extracted from cryo-EM experiments. We will particularly focus on the physics underlying these experiments, how MD facilitates structure refinement, in particular for heterogeneous and non-isotropic resolution, and how thermodynamic and kinetic information can be extracted from cryo-EM data.
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Affiliation(s)
- Lars V Bock
- Theoretical and Computational Biophysics Department, Max Planck Institute for Multidisciplinary Sciences, Am Fassberg 11, Göttingen, 37077, Germany. https://twitter.com/Pogoscience
| | - Maxim Igaev
- Theoretical and Computational Biophysics Department, Max Planck Institute for Multidisciplinary Sciences, Am Fassberg 11, Göttingen, 37077, Germany. https://twitter.com/maxotubule
| | - Helmut Grubmüller
- Theoretical and Computational Biophysics Department, Max Planck Institute for Multidisciplinary Sciences, Am Fassberg 11, Göttingen, 37077, Germany.
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4
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Lawson CL, Kryshtafovych A, Pintilie GD, Burley SK, Černý J, Chen VB, Emsley P, Gobbi A, Joachimiak A, Noreng S, Prisant M, Read RJ, Richardson JS, Rohou AL, Schneider B, Sellers BD, Shao C, Sourial E, Williams CI, Williams CJ, Yang Y, Abbaraju V, Afonine PV, Baker ML, Bond PS, Blundell TL, Burnley T, Campbell A, Cao R, Cheng J, Chojnowski G, Cowtan KD, DiMaio F, Esmaeeli R, Giri N, Grubmüller H, Hoh SW, Hou J, Hryc CF, Hunte C, Igaev M, Joseph AP, Kao WC, Kihara D, Kumar D, Lang L, Lin S, Maddhuri Venkata Subramaniya SR, Mittal S, Mondal A, Moriarty NW, Muenks A, Murshudov GN, Nicholls RA, Olek M, Palmer CM, Perez A, Pohjolainen E, Pothula KR, Rowley CN, Sarkar D, Schäfer LU, Schlicksup CJ, Schröder GF, Shekhar M, Si D, Singharoy A, Sobolev OV, Terashi G, Vaiana AC, Vedithi SC, Verburgt J, Wang X, Warshamanage R, Winn MD, Weyand S, Yamashita K, Zhao M, Schmid MF, Berman HM, Chiu W. Outcomes of the EMDataResource Cryo-EM Ligand Modeling Challenge. RESEARCH SQUARE 2024:rs.3.rs-3864137. [PMID: 38343795 PMCID: PMC10854310 DOI: 10.21203/rs.3.rs-3864137/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/18/2024]
Abstract
The EMDataResource Ligand Model Challenge aimed to assess the reliability and reproducibility of modeling ligands bound to protein and protein/nucleic-acid complexes in cryogenic electron microscopy (cryo-EM) maps determined at near-atomic (1.9-2.5 Å) resolution. Three published maps were selected as targets: E. coli beta-galactosidase with inhibitor, SARS-CoV-2 RNA-dependent RNA polymerase with covalently bound nucleotide analog, and SARS-CoV-2 ion channel ORF3a with bound lipid. Sixty-one models were submitted from 17 independent research groups, each with supporting workflow details. We found that (1) the quality of submitted ligand models and surrounding atoms varied, as judged by visual inspection and quantification of local map quality, model-to-map fit, geometry, energetics, and contact scores, and (2) a composite rather than a single score was needed to assess macromolecule+ligand model quality. These observations lead us to recommend best practices for assessing cryo-EM structures of liganded macromolecules reported at near-atomic resolution.
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Affiliation(s)
- Catherine L. Lawson
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ, USA
| | | | - Grigore D. Pintilie
- Departments of Bioengineering and of Microbiology and Immunology, Stanford University, Stanford, CA, USA
| | - Stephen K. Burley
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ, USA
- Department of Chemistry and Chemical Biology, Rutgers, The State University of New Jersey, Piscataway, NJ, USA
- Rutgers Cancer Institute of New Jersey, Rutgers, The State University of New Jersey, New Brunswick, NJ USA
- San Diego Supercomputer Center, University of California San Diego, La Jolla, CA USA
| | - Jiří Černý
- Institute of Biotechnology, Czech Academy of Sciences, Vestec, CZ
| | | | - Paul Emsley
- MRC Laboratory of Molecular Biology, Cambridge, UK
| | - Alberto Gobbi
- Discovery Chemistry, Genentech Inc, South San Francisco, USA
| | - Andrzej Joachimiak
- Structural Biology Center, X-ray Science Division, Argonne National Laboratory, Argonne, IL, USA
| | - Sigrid Noreng
- Structural Biology, Genentech Inc, South San Francisco, USA
| | | | - Randy J. Read
- Department of Haematology, Cambridge Institute for Medical Research, University of Cambridge, Cambridge, UK
| | | | | | - Bohdan Schneider
- Institute of Biotechnology, Czech Academy of Sciences, Vestec, CZ
| | | | - Chenghua Shao
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ, USA
| | | | | | | | - Ying Yang
- Structural Biology, Genentech Inc, South San Francisco, USA
| | - Venkat Abbaraju
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ, USA
| | - Pavel V. Afonine
- Molecular Biophysics and Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Matthew L. Baker
- Department of Biochemistry and Molecular Biology, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Paul S. Bond
- York Structural Biology Laboratory, Department of Chemistry, University of York, York, UK
| | - Tom L. Blundell
- Department of Biochemistry, University of Cambridge, Cambridge, UK
| | - Tom Burnley
- Scientific Computing Department, UKRI Science and Technology Facilities Council, Research Complex at Harwell, Didcot, UK
| | - Arthur Campbell
- Center for Development of Therapeutics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Renzhi Cao
- Department of Computer Science, Pacific Lutheran University, Tacoma, WA, USA
| | - Jianlin Cheng
- Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO, USA
| | | | - Kevin D. Cowtan
- York Structural Biology Laboratory, Department of Chemistry, University of York, York, UK
| | - Frank DiMaio
- Department of Biochemistry and Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Reza Esmaeeli
- Department of Chemistry and Quantum Theory Project, University of Florida, Gainesville, FL, USA
| | - Nabin Giri
- Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO, USA
| | - Helmut Grubmüller
- Theoretical and Computational Biophysics Department, Max Planck Institute for Multidisciplinary Sciences, Göttingen, Germany
| | - Soon Wen Hoh
- York Structural Biology Laboratory, Department of Chemistry, University of York, York, UK
| | - Jie Hou
- Department of Computer Science, Saint Louis University, St. Louis, MO, USA
| | - Corey F. Hryc
- Department of Biochemistry and Molecular Biology, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Carola Hunte
- Institute of Biochemistry and Molecular Biology, ZBMZ, Faculty of Medicine and CIBSS - Centre for Integrative Biological Signalling Studies, University of Freiburg, 79104 Freiburg, Germany
| | - Maxim Igaev
- Theoretical and Computational Biophysics Department, Max Planck Institute for Multidisciplinary Sciences, Göttingen, Germany
| | - Agnel P. Joseph
- Scientific Computing Department, UKRI Science and Technology Facilities Council, Research Complex at Harwell, Didcot, UK
| | - Wei-Chun Kao
- Institute of Biochemistry and Molecular Biology, ZBMZ, Faculty of Medicine and CIBSS - Centre for Integrative Biological Signalling Studies, University of Freiburg, 79104 Freiburg, Germany
| | - Daisuke Kihara
- Department of Biological Sciences, Purdue University, West Lafayette, IN, USA
- Department of Computer Science, Purdue University, West Lafayette, IN, USA
| | - Dilip Kumar
- Verna and Marrs McLean Department of Biochemistry and Molecular Biology, Baylor College of Medicine, Houston, TX, USA
| | - Lijun Lang
- Department of Chemistry and Quantum Theory Project, University of Florida, Gainesville, FL, USA
| | - Sean Lin
- Division of Computing & Software Systems, University of Washington, Bothell, WA, USA
| | | | - Sumit Mittal
- Biodesign Institute, Arizona State University, Tempe, AZ, USA
- School of Advanced Sciences and Languages, VIT Bhopal University, Bhopal, India
| | - Arup Mondal
- Department of Chemistry and Quantum Theory Project, University of Florida, Gainesville, FL, USA
| | - Nigel W. Moriarty
- Molecular Biophysics and Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Andrew Muenks
- Department of Biochemistry and Institute for Protein Design, University of Washington, Seattle, WA, USA
| | | | | | - Mateusz Olek
- York Structural Biology Laboratory, Department of Chemistry, University of York, York, UK
- Electron Bio-Imaging Centre, Diamond Light Source, Harwell Science and Innovation Campus, Didcot, UK
| | - Colin M. Palmer
- Scientific Computing Department, UKRI Science and Technology Facilities Council, Research Complex at Harwell, Didcot, UK
| | - Alberto Perez
- Department of Chemistry and Quantum Theory Project, University of Florida, Gainesville, FL, USA
| | - Emmi Pohjolainen
- Theoretical and Computational Biophysics Department, Max Planck Institute for Multidisciplinary Sciences, Göttingen, Germany
| | - Karunakar R. Pothula
- Institute of Biological Information Processing (IBI-7: Structural Biochemistry) and Jülich Centre for Structural Biology (JuStruct), Forschungszentrum Jülich, Jülich, Germany
| | | | - Daipayan Sarkar
- Department of Biological Sciences, Purdue University, West Lafayette, IN, USA
- Biodesign Institute, Arizona State University, Tempe, AZ, USA
| | - Luisa U. Schäfer
- Institute of Biological Information Processing (IBI-7: Structural Biochemistry) and Jülich Centre for Structural Biology (JuStruct), Forschungszentrum Jülich, Jülich, Germany
| | - Christopher J. Schlicksup
- Molecular Biophysics and Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Gunnar F. Schröder
- Institute of Biological Information Processing (IBI-7: Structural Biochemistry) and Jülich Centre for Structural Biology (JuStruct), Forschungszentrum Jülich, Jülich, Germany
- Physics Department, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Mrinal Shekhar
- Center for Development of Therapeutics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Biodesign Institute, Arizona State University, Tempe, AZ, USA
| | - Dong Si
- Division of Computing & Software Systems, University of Washington, Bothell, WA, USA
| | | | - Oleg V. Sobolev
- Molecular Biophysics and Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Genki Terashi
- Department of Biological Sciences, Purdue University, West Lafayette, IN, USA
| | - Andrea C. Vaiana
- Theoretical and Computational Biophysics Department, Max Planck Institute for Multidisciplinary Sciences, Göttingen, Germany
- Nature’s Toolbox (NTx), Rio Rancho, NM, USA
| | | | - Jacob Verburgt
- Department of Biological Sciences, Purdue University, West Lafayette, IN, USA
| | - Xiao Wang
- Department of Biological Sciences, Purdue University, West Lafayette, IN, USA
| | | | - Martyn D. Winn
- Scientific Computing Department, UKRI Science and Technology Facilities Council, Research Complex at Harwell, Didcot, UK
| | - Simone Weyand
- Department of Biochemistry, University of Cambridge, Cambridge, UK
| | | | - Minglei Zhao
- Department of Biochemistry and Molecular Biology, University of Chicago, Chicago, IL, USA
| | - Michael F. Schmid
- Division of Cryo-EM and Bioimaging, SSRL, SLAC National Accelerator Laboratory, Menlo Park, CA, USA
| | - Helen M. Berman
- Department of Chemistry and Chemical Biology, Rutgers, The State University of New Jersey, Piscataway, NJ, USA
- Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA 90089, USA
| | - Wah Chiu
- Departments of Bioengineering and of Microbiology and Immunology, Stanford University, Stanford, CA, USA
- Division of Cryo-EM and Bioimaging, SSRL, SLAC National Accelerator Laboratory, Menlo Park, CA, USA
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5
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Beton JG, Mulvaney T, Cragnolini T, Topf M. Cryo-EM structure and B-factor refinement with ensemble representation. Nat Commun 2024; 15:444. [PMID: 38200043 PMCID: PMC10781738 DOI: 10.1038/s41467-023-44593-1] [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: 07/11/2022] [Accepted: 12/20/2023] [Indexed: 01/12/2024] Open
Abstract
Cryo-EM experiments produce images of macromolecular assemblies that are combined to produce three-dimensional density maps. Typically, atomic models of the constituent molecules are fitted into these maps, followed by a density-guided refinement. We introduce TEMPy-ReFF, a method for atomic structure refinement in cryo-EM density maps. Our method represents atomic positions as components of a Gaussian mixture model, utilising their variances as B-factors, which are used to derive an ensemble description. Extensively tested on a substantial dataset of 229 cryo-EM maps from EMDB ranging in resolution from 2.1-4.9 Å with corresponding PDB and CERES atomic models, our results demonstrate that TEMPy-ReFF ensembles provide a superior representation of cryo-EM maps. On a single-model basis, it performs similarly to the CERES re-refinement protocol, although there are cases where it provides a better fit to the map. Furthermore, our method enables the creation of composite maps free of boundary artefacts. TEMPy-ReFF is useful for better interpretation of flexible structures, such as those involving RNA, DNA or ligands.
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Affiliation(s)
- Joseph G Beton
- Leibniz Institute of Virology (LIV) and Universitätsklinikum Hamburg Eppendorf (UKE), Centre for Structural Systems Biology (CSSB), 22607, Hamburg, Germany
| | - Thomas Mulvaney
- Leibniz Institute of Virology (LIV) and Universitätsklinikum Hamburg Eppendorf (UKE), Centre for Structural Systems Biology (CSSB), 22607, Hamburg, Germany
| | - Tristan Cragnolini
- Leibniz Institute of Virology (LIV) and Universitätsklinikum Hamburg Eppendorf (UKE), Centre for Structural Systems Biology (CSSB), 22607, Hamburg, Germany
- Institute of Structural and Molecular Biology, Birkbeck, University of London, London, UK
| | - Maya Topf
- Leibniz Institute of Virology (LIV) and Universitätsklinikum Hamburg Eppendorf (UKE), Centre for Structural Systems Biology (CSSB), 22607, Hamburg, Germany.
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6
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Aureli S, Cardenas VB, Raniolo S, Limongelli V. Conformational plasticity and allosteric communication networks explain Shelterin protein TPP1 binding to human telomerase. Commun Chem 2023; 6:242. [PMID: 37935941 PMCID: PMC10630336 DOI: 10.1038/s42004-023-01040-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2023] [Accepted: 10/24/2023] [Indexed: 11/09/2023] Open
Abstract
The Shelterin complex protein TPP1 interacts with human telomerase (TERT) by means of the TEL-patch region, controlling telomere homeostasis. Aberrations in the TPP1-TERT heterodimer formation might lead to short telomeres and severe diseases like dyskeratosis congenita and Hoyeraal-Hreidarsson syndrome. In the present study, we provide a thorough characterization of the structural properties of the TPP1's OB-domain by combining data coming from microsecond-long molecular dynamics calculations, time-series analyses, and graph-based networks. Our results show that the TEL-patch conformational freedom is influenced by a network of long-range amino acid communications that together determine the proper TPP1-TERT binding. Furthermore, we reveal that in TPP1 pathological variants Glu169Δ, Lys170Δ and Leu95Gln, the TEL-patch plasticity is reduced, affecting the correct binding to TERT and, in turn, telomere processivity, which eventually leads to accelerated aging of affected cells. Our study provides a structural basis for the design of TPP1-targeting ligands with therapeutic potential against cancer and telomeropathies.
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Affiliation(s)
- Simone Aureli
- Faculty of Biomedical Sciences, Euler Institute, Università della Svizzera italiana (USI), via G. Buffi 13, Lugano, CH-6900, Switzerland
- Institute of Pharmaceutical Sciences of Western Switzerland, University of Geneve, Rue Michel-Servet 1, Geneva, CH-1211, Switzerland
- Swiss Institute of Bioinformatics, University of Geneve, Geneva, CH-1206, Switzerland
| | - Vince Bart Cardenas
- Faculty of Biomedical Sciences, Euler Institute, Università della Svizzera italiana (USI), via G. Buffi 13, Lugano, CH-6900, Switzerland
| | - Stefano Raniolo
- Faculty of Biomedical Sciences, Euler Institute, Università della Svizzera italiana (USI), via G. Buffi 13, Lugano, CH-6900, Switzerland
| | - Vittorio Limongelli
- Faculty of Biomedical Sciences, Euler Institute, Università della Svizzera italiana (USI), via G. Buffi 13, Lugano, CH-6900, Switzerland.
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7
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Zhao C, Lu D, Zhao Q, Ren C, Zhang H, Zhai J, Gou J, Zhu S, Zhang Y, Gong X. Computational methods for in situ structural studies with cryogenic electron tomography. Front Cell Infect Microbiol 2023; 13:1135013. [PMID: 37868346 PMCID: PMC10586593 DOI: 10.3389/fcimb.2023.1135013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2022] [Accepted: 08/29/2023] [Indexed: 10/24/2023] Open
Abstract
Cryo-electron tomography (cryo-ET) plays a critical role in imaging microorganisms in situ in terms of further analyzing the working mechanisms of viruses and drug exploitation, among others. A data processing workflow for cryo-ET has been developed to reconstruct three-dimensional density maps and further build atomic models from a tilt series of two-dimensional projections. Low signal-to-noise ratio (SNR) and missing wedge are two major factors that make the reconstruction procedure challenging. Because only few near-atomic resolution structures have been reconstructed in cryo-ET, there is still much room to design new approaches to improve universal reconstruction resolutions. This review summarizes classical mathematical models and deep learning methods among general reconstruction steps. Moreover, we also discuss current limitations and prospects. This review can provide software and methods for each step of the entire procedure from tilt series by cryo-ET to 3D atomic structures. In addition, it can also help more experts in various fields comprehend a recent research trend in cryo-ET. Furthermore, we hope that more researchers can collaborate in developing computational methods and mathematical models for high-resolution three-dimensional structures from cryo-ET datasets.
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Affiliation(s)
- Cuicui Zhao
- Mathematical Intelligence Application LAB, Institute for Mathematical Sciences, Renmin University of China, Beijing, China
| | - Da Lu
- Mathematical Intelligence Application LAB, Institute for Mathematical Sciences, Renmin University of China, Beijing, China
| | - Qian Zhao
- Mathematical Intelligence Application LAB, Institute for Mathematical Sciences, Renmin University of China, Beijing, China
| | - Chongjiao Ren
- Mathematical Intelligence Application LAB, Institute for Mathematical Sciences, Renmin University of China, Beijing, China
| | - Huangtao Zhang
- Mathematical Intelligence Application LAB, Institute for Mathematical Sciences, Renmin University of China, Beijing, China
| | - Jiaqi Zhai
- Mathematical Intelligence Application LAB, Institute for Mathematical Sciences, Renmin University of China, Beijing, China
| | - Jiaxin Gou
- Mathematical Intelligence Application LAB, Institute for Mathematical Sciences, Renmin University of China, Beijing, China
| | - Shilin Zhu
- Mathematical Intelligence Application LAB, Institute for Mathematical Sciences, Renmin University of China, Beijing, China
| | - Yaqi Zhang
- Mathematical Intelligence Application LAB, Institute for Mathematical Sciences, Renmin University of China, Beijing, China
| | - Xinqi Gong
- Mathematical Intelligence Application LAB, Institute for Mathematical Sciences, Renmin University of China, Beijing, China
- Beijing Academy of Intelligence, Beijing, China
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8
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Miyashita O, Tama F. Advancing cryo-electron microscopy data analysis through accelerated simulation-based flexible fitting approaches. Curr Opin Struct Biol 2023; 82:102653. [PMID: 37451233 DOI: 10.1016/j.sbi.2023.102653] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2023] [Revised: 05/30/2023] [Accepted: 06/19/2023] [Indexed: 07/18/2023]
Abstract
Flexible fitting based on molecular dynamics simulation is a technique for structure modeling from cryo-EM data. It has been utilized for nearly two decades, and while cryo-EM resolution has improved significantly, it remains a powerful approach that can provide structural and dynamical insights that are not directly accessible from experimental data alone. Molecular dynamics simulations provide a means to extract atomistic details of conformational changes that are encoded in cryo-EM data and can also assist in improving the quality of structural models. Additionally, molecular dynamics simulations enable the characterization of conformational heterogeneity in cryo-EM data. We will summarize the advancements made in these techniques and highlight recent developments in this field.
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Affiliation(s)
- Osamu Miyashita
- RIKEN Center for Computational Science, 6-7-1, Minatojima-minami-machi, Chuo-ku, Kobe, Hyogo 650-0047, Japan.
| | - Florence Tama
- RIKEN Center for Computational Science, 6-7-1, Minatojima-minami-machi, Chuo-ku, Kobe, Hyogo 650-0047, Japan; Department of Physics, Graduate School of Science, Nagoya University, Furo-cho, Chikusa-ku, Nagoya, Aichi 464-8602, Japan; Institute of Transformative Bio-Molecules, Nagoya University, Furo-cho, Chikusa-ku, Nagoya, Aichi 464-8602, Japan.
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9
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Sarkar D, Lee H, Vant JW, Turilli M, Vermaas JV, Jha S, Singharoy A. Adaptive Ensemble Refinement of Protein Structures in High Resolution Electron Microscopy Density Maps with Radical Augmented Molecular Dynamics Flexible Fitting. J Chem Inf Model 2023; 63:5834-5846. [PMID: 37661856 DOI: 10.1021/acs.jcim.3c00350] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/05/2023]
Abstract
Recent advances in cryo-electron microscopy (cryo-EM) have enabled modeling macromolecular complexes that are essential components of the cellular machinery. The density maps derived from cryo-EM experiments are often integrated with manual, knowledge-driven or artificial intelligence-driven and physics-guided computational methods to build, fit, and refine molecular structures. Going beyond a single stationary-structure determination scheme, it is becoming more common to interpret the experimental data with an ensemble of models that contributes to an average observation. Hence, there is a need to decide on the quality of an ensemble of protein structures on-the-fly while refining them against the density maps. We introduce such an adaptive decision-making scheme during the molecular dynamics flexible fitting (MDFF) of biomolecules. Using RADICAL-Cybertools, the new RADICAL augmented MDFF implementation (R-MDFF) is examined in high-performance computing environments for refinement of two prototypical protein systems, adenylate kinase and carbon monoxide dehydrogenase. For these test cases, use of multiple replicas in flexible fitting with adaptive decision making in R-MDFF improves the overall correlation to the density by 40% relative to the refinements of the brute-force MDFF. The improvements are particularly significant at high, 2-3 Å map resolutions. More importantly, the ensemble model captures key features of biologically relevant molecular dynamics that are inaccessible to a single-model interpretation. Finally, the pipeline is applicable to systems of growing sizes, which is demonstrated using ensemble refinement of capsid proteins from the chimpanzee adenovirus. The overhead for decision making remains low and robust to computing environments. The software is publicly available on GitHub and includes a short user guide to install R-MDFF on different computing environments, from local Linux-based workstations to high-performance computing environments.
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Affiliation(s)
- Daipayan Sarkar
- MSU-DOE Plant Research Laboratory, East Lansing, Michigan 48824, United States
- School of Molecular Sciences, Arizona State University, Tempe, Arizona 85281, United States
| | - Hyungro Lee
- Pacific Northwest National Laboratory, Richland, Washington 99354, United States
- Electrical & Computer Engineering, Rutgers University, New Brunswick, New Jersey 08854, United States
| | - John W Vant
- School of Molecular Sciences, Arizona State University, Tempe, Arizona 85281, United States
| | - Matteo Turilli
- Electrical & Computer Engineering, Rutgers University, New Brunswick, New Jersey 08854, United States
- Computational Science Initiative, Brookhaven National Laboratory, Upton, New York 11973, United States
| | - Josh V Vermaas
- MSU-DOE Plant Research Laboratory, East Lansing, Michigan 48824, United States
| | - Shantenu Jha
- Electrical & Computer Engineering, Rutgers University, New Brunswick, New Jersey 08854, United States
- Computational Science Initiative, Brookhaven National Laboratory, Upton, New York 11973, United States
| | - Abhishek Singharoy
- School of Molecular Sciences, Arizona State University, Tempe, Arizona 85281, United States
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10
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Yvonnesdotter L, Rovšnik U, Blau C, Lycksell M, Howard RJ, Lindahl E. Automated simulation-based membrane protein refinement into cryo-EM data. Biophys J 2023; 122:2773-2781. [PMID: 37277992 PMCID: PMC10397807 DOI: 10.1016/j.bpj.2023.05.033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Revised: 04/02/2023] [Accepted: 05/31/2023] [Indexed: 06/07/2023] Open
Abstract
The resolution revolution has increasingly enabled single-particle cryogenic electron microscopy (cryo-EM) reconstructions of previously inaccessible systems, including membrane proteins-a category that constitutes a disproportionate share of drug targets. We present a protocol for using density-guided molecular dynamics simulations to automatically refine atomistic models into membrane protein cryo-EM maps. Using adaptive force density-guided simulations as implemented in the GROMACS molecular dynamics package, we show how automated model refinement of a membrane protein is achieved without the need to manually tune the fitting force ad hoc. We also present selection criteria to choose the best-fit model that balances stereochemistry and goodness of fit. The proposed protocol was used to refine models into a new cryo-EM density of the membrane protein maltoporin, either in a lipid bilayer or detergent micelle, and we found that results do not substantially differ from fitting in solution. Fitted structures satisfied classical model-quality metrics and improved the quality and the model-to-map correlation of the x-ray starting structure. Additionally, the density-guided fitting in combination with generalized orientation-dependent all-atom potential was used to correct the pixel-size estimation of the experimental cryo-EM density map. This work demonstrates the applicability of a straightforward automated approach to fitting membrane protein cryo-EM densities. Such computational approaches promise to facilitate rapid refinement of proteins under different conditions or with various ligands present, including targets in the highly relevant superfamily of membrane proteins.
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Affiliation(s)
- Linnea Yvonnesdotter
- Science for Life Laboratory & Swedish e-Science Research Center, Department of Applied Physics, KTH Royal Institute of Technology, Solna, Sweden
| | - Urška Rovšnik
- Science for Life Laboratory & Swedish e-Science Research Center, Department of Applied Physics, KTH Royal Institute of Technology, Solna, Sweden
| | - Christian Blau
- Science for Life Laboratory, Department of Biochemistry and Biophysics, Stockholm University, Solna, Sweden
| | - Marie Lycksell
- Science for Life Laboratory & Swedish e-Science Research Center, Department of Applied Physics, KTH Royal Institute of Technology, Solna, Sweden
| | - Rebecca Joy Howard
- Science for Life Laboratory & Swedish e-Science Research Center, Department of Applied Physics, KTH Royal Institute of Technology, Solna, Sweden
| | - Erik Lindahl
- Science for Life Laboratory & Swedish e-Science Research Center, Department of Applied Physics, KTH Royal Institute of Technology, Solna, Sweden; Science for Life Laboratory, Department of Biochemistry and Biophysics, Stockholm University, Solna, Sweden.
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11
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Blau C, Yvonnesdotter L, Lindahl E. Gentle and fast all-atom model refinement to cryo-EM densities via a maximum likelihood approach. PLoS Comput Biol 2023; 19:e1011255. [PMID: 37523411 PMCID: PMC10427019 DOI: 10.1371/journal.pcbi.1011255] [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: 09/30/2022] [Revised: 08/15/2023] [Accepted: 06/09/2023] [Indexed: 08/02/2023] Open
Abstract
Better detectors and automated data collection have generated a flood of high-resolution cryo-EM maps, which in turn has renewed interest in improving methods for determining structure models corresponding to these maps. However, automatically fitting atoms to densities becomes difficult as their resolution increases and the refinement potential has a vast number of local minima. In practice, the problem becomes even more complex when one also wants to achieve a balance between a good fit of atom positions to the map, while also establishing good stereochemistry or allowing protein secondary structure to change during fitting. Here, we present a solution to this challenge using a maximum likelihood approach by formulating the problem as identifying the structure most likely to have produced the observed density map. This allows us to derive new types of smooth refinement potential-based on relative entropy-in combination with a novel adaptive force scaling algorithm to allow balancing of force-field and density-based potentials. In a low-noise scenario, as expected from modern cryo-EM data, the relative-entropy based refinement potential outperforms alternatives, and the adaptive force scaling appears to aid all existing refinement potentials. The method is available as a component in the GROMACS molecular simulation toolkit.
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Affiliation(s)
- Christian Blau
- Department of Applied Physics, Science for Life Laboratory, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Linnea Yvonnesdotter
- Department of Biochemistry and Biophysics, Science for Life Laboratory, Stockholm University, Stockholm, Sweden
| | - Erik Lindahl
- Department of Applied Physics, Science for Life Laboratory, KTH Royal Institute of Technology, Stockholm, Sweden
- Department of Biochemistry and Biophysics, Science for Life Laboratory, Stockholm University, Stockholm, Sweden
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12
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Vuillemot R, Rouiller I, Jonić S. MDTOMO method for continuous conformational variability analysis in cryo electron subtomograms based on molecular dynamics simulations. Sci Rep 2023; 13:10596. [PMID: 37391578 PMCID: PMC10313669 DOI: 10.1038/s41598-023-37037-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2023] [Accepted: 06/14/2023] [Indexed: 07/02/2023] Open
Abstract
Cryo electron tomography (cryo-ET) allows observing macromolecular complexes in their native environment. The common routine of subtomogram averaging (STA) allows obtaining the three-dimensional (3D) structure of abundant macromolecular complexes, and can be coupled with discrete classification to reveal conformational heterogeneity of the sample. However, the number of complexes extracted from cryo-ET data is usually small, which restricts the discrete-classification results to a small number of enough populated states and, thus, results in a largely incomplete conformational landscape. Alternative approaches are currently being investigated to explore the continuity of the conformational landscapes that in situ cryo-ET studies could provide. In this article, we present MDTOMO, a method for analyzing continuous conformational variability in cryo-ET subtomograms based on Molecular Dynamics (MD) simulations. MDTOMO allows obtaining an atomic-scale model of conformational variability and the corresponding free-energy landscape, from a given set of cryo-ET subtomograms. The article presents the performance of MDTOMO on a synthetic ABC exporter dataset and an in situ SARS-CoV-2 spike dataset. MDTOMO allows analyzing dynamic properties of molecular complexes to understand their biological functions, which could also be useful for structure-based drug discovery.
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Affiliation(s)
- Rémi Vuillemot
- IMPMC-UMR 7590 CNRS, Sorbonne Université, Muséum National d'Histoire Naturelle, CC 115, 4 Place Jussieu, 75005, Paris, France
- Department of Biochemistry and Pharmacology and Bio21 Molecular Science and Biotechnology Institute, The University of Melbourne, Melbourne, VIC, 3010, Australia
| | - Isabelle Rouiller
- Department of Biochemistry and Pharmacology and Bio21 Molecular Science and Biotechnology Institute, The University of Melbourne, Melbourne, VIC, 3010, Australia
- Australian Research Council Centre for Cryo-Electron Microscopy of Membrane Proteins, Parkville, VIC, 3052, Australia
| | - Slavica Jonić
- IMPMC-UMR 7590 CNRS, Sorbonne Université, Muséum National d'Histoire Naturelle, CC 115, 4 Place Jussieu, 75005, Paris, France.
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13
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Chang L, Mondal A, MacCallum JL, Perez A. CryoFold 2.0: Cryo-EM Structure Determination with MELD. J Phys Chem A 2023; 127:3906-3913. [PMID: 37084537 DOI: 10.1021/acs.jpca.3c01731] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/23/2023]
Abstract
Cryo-electron microscopy data are becoming more prevalent and accessible at higher resolution levels, leading to the development of new computational tools to determine the atomic structure of macromolecules. However, while existing tools adapted from X-ray crystallography are suitable for the highest-resolution maps, new tools are needed for lower-resolution levels and to account for map heterogeneity. In this article, we introduce CryoFold 2.0, an integrative physics-based approach that combines Bayesian inference and the ability to handle multiple data sources with the molecular dynamics flexible fitting (MDFF) approach to determine the structures of macromolecules by using cryo-EM data. CryoFold 2.0 is incorporated into the MELD (modeling employing limited data) plugin, resulting in a pipeline that is more computationally efficient and accurate than running MELD or MDFF alone. The approach requires fewer computational resources and shorter simulation times than the original CryoFold, and it minimizes manual intervention. We demonstrate the effectiveness of the approach on eight different systems, highlighting its various benefits.
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Affiliation(s)
- Liwei Chang
- Department of Chemistry and Quantum Theory Project, University of Florida, Gainesville, Florida 32611, United States
| | - Arup Mondal
- Department of Chemistry and Quantum Theory Project, University of Florida, Gainesville, Florida 32611, United States
| | - Justin L MacCallum
- Department of Chemistry, University of Calgary, Calgary, AB T2N 1N4, Canada
| | - Alberto Perez
- Department of Chemistry and Quantum Theory Project, University of Florida, Gainesville, Florida 32611, United States
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14
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Li N, Quan A, Li D, Pan J, Ren H, Hoeltzel G, de Val N, Ashworth D, Ni W, Zhou J, Mackay S, Hewitt SM, Cachau R, Ho M. The IgG4 hinge with CD28 transmembrane domain improves V HH-based CAR T cells targeting a membrane-distal epitope of GPC1 in pancreatic cancer. Nat Commun 2023; 14:1986. [PMID: 37031249 PMCID: PMC10082787 DOI: 10.1038/s41467-023-37616-4] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2022] [Accepted: 03/23/2023] [Indexed: 04/10/2023] Open
Abstract
Heterogeneous antigen expression is a key barrier influencing the activity of chimeric antigen receptor (CAR) T cells in solid tumors. Here, we develop CAR T cells targeting glypican-1 (GPC1), an oncofetal antigen expressed in pancreatic cancer. We report the generation of dromedary camel VHH nanobody (D4)-based CAR T cells targeting GPC1 and the optimization of the hinge (H) and transmembrane domain (TM) to improve activity. We find that a structurally rigid IgG4H and CD28TM domain brings the two D4 fragments in proximity, driving CAR dimerization and leading to enhanced T-cell signaling and tumor regression in pancreatic cancer models with low antigen density in female mice. Furthermore, single-cell-based proteomic and transcriptomic analysis of D4-IgG4H-CD28TM CAR T cells reveals specific genes (e.g., HMGB1) associated with high T-cell polyfunctionality. This study demonstrates the potential of VHH-based CAR T for pancreatic cancer therapy and provides an engineering strategy for developing potent CAR T cells targeting membrane-distal epitopes.
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Affiliation(s)
- Nan Li
- Laboratory of Molecular Biology, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Alex Quan
- Laboratory of Molecular Biology, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Dan Li
- Laboratory of Molecular Biology, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Jiajia Pan
- Laboratory of Molecular Biology, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Hua Ren
- Laboratory of Molecular Biology, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Gerard Hoeltzel
- Laboratory of Molecular Biology, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Natalia de Val
- Center for Molecular Microscopy, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Frederick, MD, 21702, USA
| | | | - Weiming Ni
- IsoPlexis Corporation, Branford, CT, 06405, USA
| | - Jing Zhou
- IsoPlexis Corporation, Branford, CT, 06405, USA
| | - Sean Mackay
- IsoPlexis Corporation, Branford, CT, 06405, USA
| | - Stephen M Hewitt
- Laboratory of Pathology, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Raul Cachau
- Integrated Data Science Section, Research Technologies Branch, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Mitchell Ho
- Laboratory of Molecular Biology, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, 20892, USA.
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15
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Structure and activity of botulinum neurotoxin X. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.01.11.523524. [PMID: 36712025 PMCID: PMC9882044 DOI: 10.1101/2023.01.11.523524] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
Abstract
Botulinum neurotoxins (BoNTs) are the most potent toxins known and are used to treat an increasing number of medical disorders. All BoNTs are naturally co-expressed with a protective partner protein (NTNH) with which they form a 300 kDa complex, to resist acidic and proteolytic attack from the digestive tract. We have previously identified a new botulinum neurotoxin serotype, BoNT/X, that has unique and therapeutically attractive properties. We present the cryo-EM structure of the BoNT/X-NTNH/X complex at 3.1 Å resolution. Unexpectedly, the BoNT/X complex is stable and protease resistant at both neutral and acidic pH and disassembles only in alkaline conditions. Using the stabilizing effect of NTNH, we isolated BoNT/X and showed that it has very low potency both in vitro and in vivo . Given the high catalytic activity and translocation efficacy of BoNT/X, low activity of the full toxin is likely due to the receptor-binding domain, which presents weak ganglioside binding and exposed hydrophobic surfaces.
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16
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Vuillemot R, Mirzaei A, Harastani M, Hamitouche I, Fréchin L, Klaholz BP, Miyashita O, Tama F, Rouiller I, Jonic S. MDSPACE: Extracting Continuous Conformational Landscapes from Cryo-EM Single Particle Datasets Using 3D-to-2D Flexible Fitting based on Molecular Dynamics Simulation. J Mol Biol 2023; 435:167951. [PMID: 36638910 DOI: 10.1016/j.jmb.2023.167951] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2022] [Revised: 12/08/2022] [Accepted: 01/03/2023] [Indexed: 01/12/2023]
Abstract
This article presents an original approach for extracting atomic-resolution landscapes of continuous conformational variability of biomolecular complexes from cryo electron microscopy (cryo-EM) single particle images. This approach is based on a new 3D-to-2D flexible fitting method, which uses molecular dynamics (MD) simulation and is embedded in an iterative conformational-landscape refinement scheme. This new approach is referred to as MDSPACE, which stands for Molecular Dynamics simulation for Single Particle Analysis of Continuous Conformational hEterogeneity. The article describes the MDSPACE approach and shows its performance using synthetic and experimental datasets.
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Affiliation(s)
- Rémi Vuillemot
- IMPMC-UMR 7590 CNRS, Sorbonne Université, Muséum National d'Histoire Naturelle, Paris, France; Department of Biochemistry & Pharmacology and Bio21 Molecular Science and Biotechnology Institute, The University of Melbourne, Victoria, Australia
| | - Alex Mirzaei
- IMPMC-UMR 7590 CNRS, Sorbonne Université, Muséum National d'Histoire Naturelle, Paris, France
| | - Mohamad Harastani
- IMPMC-UMR 7590 CNRS, Sorbonne Université, Muséum National d'Histoire Naturelle, Paris, France
| | - Ilyes Hamitouche
- IMPMC-UMR 7590 CNRS, Sorbonne Université, Muséum National d'Histoire Naturelle, Paris, France
| | - Léo Fréchin
- Centre for Integrative Biology, Department of Integrated Structural Biology, IGBMC-UMR 7104 CNRS, U964 Inserm, Université de Strasbourg, Strasbourg, France
| | - Bruno P Klaholz
- Centre for Integrative Biology, Department of Integrated Structural Biology, IGBMC-UMR 7104 CNRS, U964 Inserm, Université de Strasbourg, Strasbourg, France
| | | | - Florence Tama
- RIKEN Center for Computational Science, Kobe, Japan; Institute of Transformative Biomolecules, Graduate School of Science, Nagoya University, Nagoya, Japan; Department of Physics, Graduate School of Science, Nagoya University, Nagoya, Japan
| | - Isabelle Rouiller
- Department of Biochemistry & Pharmacology and Bio21 Molecular Science and Biotechnology Institute, The University of Melbourne, Victoria, Australia
| | - Slavica Jonic
- IMPMC-UMR 7590 CNRS, Sorbonne Université, Muséum National d'Histoire Naturelle, Paris, France.
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17
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Beton JG, Cragnolini T, Kaleel M, Mulvaney T, Sweeney A, Topf M. Integrating model simulation tools and
cryo‐electron
microscopy. WIRES COMPUTATIONAL MOLECULAR SCIENCE 2022. [DOI: 10.1002/wcms.1642] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Affiliation(s)
- Joseph George Beton
- Centre for Structural Systems Biology (CSSB) Leibniz‐Institut für Virologie (LIV) Hamburg Germany
| | - Tristan Cragnolini
- Institute of Structural and Molecular Biology, Birkbeck and University College London London UK
| | - Manaz Kaleel
- Centre for Structural Systems Biology (CSSB) Leibniz‐Institut für Virologie (LIV) Hamburg Germany
| | - Thomas Mulvaney
- Centre for Structural Systems Biology (CSSB) Leibniz‐Institut für Virologie (LIV) Hamburg Germany
| | - Aaron Sweeney
- Centre for Structural Systems Biology (CSSB) Leibniz‐Institut für Virologie (LIV) Hamburg Germany
| | - Maya Topf
- Centre for Structural Systems Biology (CSSB) Leibniz‐Institut für Virologie (LIV) Hamburg Germany
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18
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Abstract
Adeno-associated virus (AAV) has a single-stranded DNA genome encapsidated in a small icosahedrally symmetric protein shell with 60 subunits. AAV is the leading delivery vector in emerging gene therapy treatments for inherited disorders, so its structure and molecular interactions with human hosts are of intense interest. A wide array of electron microscopic approaches have been used to visualize the virus and its complexes, depending on the scientific question, technology available, and amenability of the sample. Approaches range from subvolume tomographic analyses of complexes with large and flexible host proteins to detailed analysis of atomic interactions within the virus and with small ligands at resolutions as high as 1.6 Å. Analyses have led to the reclassification of glycan receptors as attachment factors, to structures with a new-found receptor protein, to identification of the epitopes of antibodies, and a new understanding of possible neutralization mechanisms. AAV is now well-enough characterized that it has also become a model system for EM methods development. Heralding a new era, cryo-EM is now also being deployed as an analytic tool in the process development and production quality control of high value pharmaceutical biologics, namely AAV vectors.
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Affiliation(s)
- Scott
M. Stagg
- Department
of Biological Sciences, Florida State University, Tallahassee, Florida 32306, United States
- Institute
of Molecular Biophysics, Florida State University, Tallahassee, Florida 32306, United States
| | - Craig Yoshioka
- Department
of Biomedical Engineering, Oregon Health
& Science University, Portland Oregon 97239, United States
| | - Omar Davulcu
- Environmental
Molecular Sciences Laboratory, Pacific Northwest
National Laboratory, 3335 Innovation Boulevard, Richland, Washington 99354, United States
| | - Michael S. Chapman
- Department
of Biochemistry, University of Missouri, Columbia, Missouri 65211, United States
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19
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The ribosome stabilizes partially folded intermediates of a nascent multi-domain protein. Nat Chem 2022; 14:1165-1173. [PMID: 35927328 PMCID: PMC7613651 DOI: 10.1038/s41557-022-01004-0] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2021] [Accepted: 06/20/2022] [Indexed: 12/13/2022]
Abstract
Co-translational folding is crucial to ensure the production of biologically active proteins. The ribosome can alter the folding pathways of nascent polypeptide chains, yet a structural understanding remains largely inaccessible experimentally. We have developed site-specific labelling of nascent chains to detect and measure, using 19F nuclear magnetic resonance (NMR) spectroscopy, multiple states accessed by an immunoglobulin-like domain within a tandem repeat protein during biosynthesis. By examining ribosomes arrested at different stages during translation of this common structural motif, we observe highly broadened NMR resonances attributable to two previously unidentified intermediates, which are stably populated across a wide folding transition. Using molecular dynamics simulations and corroborated by cryo-electron microscopy, we obtain models of these partially folded states, enabling experimental verification of a ribosome-binding site that contributes to their high stabilities. We thus demonstrate a mechanism by which the ribosome could thermodynamically regulate folding and other co-translational processes. ![]()
Most proteins must fold co-translationally on the ribosome to adopt biologically active conformations, yet structural, mechanistic descriptions are lacking. Using 19F NMR spectroscopy to study a nascent multi-domain protein has now enabled the identification of two co-translational folding intermediates that are significantly more stable than intermediates formed off the ribosome, suggesting that the ribosome may thermodynamically regulate folding.
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20
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Williams A, Zhan CG. Fast Prediction of Binding Affinities of SARS-CoV-2 Spike Protein and Its Mutants with Antibodies through Intermolecular Interaction Modeling-Based Machine Learning. J Phys Chem B 2022; 126:5194-5206. [PMID: 35817617 PMCID: PMC9301770 DOI: 10.1021/acs.jpcb.2c02123] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2022] [Revised: 06/28/2022] [Indexed: 11/30/2022]
Abstract
Since the introduction of the novel SARS-CoV-2 virus (COVID-19) in late 2019, various new variants have appeared with mutations that confer resistance to the vaccines and monoclonal antibodies that were developed in response to the wild-type virus. As we continue through the pandemic, an accurate and efficient methodology is needed to help predict the effects certain mutations will have on both our currently produced therapeutics and those that are in development. Using published cryo-electron microscopy and X-ray crystallography structures of the spike receptor binding domain region with currently known antibodies, in the present study, we created and cross-validated an intermolecular interaction modeling-based multi-layer perceptron machine learning approach that can accurately predict the mutation-caused shifts in the binding affinity between the spike protein (wild-type or mutant) and various antibodies. This validated artificial intelligence (AI) model was used to predict the binding affinity (Kd) of reported SARS-CoV-2 antibodies with various variants of concern, including the most recently identified "Deltamicron" (or "Deltacron") variant. This AI model may be employed in the future to predict the Kd of developed novel antibody therapeutics to overcome the challenging antibody resistance issue and develop structural bases for the effects of both current and new mutants of the spike protein. In addition, the similar AI strategy and approach based on modeling of the intermolecular interactions may be useful in development of machine learning models predicting binding affinities for other protein-protein binding systems, including other antibodies binding with their antigens.
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Affiliation(s)
- Alexander
H. Williams
- Molecular
Modeling and Biopharmaceutical Center, University
of Kentucky, 789 South Limestone Street, Lexington, Kentucky 40536, United States
- Department
of Pharmaceutical Sciences, College of Pharmacy, University of Kentucky, 789 South Limestone Street, Lexington, Kentucky 40536, United States
| | - Chang-Guo Zhan
- Molecular
Modeling and Biopharmaceutical Center, University
of Kentucky, 789 South Limestone Street, Lexington, Kentucky 40536, United States
- Department
of Pharmaceutical Sciences, College of Pharmacy, University of Kentucky, 789 South Limestone Street, Lexington, Kentucky 40536, United States
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21
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He J, Lin P, Chen J, Cao H, Huang SY. Model building of protein complexes from intermediate-resolution cryo-EM maps with deep learning-guided automatic assembly. Nat Commun 2022; 13:4066. [PMID: 35831370 PMCID: PMC9279371 DOI: 10.1038/s41467-022-31748-9] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2022] [Accepted: 06/30/2022] [Indexed: 12/29/2022] Open
Abstract
Advances in microscopy instruments and image processing algorithms have led to an increasing number of cryo-electron microscopy (cryo-EM) maps. However, building accurate models into intermediate-resolution EM maps remains challenging and labor-intensive. Here, we propose an automatic model building method of multi-chain protein complexes from intermediate-resolution cryo-EM maps, named EMBuild, by integrating AlphaFold structure prediction, FFT-based global fitting, domain-based semi-flexible refinement, and graph-based iterative assembling on the main-chain probability map predicted by a deep convolutional network. EMBuild is extensively evaluated on diverse test sets of 47 single-particle EM maps at 4.0-8.0 Å resolution and 16 subtomogram averaging maps of cryo-ET data at 3.7-9.3 Å resolution, and compared with state-of-the-art approaches. We demonstrate that EMBuild is able to build high-quality complex structures that are comparably accurate to the manually built PDB structures from the cryo-EM maps. These results demonstrate the accuracy and reliability of EMBuild in automatic model building.
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Affiliation(s)
- Jiahua He
- School of Physics and Key Laboratory of Molecular Biophysics of MOE, Huazhong University of Science and Technology, Wuhan, Hubei, 430074, China
| | - Peicong Lin
- School of Physics and Key Laboratory of Molecular Biophysics of MOE, Huazhong University of Science and Technology, Wuhan, Hubei, 430074, China
| | - Ji Chen
- School of Physics and Key Laboratory of Molecular Biophysics of MOE, Huazhong University of Science and Technology, Wuhan, Hubei, 430074, China
| | - Hong Cao
- School of Physics and Key Laboratory of Molecular Biophysics of MOE, Huazhong University of Science and Technology, Wuhan, Hubei, 430074, China
| | - Sheng-You Huang
- School of Physics and Key Laboratory of Molecular Biophysics of MOE, Huazhong University of Science and Technology, Wuhan, Hubei, 430074, China.
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22
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Mondragón-Solórzano G, Sandoval-Lira J, Nochebuena J, Cisneros GA, Barroso-Flores J. Electronic Structure Effects Related to the Origin of the Remarkable Near-Infrared Absorption of Blastochloris viridis' Light Harvesting 1-Reaction Center Complex. J Chem Theory Comput 2022; 18:4555-4564. [PMID: 35767461 PMCID: PMC10408377 DOI: 10.1021/acs.jctc.2c00497] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Various photosynthetic organisms have evolved to absorb light in different regions of the visible light spectrum, thus adapting to the various lighting conditions available on Earth. While most of these autotrophic organisms absorb wavelengths around the 700-800 nm region, some are capable of red-shifted absorptions above this range, but none as remarkably as Blastochloris viridis whose main absorption is observed at 1015 nm, approximately 220 nm (0.34 eV) lower in energy than their main constituent pigments, BChl-b, whose main absorption is observed at 795 nm. The structure of its light harvesting 1-reaction center was recently elucidated by cryo-EM; however, the electronic structure details behind this red-shifted absorption remain unattended. We used hybrid quantum mechanics/molecular mechanics (QM/MM) calculations to optimize one of the active centers and performed classical molecular dynamics (MD) simulations to sample conformations beyond the optimized structure. We did excited state calculations with the time-dependent density functional theory method at the CAM-B3LYP/cc-pVDZ level of theory. We reproduced the near IR absorption by sequentially modifying the number of components involved in our systems using representative structures from the calculated MD ensemble. Natural transition orbital analysis reveals the participation of the BChl-b fragments to the main transition in the native structure and the structures obtained from the QM/MM and MD simulations. H-bonding pigment-protein interactions play a role on the conformation stabilization and orientation; however, the bacteriochlorin ring conformations and the exciton delocalization are the most relevant factors to explain the red-shifting phenomenon.
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Affiliation(s)
- Gustavo Mondragón-Solórzano
- Centro Conjunto de Investigación en Química Sustentable UAEM-UNAM. Carretera Toluca-Atlacomulco Km. 14.5, Unidad San Cayetano. Toluca de Lerdo 50200, México
- Instituto de Química. Universidad Nacional Autónoma de México, Circuito Exterior, Ciudad Universitaria, CDMX 04510, México
| | - Jacinto Sandoval-Lira
- Centro Conjunto de Investigación en Química Sustentable UAEM-UNAM. Carretera Toluca-Atlacomulco Km. 14.5, Unidad San Cayetano. Toluca de Lerdo 50200, México
- Instituto de Química. Universidad Nacional Autónoma de México, Circuito Exterior, Ciudad Universitaria, CDMX 04510, México
- Departamento de Ingeniería Ambiental, Instituto Tecnológico Superior de San Martín Texmelucan, TecNM, Camino a la Barranca de Pesos, C.P. 74120 San Martín Texmelucan, Puebla, México
| | - Jorge Nochebuena
- Department of Physics, University of Texas at Dallas, Richardson, Texas 75801, United States
| | - G Andrés Cisneros
- Department of Physics, University of Texas at Dallas, Richardson, Texas 75801, United States
- Department of Chemistry and Biochemistry, University of Texas at Dallas, Richardson, Texas 75801, United States
| | - Joaquín Barroso-Flores
- Centro Conjunto de Investigación en Química Sustentable UAEM-UNAM. Carretera Toluca-Atlacomulco Km. 14.5, Unidad San Cayetano. Toluca de Lerdo 50200, México
- Instituto de Química. Universidad Nacional Autónoma de México, Circuito Exterior, Ciudad Universitaria, CDMX 04510, México
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23
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Zhu Z, Deng Z, Wang Q, Wang Y, Zhang D, Xu R, Guo L, Wen H. Simulation and Machine Learning Methods for Ion-Channel Structure Determination, Mechanistic Studies and Drug Design. Front Pharmacol 2022; 13:939555. [PMID: 35837274 PMCID: PMC9275593 DOI: 10.3389/fphar.2022.939555] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2022] [Accepted: 06/07/2022] [Indexed: 11/13/2022] Open
Abstract
Ion channels are expressed in almost all living cells, controlling the in-and-out communications, making them ideal drug targets, especially for central nervous system diseases. However, owing to their dynamic nature and the presence of a membrane environment, ion channels remain difficult targets for the past decades. Recent advancement in cryo-electron microscopy and computational methods has shed light on this issue. An explosion in high-resolution ion channel structures paved way for structure-based rational drug design and the state-of-the-art simulation and machine learning techniques dramatically improved the efficiency and effectiveness of computer-aided drug design. Here we present an overview of how simulation and machine learning-based methods fundamentally changed the ion channel-related drug design at different levels, as well as the emerging trends in the field.
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Affiliation(s)
- Zhengdan Zhu
- Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China
- Beijing Institute of Big Data Research, Beijing, China
| | - Zhenfeng Deng
- DP Technology, Beijing, China
- School of Pharmaceutical Sciences, Peking University, Beijing, China
| | | | | | - Duo Zhang
- Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China
- DP Technology, Beijing, China
| | - Ruihan Xu
- DP Technology, Beijing, China
- National Engineering Research Center of Visual Technology, Peking University, Beijing, China
| | | | - Han Wen
- DP Technology, Beijing, China
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24
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Hong J, Kwon HJ, Cachau R, Chen CZ, Butay KJ, Duan Z, Li D, Ren H, Liang T, Zhu J, Dandey VP, Martin NP, Esposito D, Ortega-Rodriguez U, Xu M, Borgnia MJ, Xie H, Ho M. Dromedary camel nanobodies broadly neutralize SARS-CoV-2 variants. Proc Natl Acad Sci U S A 2022; 119:e2201433119. [PMID: 35476528 PMCID: PMC9170159 DOI: 10.1073/pnas.2201433119] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2022] [Accepted: 03/24/2022] [Indexed: 01/07/2023] Open
Abstract
The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike is a trimer of S1/S2 heterodimers with three receptor-binding domains (RBDs) at the S1 subunit for human angiotensin-converting enzyme 2 (hACE2). Due to their small size, nanobodies can recognize protein cavities that are not accessible to conventional antibodies. To isolate high-affinity nanobodies, large libraries with great diversity are highly desirable. Dromedary camels (Camelus dromedarius) are natural reservoirs of coronaviruses like Middle East respiratory syndrome CoV (MERS-CoV) that are transmitted to humans. Here, we built large dromedary camel VHH phage libraries to isolate nanobodies that broadly neutralize SARS-CoV-2 variants. We isolated two VHH nanobodies, NCI-CoV-7A3 (7A3) and NCI-CoV-8A2 (8A2), which have a high affinity for the RBD via targeting nonoverlapping epitopes and show broad neutralization activity against SARS-CoV-2 and its emerging variants of concern. Cryoelectron microscopy (cryo-EM) complex structures revealed that 8A2 binds the RBD in its up mode with a long CDR3 loop directly involved in the ACE2 binding residues and that 7A3 targets a deeply buried region that uniquely extends from the S1 subunit to the apex of the S2 subunit regardless of the conformational state of the RBD. At a dose of ≥5 mg/kg, 7A3 efficiently protected transgenic mice expressing hACE2 from the lethal challenge of variants B.1.351 or B.1.617.2, suggesting its therapeutic use against COVID-19 variants. The dromedary camel VHH phage libraries could be helpful as a unique platform ready for quickly isolating potent nanobodies against future emerging viruses.
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Affiliation(s)
- Jessica Hong
- Laboratory of Molecular Biology, Center for Cancer Research, National Cancer Institute, NIH, Bethesda, MD 20891
| | - Hyung Joon Kwon
- Division of Viral Products, Office of Vaccines Research and Review, Center for Biologics Evaluation and Research, United States Food and Drug Administration, Silver Spring, MD 20993
| | - Raul Cachau
- Data Science and Information Technology Program, Leidos Biomedical Research, Inc., Frederick, MD 21702
| | - Catherine Z. Chen
- National Center for Advancing Translational Sciences, NIH, Rockville, MD 20850
| | - Kevin John Butay
- Molecular Microscopy Group, National Institute of Environmental Health Sciences, Research Triangle Park, NC 27709
| | - Zhijian Duan
- Antibody Engineering Program, Center for Cancer Research, National Cancer Institute, NIH, Bethesda, MD 20891
| | - Dan Li
- Laboratory of Molecular Biology, Center for Cancer Research, National Cancer Institute, NIH, Bethesda, MD 20891
| | - Hua Ren
- Laboratory of Molecular Biology, Center for Cancer Research, National Cancer Institute, NIH, Bethesda, MD 20891
| | - Tianyuzhou Liang
- Laboratory of Molecular Biology, Center for Cancer Research, National Cancer Institute, NIH, Bethesda, MD 20891
| | - Jianghai Zhu
- Data Science and Information Technology Program, Leidos Biomedical Research, Inc., Frederick, MD 21702
| | - Venkata P. Dandey
- Molecular Microscopy Group, National Institute of Environmental Health Sciences, Research Triangle Park, NC 27709
| | - Negin P. Martin
- Viral Vector Core, National Institute of Environmental Health Sciences, Research Triangle Park, NC 27709
| | - Dominic Esposito
- Protein Expression Laboratory, NCI RAS Initiative, Frederick National Laboratory for Cancer Research, Frederick, MD 21702
| | - Uriel Ortega-Rodriguez
- Division of Viral Products, Office of Vaccines Research and Review, Center for Biologics Evaluation and Research, United States Food and Drug Administration, Silver Spring, MD 20993
| | - Miao Xu
- National Center for Advancing Translational Sciences, NIH, Rockville, MD 20850
| | - Mario J. Borgnia
- Molecular Microscopy Group, National Institute of Environmental Health Sciences, Research Triangle Park, NC 27709
| | - Hang Xie
- Division of Viral Products, Office of Vaccines Research and Review, Center for Biologics Evaluation and Research, United States Food and Drug Administration, Silver Spring, MD 20993
| | - Mitchell Ho
- Laboratory of Molecular Biology, Center for Cancer Research, National Cancer Institute, NIH, Bethesda, MD 20891
- Antibody Engineering Program, Center for Cancer Research, National Cancer Institute, NIH, Bethesda, MD 20891
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25
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DeVore K, Chiu PL. Probing Structural Perturbation of Biomolecules by Extracting Cryo-EM Data Heterogeneity. Biomolecules 2022; 12:biom12050628. [PMID: 35625556 PMCID: PMC9138638 DOI: 10.3390/biom12050628] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2022] [Revised: 04/15/2022] [Accepted: 04/18/2022] [Indexed: 02/04/2023] Open
Abstract
Single-particle cryogenic electron microscopy (cryo-EM) has become an indispensable tool to probe high-resolution structural detail of biomolecules. It enables direct visualization of the biomolecules and opens a possibility for averaging molecular images to reconstruct a three-dimensional Coulomb potential density map. Newly developed algorithms for data analysis allow for the extraction of structural heterogeneity from a massive and low signal-to-noise-ratio (SNR) cryo-EM dataset, expanding our understanding of multiple conformational states, or further implications in dynamics, of the target biomolecule. This review provides an overview that briefly describes the workflow of single-particle cryo-EM, including imaging and data processing, and new methods developed for analyzing the data heterogeneity to understand the structural variability of biomolecules.
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26
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Wang J, Skeens E, Arantes PR, Maschietto F, Allen B, Kyro GW, Lisi GP, Palermo G, Batista VS. Structural Basis for Reduced Dynamics of Three Engineered HNH Endonuclease Lys-to-Ala Mutants for the Clustered Regularly Interspaced Short Palindromic Repeat (CRISPR)-Associated 9 (CRISPR/Cas9) Enzyme. Biochemistry 2022; 61:785-794. [PMID: 35420793 DOI: 10.1021/acs.biochem.2c00127] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
Many bacteria possess type-II immunity against invading phages or plasmids known as the clustered regularly interspaced short palindromic repeat (CRISPR)/CRISPR-associated 9 (Cas9) system to detect and degrade the foreign DNA sequences. The Cas9 protein has two endonucleases responsible for double-strand breaks (the HNH domain for cleaving the target strand of DNA duplexes and RuvC domain for the nontarget strand, respectively) and a single-guide RNA-binding domain where the RNA and target DNA strands are base-paired. Three engineered single Lys-to-Ala HNH mutants (K810A, K848A, and K855A) exhibit an enhanced substrate specificity for cleavage of the target DNA strand. We report in this study that in the wild-type (wt) enzyme, D835, Y836, and D837 within the Y836-containing loop (comprising E827-D837) adjacent to the catalytic site have uncharacterizable broadened 1H15N nuclear magnetic resonance (NMR) features, whereas remaining residues in the loop have different extents of broadened NMR spectra. We find that this loop in the wt enzyme exhibits three distinct conformations over the duration of the molecular dynamics simulations, whereas the three Lys-to-Ala mutants retain only one conformation. The versatility of multiple alternate conformations of this loop in the wt enzyme could help to recruit noncognate DNA substrates into the HNH active site for cleavage, thereby reducing its substrate specificity relative to the three mutants. Our study provides further experimental and computational evidence that Lys-to-Ala substitutions reduce dynamics of proteins and thus increase their stability.
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Affiliation(s)
- Jimin Wang
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, Connecticut 06520-8114, United States
| | - Erin Skeens
- Department of Molecular and Cell Biology and Biochemistry, Brown University, Providence, Rhode Island 02912, United States
| | - Pablo R Arantes
- Department of Bioengineering and Department of Chemistry, University of California Riverside, Riverside, California 92521-9800, United States
| | - Federica Maschietto
- Department of Chemistry, Yale University, New Haven, Connecticut 06511-8499, United States
| | - Brandon Allen
- Department of Chemistry, Yale University, New Haven, Connecticut 06511-8499, United States
| | - Gregory W Kyro
- Department of Chemistry, Yale University, New Haven, Connecticut 06511-8499, United States
| | - George P Lisi
- Department of Molecular and Cell Biology and Biochemistry, Brown University, Providence, Rhode Island 02912, United States
| | - Giulia Palermo
- Department of Bioengineering and Department of Chemistry, University of California Riverside, Riverside, California 92521-9800, United States
| | - Victor S Batista
- Department of Chemistry, Yale University, New Haven, Connecticut 06511-8499, United States
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27
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Williams AH, Zhan CG. Generalized Methodology for the Quick Prediction of Variant SARS-CoV-2 Spike Protein Binding Affinities with Human Angiotensin-Converting Enzyme II. J Phys Chem B 2022; 126:2353-2360. [PMID: 35315274 PMCID: PMC8982491 DOI: 10.1021/acs.jpcb.1c10718] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2021] [Revised: 02/24/2022] [Indexed: 12/25/2022]
Abstract
Variants of the SARS-CoV-2 virus continue to remain a threat 2 years from the beginning of the pandemic. As more variants arise, and the B.1.1.529 (Omicron) variant threatens to create another wave of infections, a method is needed to predict the binding affinity of the spike protein quickly and accurately with human angiotensin-converting enzyme II (ACE2). We present an accurate and convenient energy minimization/molecular mechanics Poisson-Boltzmann surface area methodology previously used with engineered ACE2 therapeutics to predict the binding affinity of the Omicron variant. Without any additional data from the variants discovered after the publication of our first model, the methodology can accurately predict the binding of the spike/ACE2 variant complexes. From this methodology, we predicted that the Omicron variant spike has a Kd of ∼22.69 nM (which is very close to the experimental Kd of 20.63 nM published during the review process of the current report) and that spike protein of the new "Stealth" Omicron variant (BA.2) will display a Kd of ∼12.9 nM with the wild-type ACE2 protein. This methodology can be used with as-yet discovered variants, allowing for quick determinations regarding the variant's infectivity versus either the wild-type virus or its variants.
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Affiliation(s)
- Alexander H. Williams
- Molecular Modeling and Biopharmaceutical Center, College of Pharmacy, University of Kentucky, 789 South Limestone Street, Lexington, KY 40536
- Department of Pharmaceutical Sciences, College of Pharmacy, University of Kentucky, 789 South Limestone Street, Lexington, KY 40536
| | - Chang-Guo Zhan
- Molecular Modeling and Biopharmaceutical Center, College of Pharmacy, University of Kentucky, 789 South Limestone Street, Lexington, KY 40536
- Department of Pharmaceutical Sciences, College of Pharmacy, University of Kentucky, 789 South Limestone Street, Lexington, KY 40536
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28
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Abstract
The mechanochemical basis of microtubule growth, which is essential for the normal function and division of eukaryotic cells, has remained elusive and controversial, despite extensive work. In particular, recent findings have created the paradox that the microtubule plus-end tips look very similar during both growing and shrinking phases, thereby challenging the traditional textbook picture. Our large-scale atomistic simulations resolve this paradox and explain microtubule growth and shrinkage dynamics as a process governed by energy barriers between protofilament conformations, the heights of which are in turn fine-tuned by different nucleotide states, thus implementing an information-driven Brownian ratchet. Microtubules (MTs), mesoscopic cellular filaments, grow primarily by the addition of GTP-bound tubulin dimers at their dynamic flaring plus-end tips. They operate as chemomechanical energy transducers with stochastic transitions to an astounding shortening motion upon hydrolyzing GTP to GDP. Time-resolved dynamics of the MT tip—a key determinant of this behavior—as a function of nucleotide state, internal lattice strain, and stabilizing lateral interactions have not been fully understood. Here we use atomistic simulations to study the spontaneous relaxation of complete GTP-MT and GDP-MT tip models from unfavorable straight to relaxed splayed conformations and to comprehensively characterize the elasticity of MT tips. Our simulations reveal the dominance of viscoelastic dynamics of MT protofilaments during the relaxation process, driven by the stored bending-torsional strain and counterbalanced by the interprotofilament interactions. We show that the posthydrolysis MT tip is exposed to higher activation energy barriers for straight lattice formation, which translates into its inability to elongate. Our study provides an information-driven Brownian ratchet mechanism for the elastic energy conversion and release by MT tips and offers insights into the mechanoenzymatics of MTs.
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29
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Liedtke J, Depelteau JS, Briegel A. How advances in cryo-electron tomography have contributed to our current view of bacterial cell biology. J Struct Biol X 2022; 6:100065. [PMID: 35252838 PMCID: PMC8894267 DOI: 10.1016/j.yjsbx.2022.100065] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2021] [Revised: 02/17/2022] [Accepted: 02/23/2022] [Indexed: 12/13/2022] Open
Abstract
Advancements in the field of cryo-electron tomography have greatly contributed to our current understanding of prokaryotic cell organization and revealed intracellular structures with remarkable architecture. In this review, we present some of the prominent advancements in cryo-electron tomography, illustrated by a subset of structural examples to demonstrate the power of the technique. More specifically, we focus on technical advances in automation of data collection and processing, sample thinning approaches, correlative cryo-light and electron microscopy, and sub-tomogram averaging methods. In turn, each of these advances enabled new insights into bacterial cell architecture, cell cycle progression, and the structure and function of molecular machines. Taken together, these significant advances within the cryo-electron tomography workflow have led to a greater understanding of prokaryotic biology. The advances made the technique available to a wider audience and more biological questions and provide the basis for continued advances in the near future.
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Affiliation(s)
- Janine Liedtke
- Department of Microbial Sciences, Institute of Biology, Leiden University, Sylviusweg 72, 2333 BE Leiden, The Netherlands.,Centre for Microbial Cell Biology, Leiden University, Sylviusweg 72, 2333 BE Leiden, The Netherlands
| | - Jamie S Depelteau
- Department of Microbial Sciences, Institute of Biology, Leiden University, Sylviusweg 72, 2333 BE Leiden, The Netherlands.,Centre for Microbial Cell Biology, Leiden University, Sylviusweg 72, 2333 BE Leiden, The Netherlands
| | - Ariane Briegel
- Department of Microbial Sciences, Institute of Biology, Leiden University, Sylviusweg 72, 2333 BE Leiden, The Netherlands.,Centre for Microbial Cell Biology, Leiden University, Sylviusweg 72, 2333 BE Leiden, The Netherlands
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30
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Exploring cryo-electron microscopy with molecular dynamics. Biochem Soc Trans 2022; 50:569-581. [PMID: 35212361 DOI: 10.1042/bst20210485] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2021] [Revised: 01/31/2022] [Accepted: 02/02/2022] [Indexed: 11/17/2022]
Abstract
Single particle analysis cryo-electron microscopy (EM) and molecular dynamics (MD) have been complimentary methods since cryo-EM was first applied to the field of structural biology. The relationship started by biasing structural models to fit low-resolution cryo-EM maps of large macromolecular complexes not amenable to crystallization. The connection between cryo-EM and MD evolved as cryo-EM maps improved in resolution, allowing advanced sampling algorithms to simultaneously refine backbone and sidechains. Moving beyond a single static snapshot, modern inferencing approaches integrate cryo-EM and MD to generate structural ensembles from cryo-EM map data or directly from the particle images themselves. We summarize the recent history of MD innovations in the area of cryo-EM modeling. The merits for the myriad of MD based cryo-EM modeling methods are discussed, as well as, the discoveries that were made possible by the integration of molecular modeling with cryo-EM. Lastly, current challenges and potential opportunities are reviewed.
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31
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Neijenhuis T, van Keulen SC, Bonvin AMJJ. Interface refinement of low- to medium-resolution Cryo-EM complexes using HADDOCK2.4. Structure 2022; 30:476-484.e3. [PMID: 35216656 DOI: 10.1016/j.str.2022.02.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2021] [Revised: 11/25/2021] [Accepted: 01/28/2022] [Indexed: 10/19/2022]
Abstract
A wide range of cellular processes requires the formation of multimeric protein complexes. The rise of cryo-electron microscopy (cryo-EM) has enabled the structural characterization of these protein assemblies. The density maps produced can, however, still suffer from limited resolution, impeding the process of resolving structures at atomic resolution. In order to solve this issue, monomers can be fitted into low- to medium-resolution maps. Unfortunately, the models produced frequently contain atomic clashes at the protein-protein interfaces (PPIs), as intermolecular interactions are typically not considered during monomer fitting. Here, we present a refinement approach based on HADDOCK2.4 to remove intermolecular clashes and optimize PPIs. A dataset of 14 cryo-EM complexes was used to test eight protocols. The best-performing protocol, consisting of a semi-flexible simulated annealing refinement with centroid restraints on the monomers, was able to decrease intermolecular atomic clashes by 98% without significantly deteriorating the quality of the cryo-EM density fit.
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Affiliation(s)
- Tim Neijenhuis
- Computational Structural Biology Group, Bijvoet Center for Biomolecular Research, Science for Life, Faculty of Science - Chemistry, Utrecht University, Padualaan 8, 3584 CH, Utrecht, the Netherlands
| | - Siri C van Keulen
- Computational Structural Biology Group, Bijvoet Center for Biomolecular Research, Science for Life, Faculty of Science - Chemistry, Utrecht University, Padualaan 8, 3584 CH, Utrecht, the Netherlands
| | - Alexandre M J J Bonvin
- Computational Structural Biology Group, Bijvoet Center for Biomolecular Research, Science for Life, Faculty of Science - Chemistry, Utrecht University, Padualaan 8, 3584 CH, Utrecht, the Netherlands.
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32
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Vuillemot R, Miyashita O, Tama F, Rouiller I, Jonic S. NMMD: Efficient cryo-EM flexible fitting based on simultaneous Normal Mode and Molecular Dynamics atomic displacements. J Mol Biol 2022; 434:167483. [PMID: 35150654 DOI: 10.1016/j.jmb.2022.167483] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2021] [Revised: 01/18/2022] [Accepted: 01/31/2022] [Indexed: 11/28/2022]
Abstract
Atomic models of cryo electron microscopy (cryo-EM) maps of biomolecular conformations are often obtained by flexible fitting of the maps with available atomic structures of other conformations (e.g., obtained by X-ray crystallography). This article presents a new flexible fitting method, NMMD, which combines normal mode analysis (NMA) and molecular dynamics simulation (MD). Given an atomic structure and a cryo-EM map to fit, NMMD simultaneously estimates global atomic displacements based on NMA and local displacements based on MD. NMMD was implemented by modifying EMfit, a flexible fitting method using MD only, in GENESIS 1.4. As EMfit, NMMD can be run with replica exchange umbrella sampling procedure. The new method was tested using a variety of EM maps (synthetic and experimental, with different noise levels and resolutions). The results of the tests show that adding normal modes to MD-based fitting makes the fitting faster (40% in average) and, in the majority of cases, more accurate.
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Affiliation(s)
- Rémi Vuillemot
- IMPMC - UMR 7590 CNRS, Sorbonne Université, Muséum National d'Histoire Naturelle, Paris, France; Bio21 Molecular Science and Biotechnology Institute, University of Melbourne, Victoria, Australia
| | | | - Florence Tama
- Institute of Transformative Biomolecules and Department of Physics, Graduate School of Science, Nagoya University, Japan
| | - Isabelle Rouiller
- Bio21 Molecular Science and Biotechnology Institute, University of Melbourne, Victoria, Australia
| | - Slavica Jonic
- IMPMC - UMR 7590 CNRS, Sorbonne Université, Muséum National d'Histoire Naturelle, Paris, France.
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33
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Britt HM, Cragnolini T, Thalassinos K. Integration of Mass Spectrometry Data for Structural Biology. Chem Rev 2021; 122:7952-7986. [PMID: 34506113 DOI: 10.1021/acs.chemrev.1c00356] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
Abstract
Mass spectrometry (MS) is increasingly being used to probe the structure and dynamics of proteins and the complexes they form with other macromolecules. There are now several specialized MS methods, each with unique sample preparation, data acquisition, and data processing protocols. Collectively, these methods are referred to as structural MS and include cross-linking, hydrogen-deuterium exchange, hydroxyl radical footprinting, native, ion mobility, and top-down MS. Each of these provides a unique type of structural information, ranging from composition and stoichiometry through to residue level proximity and solvent accessibility. Structural MS has proved particularly beneficial in studying protein classes for which analysis by classic structural biology techniques proves challenging such as glycosylated or intrinsically disordered proteins. To capture the structural details for a particular system, especially larger multiprotein complexes, more than one structural MS method with other structural and biophysical techniques is often required. Key to integrating these diverse data are computational strategies and software solutions to facilitate this process. We provide a background to the structural MS methods and briefly summarize other structural methods and how these are combined with MS. We then describe current state of the art approaches for the integration of structural MS data for structural biology. We quantify how often these methods are used together and provide examples where such combinations have been fruitful. To illustrate the power of integrative approaches, we discuss progress in solving the structures of the proteasome and the nuclear pore complex. We also discuss how information from structural MS, particularly pertaining to protein dynamics, is not currently utilized in integrative workflows and how such information can provide a more accurate picture of the systems studied. We conclude by discussing new developments in the MS and computational fields that will further enable in-cell structural studies.
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Affiliation(s)
- Hannah M Britt
- Institute of Structural and Molecular Biology, Division of Biosciences, University College London, London WC1E 6BT, United Kingdom
| | - Tristan Cragnolini
- Institute of Structural and Molecular Biology, Division of Biosciences, University College London, London WC1E 6BT, United Kingdom.,Institute of Structural and Molecular Biology, Birkbeck College, University of London, London WC1E 7HX, United Kingdom
| | - Konstantinos Thalassinos
- Institute of Structural and Molecular Biology, Division of Biosciences, University College London, London WC1E 6BT, United Kingdom.,Institute of Structural and Molecular Biology, Birkbeck College, University of London, London WC1E 7HX, United Kingdom
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34
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Mori T, Terashi G, Matsuoka D, Kihara D, Sugita Y. Efficient Flexible Fitting Refinement with Automatic Error Fixing for De Novo Structure Modeling from Cryo-EM Density Maps. J Chem Inf Model 2021; 61:3516-3528. [PMID: 34142833 PMCID: PMC9282639 DOI: 10.1021/acs.jcim.1c00230] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Structural modeling of proteins from cryo-electron microscopy (cryo-EM) density maps is one of the challenging issues in structural biology. De novo modeling combined with flexible fitting refinement (FFR) has been widely used to build a structure of new proteins. In de novo prediction, artificial conformations containing local structural errors such as chirality errors, cis peptide bonds, and ring penetrations are frequently generated and cannot be easily removed in the subsequent FFR. Moreover, refinement can be significantly suppressed due to the low mobility of atoms inside the protein. To overcome these problems, we propose an efficient scheme for FFR, in which the local structural errors are fixed first, followed by FFR using an iterative simulated annealing (SA) molecular dynamics protocol with the united atom (UA) model in an implicit solvent model; we call this scheme "SAUA-FFR". The best model is selected from multiple flexible fitting runs with various biasing force constants to reduce overfitting. We apply our scheme to the decoys obtained from MAINMAST and demonstrate an improvement of the best model of eight selected proteins in terms of the root-mean-square deviation, MolProbity score, and RWplus score compared to the original scheme of MAINMAST. Fixing the local structural errors can enhance the formation of secondary structures, and the UA model enables progressive refinement compared to the all-atom model owing to its high mobility in the implicit solvent. The SAUA-FFR scheme realizes efficient and accurate protein structure modeling from medium-resolution maps with less overfitting.
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Affiliation(s)
- Takaharu Mori
- RIKEN Cluster for Pioneering Research, 2-1 Hirosawa, Wako-shi, Saitama 351-0198, Japan
| | - Genki Terashi
- Department of Biological Sciences, Purdue University, West Lafayette, Indiana 47907, United States
| | - Daisuke Matsuoka
- RIKEN Cluster for Pioneering Research, 2-1 Hirosawa, Wako-shi, Saitama 351-0198, Japan
| | - Daisuke Kihara
- Department of Biological Sciences, Purdue University, West Lafayette, Indiana 47907, United States.,Department of Computer Science, Purdue University, West Lafayette, Indiana 47907, United States
| | - Yuji Sugita
- RIKEN Cluster for Pioneering Research, 2-1 Hirosawa, Wako-shi, Saitama 351-0198, Japan.,RIKEN Center for Computational Science, 7-1-26 Minatojima-minamimachi, Chuo-ku, Kobe, Hyogo 650-0047, Japan.,RIKEN Center for Biosystems Dynamics Research, 7-1-26 Minatojima-minamimachi, Chuo-ku, Kobe, Hyogo 650-0047, Japan
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35
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Israeli H, Degtjarik O, Fierro F, Chunilal V, Gill AK, Roth NJ, Botta J, Prabahar V, Peleg Y, Chan LF, Ben-Zvi D, McCormick PJ, Niv MY, Shalev-Benami M. Structure reveals the activation mechanism of the MC4 receptor to initiate satiation signaling. Science 2021; 372:808-814. [PMID: 33858992 DOI: 10.1126/science.abf7958] [Citation(s) in RCA: 39] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2020] [Accepted: 04/08/2021] [Indexed: 12/18/2022]
Abstract
Obesity is a global epidemic that causes morbidity and impaired quality of life. The melanocortin receptor 4 (MC4R) is at the crux of appetite, energy homeostasis, and body-weight control in the central nervous system and is a prime target for anti-obesity drugs. Here, we present the cryo-electron microscopy (cryo-EM) structure of the human MC4R-Gs signaling complex bound to the agonist setmelanotide, a cyclic peptide recently approved for the treatment of obesity. The work reveals the mechanism of MC4R activation, highlighting a molecular switch that initiates satiation signaling. In addition, our findings indicate that calcium (Ca2+) is required for agonist, but not antagonist, efficacy. These results fill a gap in the understanding of MC4R activation and could guide the design of future weight-management drugs.
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Affiliation(s)
- Hadar Israeli
- Department of Chemical and Structural Biology, Weizmann Institute of Science, Rehovot 7610001, Israel
- Department of Developmental Biology and Cancer Research, The Institute for Medical Research Israel-Canada, The Hebrew University-Hadassah Medical School, Jerusalem, Israel
| | - Oksana Degtjarik
- Department of Chemical and Structural Biology, Weizmann Institute of Science, Rehovot 7610001, Israel
| | - Fabrizio Fierro
- The Faculty of Agriculture, Food and Environment, The Hebrew University, Rehovot, Israel
- The Fritz Haber Center for Molecular Dynamics, The Hebrew University, Jerusalem, Israel
| | - Vidicha Chunilal
- Centre for Endocrinology, William Harvey Research Institute, Barts and the London School of Medicine, Queen Mary, University of London, Charterhouse Square, London, UK
| | - Amandeep Kaur Gill
- Centre for Endocrinology, William Harvey Research Institute, Barts and the London School of Medicine, Queen Mary, University of London, Charterhouse Square, London, UK
| | - Nicolas J Roth
- Centre for Endocrinology, William Harvey Research Institute, Barts and the London School of Medicine, Queen Mary, University of London, Charterhouse Square, London, UK
| | - Joaquin Botta
- Centre for Endocrinology, William Harvey Research Institute, Barts and the London School of Medicine, Queen Mary, University of London, Charterhouse Square, London, UK
| | - Vadivel Prabahar
- Department of Chemical and Structural Biology, Weizmann Institute of Science, Rehovot 7610001, Israel
| | - Yoav Peleg
- Structural Proteomics Unit (SPU), Life Sciences Core Facilities (LSCF), Weizmann Institute of Science, Rehovot 7610001, Israel
| | - Li F Chan
- Centre for Endocrinology, William Harvey Research Institute, Barts and the London School of Medicine, Queen Mary, University of London, Charterhouse Square, London, UK
| | - Danny Ben-Zvi
- Department of Developmental Biology and Cancer Research, The Institute for Medical Research Israel-Canada, The Hebrew University-Hadassah Medical School, Jerusalem, Israel.
| | - Peter J McCormick
- Centre for Endocrinology, William Harvey Research Institute, Barts and the London School of Medicine, Queen Mary, University of London, Charterhouse Square, London, UK.
| | - Masha Y Niv
- The Faculty of Agriculture, Food and Environment, The Hebrew University, Rehovot, Israel.
- The Fritz Haber Center for Molecular Dynamics, The Hebrew University, Jerusalem, Israel
| | - Moran Shalev-Benami
- Department of Chemical and Structural Biology, Weizmann Institute of Science, Rehovot 7610001, Israel.
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36
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Kamenik AS, Handle PH, Hofer F, Kahler U, Kraml J, Liedl KR. Polarizable and non-polarizable force fields: Protein folding, unfolding, and misfolding. J Chem Phys 2021; 153:185102. [PMID: 33187403 DOI: 10.1063/5.0022135] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
Abstract
Molecular dynamics simulations are an invaluable tool to characterize the dynamic motions of proteins in atomistic detail. However, the accuracy of models derived from simulations inevitably relies on the quality of the underlying force field. Here, we present an evaluation of current non-polarizable and polarizable force fields (AMBER ff14SB, CHARMM 36m, GROMOS 54A7, and Drude 2013) based on the long-standing biophysical challenge of protein folding. We quantify the thermodynamics and kinetics of the β-hairpin formation using Markov state models of the fast-folding mini-protein CLN025. Furthermore, we study the (partial) folding dynamics of two more complex systems, a villin headpiece variant and a WW domain. Surprisingly, the polarizable force field in our set, Drude 2013, consistently leads to destabilization of the native state, regardless of the secondary structure element present. All non-polarizable force fields, on the other hand, stably characterize the native state ensembles in most cases even when starting from a partially unfolded conformation. Focusing on CLN025, we find that the conformational space captured with AMBER ff14SB and CHARMM 36m is comparable, but the ensembles from CHARMM 36m simulations are clearly shifted toward disordered conformations. While the AMBER ff14SB ensemble overstabilizes the native fold, CHARMM 36m and GROMOS 54A7 ensembles both agree remarkably well with experimental state populations. In addition, GROMOS 54A7 also reproduces experimental folding times most accurately. Our results further indicate an over-stabilization of helical structures with AMBER ff14SB. Nevertheless, the presented investigations strongly imply that reliable (un)folding dynamics of small proteins can be captured in feasible computational time with current additive force fields.
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Affiliation(s)
- Anna S Kamenik
- Institute of General, Inorganic and Theoretical Chemistry, University of Innsbruck, Innrain 80/82, A-6020 Innsbruck, Austria
| | - Philip H Handle
- Institute of General, Inorganic and Theoretical Chemistry, University of Innsbruck, Innrain 80/82, A-6020 Innsbruck, Austria
| | - Florian Hofer
- Institute of General, Inorganic and Theoretical Chemistry, University of Innsbruck, Innrain 80/82, A-6020 Innsbruck, Austria
| | - Ursula Kahler
- Institute of General, Inorganic and Theoretical Chemistry, University of Innsbruck, Innrain 80/82, A-6020 Innsbruck, Austria
| | - Johannes Kraml
- Institute of General, Inorganic and Theoretical Chemistry, University of Innsbruck, Innrain 80/82, A-6020 Innsbruck, Austria
| | - Klaus R Liedl
- Institute of General, Inorganic and Theoretical Chemistry, University of Innsbruck, Innrain 80/82, A-6020 Innsbruck, Austria
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37
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Williams AH, Zhan CG. Fast Prediction of Binding Affinities of the SARS-CoV-2 Spike Protein Mutant N501Y (UK Variant) with ACE2 and Miniprotein Drug Candidates. J Phys Chem B 2021; 125:4330-4336. [PMID: 33881861 PMCID: PMC8084269 DOI: 10.1021/acs.jpcb.1c00869] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2021] [Revised: 03/30/2021] [Indexed: 12/28/2022]
Abstract
A recently identified variant of SARS-CoV-2 virus, known as the United Kingdom (UK) variant (lineage B.1.1.7), has an N501Y mutation on its spike protein. SARS-CoV-2 spike protein binds with angiotensin-converting enzyme 2 (ACE2), a key protein for the viral entry into the host cells. Here, we report an efficient computational approach, including the simple energy minimizations and binding free energy calculations, starting from an experimental structure of the binding complex along with experimental calibration of the calculated binding free energies, to rapidly and reliably predict the binding affinities of the N501Y mutant with human ACE2 (hACE2) and recently reported miniprotein and hACE2 decoy (CTC-445.2) drug candidates. It has been demonstrated that the N501Y mutation markedly increases the ACE2-spike protein binding affinity (Kd) from 22 to 0.44 nM, which could partially explain why the UK variant is more infectious. The miniproteins are predicted to have ∼10,000- to 100,000-fold diminished binding affinities with the N501Y mutant, creating a need for design of novel therapeutic candidates to overcome the N501Y mutation-induced drug resistance. The N501Y mutation is also predicted to decrease the binding affinity of a hACE2 decoy (CTC-445.2) binding with the spike protein by ∼200-fold. This convenient computational approach along with experimental calibration may be similarly used in the future to predict the binding affinities of potential new variants of the spike protein.
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Affiliation(s)
- Alexander H. Williams
- Molecular Modeling and Biopharmaceutical Center, College of Pharmacy, University of Kentucky, 789 South Limestone Street, Lexington, KY 40536
- Department of Pharmaceutical Sciences, College of Pharmacy, University of Kentucky, 789 South Limestone Street, Lexington, KY 40536
| | - Chang-Guo Zhan
- Molecular Modeling and Biopharmaceutical Center, College of Pharmacy, University of Kentucky, 789 South Limestone Street, Lexington, KY 40536
- Department of Pharmaceutical Sciences, College of Pharmacy, University of Kentucky, 789 South Limestone Street, Lexington, KY 40536
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38
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Bous J, Orcel H, Floquet N, Leyrat C, Lai-Kee-Him J, Gaibelet G, Ancelin A, Saint-Paul J, Trapani S, Louet M, Sounier R, Déméné H, Granier S, Bron P, Mouillac B. Cryo-electron microscopy structure of the antidiuretic hormone arginine-vasopressin V2 receptor signaling complex. SCIENCE ADVANCES 2021; 7:7/21/eabg5628. [PMID: 34020960 PMCID: PMC8139594 DOI: 10.1126/sciadv.abg5628] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/14/2021] [Accepted: 04/01/2021] [Indexed: 05/08/2023]
Abstract
The antidiuretic hormone arginine-vasopressin (AVP) forms a signaling complex with the V2 receptor (V2R) and the Gs protein, promoting kidney water reabsorption. Molecular mechanisms underlying activation of this critical G protein-coupled receptor (GPCR) signaling system are still unknown. To fill this gap of knowledge, we report here the cryo-electron microscopy structure of the AVP-V2R-Gs complex. Single-particle analysis revealed the presence of three different states. The two best maps were combined with computational and nuclear magnetic resonance spectroscopy constraints to reconstruct two structures of the ternary complex. These structures differ in AVP and Gs binding modes. They reveal an original receptor-Gs interface in which the Gαs subunit penetrates deep into the active V2R. The structures help to explain how V2R R137H or R137L/C variants can lead to two severe genetic diseases. Our study provides important structural insights into the function of this clinically relevant GPCR signaling complex.
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Affiliation(s)
- Julien Bous
- Institut de Génomique Fonctionnelle, Université de Montpellier, CNRS, INSERM, 34094 Montpellier cedex 5, France
- Centre de Biochimie Structurale, Université de Montpellier, CNRS, INSERM, 34090 Montpellier, France
| | - Hélène Orcel
- Institut de Génomique Fonctionnelle, Université de Montpellier, CNRS, INSERM, 34094 Montpellier cedex 5, France
| | - Nicolas Floquet
- Institut des Biomolécules Max Mousseron, Université de Montpellier, CNRS, ENSCM, 34093 Montpellier cedex 5, France
| | - Cédric Leyrat
- Institut de Génomique Fonctionnelle, Université de Montpellier, CNRS, INSERM, 34094 Montpellier cedex 5, France
| | - Joséphine Lai-Kee-Him
- Centre de Biochimie Structurale, Université de Montpellier, CNRS, INSERM, 34090 Montpellier, France
| | - Gérald Gaibelet
- Institut de Génomique Fonctionnelle, Université de Montpellier, CNRS, INSERM, 34094 Montpellier cedex 5, France
| | - Aurélie Ancelin
- Centre de Biochimie Structurale, Université de Montpellier, CNRS, INSERM, 34090 Montpellier, France
| | - Julie Saint-Paul
- Institut de Génomique Fonctionnelle, Université de Montpellier, CNRS, INSERM, 34094 Montpellier cedex 5, France
| | - Stefano Trapani
- Centre de Biochimie Structurale, Université de Montpellier, CNRS, INSERM, 34090 Montpellier, France
| | - Maxime Louet
- Institut des Biomolécules Max Mousseron, Université de Montpellier, CNRS, ENSCM, 34093 Montpellier cedex 5, France
| | - Rémy Sounier
- Institut de Génomique Fonctionnelle, Université de Montpellier, CNRS, INSERM, 34094 Montpellier cedex 5, France
| | - Hélène Déméné
- Centre de Biochimie Structurale, Université de Montpellier, CNRS, INSERM, 34090 Montpellier, France
| | - Sébastien Granier
- Institut de Génomique Fonctionnelle, Université de Montpellier, CNRS, INSERM, 34094 Montpellier cedex 5, France.
| | - Patrick Bron
- Centre de Biochimie Structurale, Université de Montpellier, CNRS, INSERM, 34090 Montpellier, France.
| | - Bernard Mouillac
- Institut de Génomique Fonctionnelle, Université de Montpellier, CNRS, INSERM, 34094 Montpellier cedex 5, France.
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39
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Nierzwicki Ł, Palermo G. Molecular Dynamics to Predict Cryo-EM: Capturing Transitions and Short-Lived Conformational States of Biomolecules. Front Mol Biosci 2021; 8:641208. [PMID: 33884260 PMCID: PMC8053777 DOI: 10.3389/fmolb.2021.641208] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2020] [Accepted: 02/15/2021] [Indexed: 12/21/2022] Open
Abstract
Single-particle cryogenic electron microscopy (cryo-EM) has revolutionized the field of the structural biology, providing an access to the atomic resolution structures of large biomolecular complexes in their near-native environment. Today's cryo-EM maps can frequently reach the atomic-level resolution, while often containing a range of resolutions, with conformationally variable regions obtained at 6 Å or worse. Low resolution density maps obtained for protein flexible domains, as well as the ensemble of coexisting conformational states arising from cryo-EM, poses new challenges and opportunities for Molecular Dynamics (MD) simulations. With the ability to describe the biomolecular dynamics at the atomic level, MD can extend the capabilities of cryo-EM, capturing the conformational variability and predicting biologically relevant short-lived conformational states. Here, we report about the state-of-the-art MD procedures that are currently used to refine, reconstruct and interpret cryo-EM maps. We show the capability of MD to predict short-lived conformational states, finding remarkable confirmation by cryo-EM structures subsequently solved. This has been the case of the CRISPR-Cas9 genome editing machinery, whose catalytically active structure has been predicted through both long-time scale MD and enhanced sampling techniques 2 years earlier than cryo-EM. In summary, this contribution remarks the ability of MD to complement cryo-EM, describing conformational landscapes and relating structural transitions to function, ultimately discerning relevant short-lived conformational states and providing mechanistic knowledge of biological function.
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Affiliation(s)
- Łukasz Nierzwicki
- Department of Bioengineering, University of California, Riverside, CA, United States
| | - Giulia Palermo
- Department of Bioengineering, University of California, Riverside, CA, United States
- Department of Chemistry, University of California, Riverside, CA, United States
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40
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Lawson CL, Kryshtafovych A, Adams PD, Afonine PV, Baker ML, Barad BA, Bond P, Burnley T, Cao R, Cheng J, Chojnowski G, Cowtan K, Dill KA, DiMaio F, Farrell DP, Fraser JS, Herzik MA, Hoh SW, Hou J, Hung LW, Igaev M, Joseph AP, Kihara D, Kumar D, Mittal S, Monastyrskyy B, Olek M, Palmer CM, Patwardhan A, Perez A, Pfab J, Pintilie GD, Richardson JS, Rosenthal PB, Sarkar D, Schäfer LU, Schmid MF, Schröder GF, Shekhar M, Si D, Singharoy A, Terashi G, Terwilliger TC, Vaiana A, Wang L, Wang Z, Wankowicz SA, Williams CJ, Winn M, Wu T, Yu X, Zhang K, Berman HM, Chiu W. Cryo-EM model validation recommendations based on outcomes of the 2019 EMDataResource challenge. Nat Methods 2021; 18:156-164. [PMID: 33542514 PMCID: PMC7864804 DOI: 10.1038/s41592-020-01051-w] [Citation(s) in RCA: 61] [Impact Index Per Article: 20.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2020] [Accepted: 12/21/2020] [Indexed: 01/30/2023]
Abstract
This paper describes outcomes of the 2019 Cryo-EM Model Challenge. The goals were to (1) assess the quality of models that can be produced from cryogenic electron microscopy (cryo-EM) maps using current modeling software, (2) evaluate reproducibility of modeling results from different software developers and users and (3) compare performance of current metrics used for model evaluation, particularly Fit-to-Map metrics, with focus on near-atomic resolution. Our findings demonstrate the relatively high accuracy and reproducibility of cryo-EM models derived by 13 participating teams from four benchmark maps, including three forming a resolution series (1.8 to 3.1 Å). The results permit specific recommendations to be made about validating near-atomic cryo-EM structures both in the context of individual experiments and structure data archives such as the Protein Data Bank. We recommend the adoption of multiple scoring parameters to provide full and objective annotation and assessment of the model, reflective of the observed cryo-EM map density.
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Affiliation(s)
- Catherine L. Lawson
- grid.430387.b0000 0004 1936 8796Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ USA
| | - Andriy Kryshtafovych
- grid.27860.3b0000 0004 1936 9684Genome Center, University of California, Davis, CA USA
| | - Paul D. Adams
- grid.184769.50000 0001 2231 4551Molecular Biophysics and Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, CA USA ,grid.47840.3f0000 0001 2181 7878Department of Bioengineering, University of California Berkeley, Berkeley, CA USA
| | - Pavel V. Afonine
- grid.184769.50000 0001 2231 4551Molecular Biophysics and Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, CA USA
| | - Matthew L. Baker
- grid.267308.80000 0000 9206 2401Department of Biochemistry and Molecular Biology, The University of Texas Health Science Center at Houston, Houston, TX USA
| | - Benjamin A. Barad
- grid.214007.00000000122199231Department of Integrated Computational Structural Biology, The Scripps Research Institute, La Jolla, CA USA
| | - Paul Bond
- grid.5685.e0000 0004 1936 9668York Structural Biology Laboratory, Department of Chemistry, University of York, York, UK
| | - Tom Burnley
- grid.465239.fScientific Computing Department, UKRI Science and Technology Facilities Council, Research Complex at Harwell, Didcot, UK
| | - Renzhi Cao
- grid.261584.c0000 0001 0492 9915Department of Computer Science, Pacific Lutheran University, Tacoma, WA USA
| | - Jianlin Cheng
- grid.134936.a0000 0001 2162 3504Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO USA
| | - Grzegorz Chojnowski
- grid.475756.20000 0004 0444 5410European Molecular Biology Laboratory, c/o DESY, Hamburg, Germany
| | - Kevin Cowtan
- grid.5685.e0000 0004 1936 9668York Structural Biology Laboratory, Department of Chemistry, University of York, York, UK
| | - Ken A. Dill
- grid.36425.360000 0001 2216 9681Laufer Center, Stony Brook University, Stony Brook, NY USA
| | - Frank DiMaio
- grid.34477.330000000122986657Department of Biochemistry and Institute for Protein Design, University of Washington, Seattle, WA USA
| | - Daniel P. Farrell
- grid.34477.330000000122986657Department of Biochemistry and Institute for Protein Design, University of Washington, Seattle, WA USA
| | - James S. Fraser
- grid.266102.10000 0001 2297 6811Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA USA
| | - Mark A. Herzik
- grid.266100.30000 0001 2107 4242Department of Chemistry and Biochemistry, University of California, San Diego, La Jolla, CA USA
| | - Soon Wen Hoh
- grid.5685.e0000 0004 1936 9668York Structural Biology Laboratory, Department of Chemistry, University of York, York, UK
| | - Jie Hou
- grid.262962.b0000 0004 1936 9342Department of Computer Science, Saint Louis University, St. Louis, MO USA
| | - Li-Wei Hung
- grid.148313.c0000 0004 0428 3079Los Alamos National Laboratory, Los Alamos, NM USA
| | - Maxim Igaev
- grid.418140.80000 0001 2104 4211Theoretical and Computational Biophysics, Max Planck Institute for Biophysical Chemistry, Göttingen, Germany
| | - Agnel P. Joseph
- grid.465239.fScientific Computing Department, UKRI Science and Technology Facilities Council, Research Complex at Harwell, Didcot, UK
| | - Daisuke Kihara
- grid.169077.e0000 0004 1937 2197Department of Biological Sciences, Purdue University, West Lafayette, IN USA ,grid.169077.e0000 0004 1937 2197Department of Computer Science, Purdue University, West Lafayette, IN USA
| | - Dilip Kumar
- grid.39382.330000 0001 2160 926XVerna and Marrs McLean Department of Biochemistry and Molecular Biology, Baylor College of Medicine, Houston, TX USA
| | - Sumit Mittal
- grid.215654.10000 0001 2151 2636Biodesign Institute, Arizona State University, Tempe, AZ USA ,grid.411530.20000 0001 0694 3745School of Advanced Sciences and Languages, VIT Bhopal University, Bhopal, India
| | - Bohdan Monastyrskyy
- grid.27860.3b0000 0004 1936 9684Genome Center, University of California, Davis, CA USA
| | - Mateusz Olek
- grid.5685.e0000 0004 1936 9668York Structural Biology Laboratory, Department of Chemistry, University of York, York, UK
| | - Colin M. Palmer
- grid.465239.fScientific Computing Department, UKRI Science and Technology Facilities Council, Research Complex at Harwell, Didcot, UK
| | - Ardan Patwardhan
- grid.225360.00000 0000 9709 7726The European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, UK
| | - Alberto Perez
- grid.15276.370000 0004 1936 8091Department of Chemistry, University of Florida, Gainesville, FL USA
| | - Jonas Pfab
- grid.462982.30000 0000 8883 2602Division of Computing & Software Systems, University of Washington, Bothell, WA USA
| | - Grigore D. Pintilie
- grid.168010.e0000000419368956Department of Bioengineering, Stanford University, Stanford, CA USA
| | - Jane S. Richardson
- grid.26009.3d0000 0004 1936 7961Department of Biochemistry, Duke University, Durham, NC USA
| | - Peter B. Rosenthal
- grid.451388.30000 0004 1795 1830Structural Biology of Cells and Viruses Laboratory, Francis Crick Institute, London, UK
| | - Daipayan Sarkar
- grid.169077.e0000 0004 1937 2197Department of Biological Sciences, Purdue University, West Lafayette, IN USA ,grid.215654.10000 0001 2151 2636Biodesign Institute, Arizona State University, Tempe, AZ USA
| | - Luisa U. Schäfer
- grid.8385.60000 0001 2297 375XInstitute of Biological Information Processing (IBI-7: Structural Biochemistry) and Jülich Centre for Structural Biology (JuStruct), Forschungszentrum Jülich, Jülich, Germany
| | - Michael F. Schmid
- grid.168010.e0000000419368956Division of CryoEM and Biomaging, SSRL, SLAC National Accelerator Laboratory, Stanford University, Menlo Park, CA USA
| | - Gunnar F. Schröder
- grid.8385.60000 0001 2297 375XInstitute of Biological Information Processing (IBI-7: Structural Biochemistry) and Jülich Centre for Structural Biology (JuStruct), Forschungszentrum Jülich, Jülich, Germany ,grid.411327.20000 0001 2176 9917Physics Department, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Mrinal Shekhar
- grid.215654.10000 0001 2151 2636Biodesign Institute, Arizona State University, Tempe, AZ USA ,grid.66859.34Center for Development of Therapeutics, Broad Institute of MIT and Harvard, Cambridge, MA USA
| | - Dong Si
- grid.462982.30000 0000 8883 2602Division of Computing & Software Systems, University of Washington, Bothell, WA USA
| | - Abishek Singharoy
- grid.215654.10000 0001 2151 2636Biodesign Institute, Arizona State University, Tempe, AZ USA
| | - Genki Terashi
- grid.418140.80000 0001 2104 4211Theoretical and Computational Biophysics, Max Planck Institute for Biophysical Chemistry, Göttingen, Germany
| | | | - Andrea Vaiana
- grid.418140.80000 0001 2104 4211Theoretical and Computational Biophysics, Max Planck Institute for Biophysical Chemistry, Göttingen, Germany
| | - Liguo Wang
- grid.34477.330000000122986657Department of Biological Structure, University of Washington, Seattle, WA USA
| | - Zhe Wang
- grid.225360.00000 0000 9709 7726The European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, UK
| | - Stephanie A. Wankowicz
- grid.266102.10000 0001 2297 6811Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA USA ,grid.266102.10000 0001 2297 6811Biophysics Graduate Program, University of California, San Francisco, CA USA
| | | | - Martyn Winn
- grid.465239.fScientific Computing Department, UKRI Science and Technology Facilities Council, Research Complex at Harwell, Didcot, UK
| | - Tianqi Wu
- grid.134936.a0000 0001 2162 3504Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO USA
| | - Xiaodi Yu
- grid.497530.c0000 0004 0389 4927SMPS, Janssen Research and Development, Spring House, PA USA
| | - Kaiming Zhang
- grid.168010.e0000000419368956Department of Bioengineering, Stanford University, Stanford, CA USA
| | - Helen M. Berman
- grid.430387.b0000 0004 1936 8796Department of Chemistry and Chemical Biology, Rutgers, The State University of New Jersey, Piscataway, NJ USA ,grid.42505.360000 0001 2156 6853Department of Biological Sciences and Bridge Institute, University of Southern California, Los Angeles, CA USA
| | - Wah Chiu
- grid.168010.e0000000419368956Department of Bioengineering, Stanford University, Stanford, CA USA ,grid.168010.e0000000419368956Division of CryoEM and Biomaging, SSRL, SLAC National Accelerator Laboratory, Stanford University, Menlo Park, CA USA
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Risselada HJ, Grubmüller H. How proteins open fusion pores: insights from molecular simulations. EUROPEAN BIOPHYSICS JOURNAL: EBJ 2020; 50:279-293. [PMID: 33340336 PMCID: PMC8071795 DOI: 10.1007/s00249-020-01484-3] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/04/2020] [Revised: 11/16/2020] [Accepted: 11/24/2020] [Indexed: 02/06/2023]
Abstract
Fusion proteins can play a versatile and involved role during all stages of the fusion reaction. Their roles go far beyond forcing the opposing membranes into close proximity to drive stalk formation and fusion. Molecular simulations have played a central role in providing a molecular understanding of how fusion proteins actively overcome the free energy barriers of the fusion reaction up to the expansion of the fusion pore. Unexpectedly, molecular simulations have revealed a preference of the biological fusion reaction to proceed through asymmetric pathways resulting in the formation of, e.g., a stalk-hole complex, rim-pore, or vertex pore. Force-field based molecular simulations are now able to directly resolve the minimum free-energy path in protein-mediated fusion as well as quantifying the free energies of formed reaction intermediates. Ongoing developments in Graphics Processing Units (GPUs), free energy calculations, and coarse-grained force-fields will soon gain additional insights into the diverse roles of fusion proteins.
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Affiliation(s)
- H Jelger Risselada
- Department of Theoretical Physics, Georg-August University of Göttingen, Göttingen, Germany. .,Leiden University, Leiden Institute of Chemistry (LIC), Leiden, The Netherlands.
| | - Helmut Grubmüller
- Max Planck Institute for Biophysical Chemistry, Theoretical and Computational Biophysics Department, Göttingen, Germany
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42
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Vant JW, Sarkar D, Streitwieser E, Fiorin G, Skeel R, Vermaas JV, Singharoy A. Data-guided Multi-Map variables for ensemble refinement of molecular movies. J Chem Phys 2020; 153:214102. [PMID: 33291927 PMCID: PMC7714525 DOI: 10.1063/5.0022433] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2020] [Accepted: 11/09/2020] [Indexed: 12/17/2022] Open
Abstract
Driving molecular dynamics simulations with data-guided collective variables offer a promising strategy to recover thermodynamic information from structure-centric experiments. Here, the three-dimensional electron density of a protein, as it would be determined by cryo-EM or x-ray crystallography, is used to achieve simultaneously free-energy costs of conformational transitions and refined atomic structures. Unlike previous density-driven molecular dynamics methodologies that determine only the best map-model fits, our work employs the recently developed Multi-Map methodology to monitor concerted movements within equilibrium, non-equilibrium, and enhanced sampling simulations. Construction of all-atom ensembles along the chosen values of the Multi-Map variable enables simultaneous estimation of average properties, as well as real-space refinement of the structures contributing to such averages. Using three proteins of increasing size, we demonstrate that biased simulation along the reaction coordinates derived from electron densities can capture conformational transitions between known intermediates. The simulated pathways appear reversible with minimal hysteresis and require only low-resolution density information to guide the transition. The induced transitions also produce estimates for free energy differences that can be directly compared to experimental observables and population distributions. The refined model quality is superior compared to those found in the Protein Data Bank. We find that the best quantitative agreement with experimental free-energy differences is obtained using medium resolution density information coupled to comparatively large structural transitions. Practical considerations for probing the transitions between multiple intermediate density states are also discussed.
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Affiliation(s)
- John W. Vant
- School of Molecular Sciences, Arizona State University, Tempe, Arizona 85281, USA
| | | | - Ellen Streitwieser
- School of Molecular Sciences, Arizona State University, Tempe, Arizona 85281, USA
| | - Giacomo Fiorin
- Theoretical Molecular Biophysics Laboratory, National Heart, Lung and Blood Institute, National Institutes of Health, 10 Center Drive, Bethesda, Maryland 20814, USA
| | - Robert Skeel
- School of Mathematical and Statistical Sciences, Arizona State University, Tempe, Arizona 85281, USA
| | - Josh V. Vermaas
- Computing and Computational Sciences Directorate, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37830, USA
| | - Abhishek Singharoy
- School of Molecular Sciences, Arizona State University, Tempe, Arizona 85281, USA
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43
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Kube M, Kohler F, Feigl E, Nagel-Yüksel B, Willner EM, Funke JJ, Gerling T, Stömmer P, Honemann MN, Martin TG, Scheres SHW, Dietz H. Revealing the structures of megadalton-scale DNA complexes with nucleotide resolution. Nat Commun 2020; 11:6229. [PMID: 33277481 PMCID: PMC7718922 DOI: 10.1038/s41467-020-20020-7] [Citation(s) in RCA: 32] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2020] [Accepted: 11/11/2020] [Indexed: 11/25/2022] Open
Abstract
The methods of DNA nanotechnology enable the rational design of custom shapes that self-assemble in solution from sets of DNA molecules. DNA origami, in which a long template DNA single strand is folded by many short DNA oligonucleotides, can be employed to make objects comprising hundreds of unique DNA strands and thousands of base pairs, thus in principle providing many degrees of freedom for modelling complex objects of defined 3D shapes and sizes. Here, we address the problem of accurate structural validation of DNA objects in solution with cryo-EM based methodologies. By taking into account structural fluctuations, we can determine structures with improved detail compared to previous work. To interpret the experimental cryo-EM maps, we present molecular-dynamics-based methods for building pseudo-atomic models in a semi-automated fashion. Among other features, our data allows discerning details such as helical grooves, single-strand versus double-strand crossovers, backbone phosphate positions, and single-strand breaks. Obtaining this higher level of detail is a step forward that now allows designers to inspect and refine their designs with base-pair level interventions. Precision design of DNA origami needs precision validation. Here, the authors developed cryo-EM methods for obtaining high resolution structural data and for constructing pseudo-atomic models in a semi-automated fashion, allowing for iterative nanodevice inspection and refinement.
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Affiliation(s)
- Massimo Kube
- Physik Department, Technische Universität München, Garching, Germany
| | - Fabian Kohler
- Physik Department, Technische Universität München, Garching, Germany
| | - Elija Feigl
- Physik Department, Technische Universität München, Garching, Germany
| | - Baki Nagel-Yüksel
- Physik Department, Technische Universität München, Garching, Germany
| | - Elena M Willner
- Physik Department, Technische Universität München, Garching, Germany
| | - Jonas J Funke
- Physik Department, Technische Universität München, Garching, Germany
| | - Thomas Gerling
- Physik Department, Technische Universität München, Garching, Germany
| | - Pierre Stömmer
- Physik Department, Technische Universität München, Garching, Germany
| | | | | | | | - Hendrik Dietz
- Physik Department, Technische Universität München, Garching, Germany.
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44
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Cárdenas R, Martínez-Seoane J, Amero C. Combining Experimental Data and Computational Methods for the Non-Computer Specialist. Molecules 2020; 25:E4783. [PMID: 33081072 PMCID: PMC7594097 DOI: 10.3390/molecules25204783] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2020] [Revised: 08/25/2020] [Accepted: 08/28/2020] [Indexed: 01/01/2023] Open
Abstract
Experimental methods are indispensable for the study of the function of biological macromolecules, not just as static structures, but as dynamic systems that change conformation, bind partners, perform reactions, and respond to different stimulus. However, providing a detailed structural interpretation of the results is often a very challenging task. While experimental and computational methods are often considered as two different and separate approaches, the power and utility of combining both is undeniable. The integration of the experimental data with computational techniques can assist and enrich the interpretation, providing new detailed molecular understanding of the systems. Here, we briefly describe the basic principles of how experimental data can be combined with computational methods to obtain insights into the molecular mechanism and expand the interpretation through the generation of detailed models.
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Affiliation(s)
| | | | - Carlos Amero
- Laboratorio de Bioquímica y Resonancia Magnética Nuclear, Centro de Investigaciones Químicas, Instituto de Investigación en Ciencias Básicas y Aplicadas, Universidad Autónoma del Estado de Morelos, Cuernavaca, Morelos 62209, Mexico; (R.C.); (J.M.-S.)
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45
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Turoňová B, Sikora M, Schürmann C, Hagen WJH, Welsch S, Blanc FEC, von Bülow S, Gecht M, Bagola K, Hörner C, van Zandbergen G, Landry J, de Azevedo NTD, Mosalaganti S, Schwarz A, Covino R, Mühlebach MD, Hummer G, Krijnse Locker J, Beck M. In situ structural analysis of SARS-CoV-2 spike reveals flexibility mediated by three hinges. Science 2020; 370:203-208. [PMID: 32817270 DOI: 10.1101/2020.06.26.173476] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2020] [Accepted: 08/13/2020] [Indexed: 05/24/2023]
Abstract
The spike protein (S) of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is required for cell entry and is the primary focus for vaccine development. In this study, we combined cryo-electron tomography, subtomogram averaging, and molecular dynamics simulations to structurally analyze S in situ. Compared with the recombinant S, the viral S was more heavily glycosylated and occurred mostly in the closed prefusion conformation. We show that the stalk domain of S contains three hinges, giving the head unexpected orientational freedom. We propose that the hinges allow S to scan the host cell surface, shielded from antibodies by an extensive glycan coat. The structure of native S contributes to our understanding of SARS-CoV-2 infection and potentially to the development of safe vaccines.
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Affiliation(s)
- Beata Turoňová
- Structural and Computational Biology Unit, European Molecular Biology Laboratory (EMBL), Meyerhofstr. 1, 69117 Heidelberg, Germany
- Department of Molecular Sociology, Max Planck Institute of Biophysics, Max-von-Laue Str. 3, 60438 Frankfurt am Main, Germany
| | - Mateusz Sikora
- Department of Theoretical Biophysics, Max Planck Institute of Biophysics, Max-von-Laue Str. 3, 60438 Frankfurt am Main, Germany
| | - Christoph Schürmann
- Division of Veterinary Medicine, Paul Ehrlich Institute, Paul Ehrlich Strasse 51-59, 63225 Langen, Germany
| | - Wim J H Hagen
- Structural and Computational Biology Unit, European Molecular Biology Laboratory (EMBL), Meyerhofstr. 1, 69117 Heidelberg, Germany
| | - Sonja Welsch
- Central Electron Microscopy Facility, Max Planck Institute of Biophysics, Max-von-Laue Str. 3, 60438 Frankfurt am Main, Germany
| | - Florian E C Blanc
- Department of Theoretical Biophysics, Max Planck Institute of Biophysics, Max-von-Laue Str. 3, 60438 Frankfurt am Main, Germany
| | - Sören von Bülow
- Department of Theoretical Biophysics, Max Planck Institute of Biophysics, Max-von-Laue Str. 3, 60438 Frankfurt am Main, Germany
| | - Michael Gecht
- Department of Theoretical Biophysics, Max Planck Institute of Biophysics, Max-von-Laue Str. 3, 60438 Frankfurt am Main, Germany
| | - Katrin Bagola
- Division of Immunology, Paul Ehrlich Institute, Paul Ehrlich Strasse 51-59, 63225 Langen, Germany
| | - Cindy Hörner
- Division of Veterinary Medicine, Paul Ehrlich Institute, Paul Ehrlich Strasse 51-59, 63225 Langen, Germany
- German Center for Infection Research, Gießen-Marburg-Langen, Germany
| | - Ger van Zandbergen
- Division of Immunology, Paul Ehrlich Institute, Paul Ehrlich Strasse 51-59, 63225 Langen, Germany
- Institute for Immunology, University Medical Center, Johannes Gutenberg University Mainz, Mainz, Germany
- Research Center for Immunotherapy (FZI), University Medical Center, Johannes Gutenberg-University Mainz, Mainz, Germany
| | - Jonathan Landry
- Genomics Core Facility, EMBL, Meyerhofstr. 1, 69117 Heidelberg, Germany
| | | | - Shyamal Mosalaganti
- Structural and Computational Biology Unit, European Molecular Biology Laboratory (EMBL), Meyerhofstr. 1, 69117 Heidelberg, Germany
- Department of Molecular Sociology, Max Planck Institute of Biophysics, Max-von-Laue Str. 3, 60438 Frankfurt am Main, Germany
| | - Andre Schwarz
- Structural and Computational Biology Unit, European Molecular Biology Laboratory (EMBL), Meyerhofstr. 1, 69117 Heidelberg, Germany
| | - Roberto Covino
- Department of Theoretical Biophysics, Max Planck Institute of Biophysics, Max-von-Laue Str. 3, 60438 Frankfurt am Main, Germany
- Frankfurt Institute for Advanced Studies, Ruth-Moufang-Str. 1, 60438 Frankfurt am Main, Germany
| | - Michael D Mühlebach
- Division of Veterinary Medicine, Paul Ehrlich Institute, Paul Ehrlich Strasse 51-59, 63225 Langen, Germany
- German Center for Infection Research, Gießen-Marburg-Langen, Germany
| | - Gerhard Hummer
- Department of Theoretical Biophysics, Max Planck Institute of Biophysics, Max-von-Laue Str. 3, 60438 Frankfurt am Main, Germany.
- Institute of Biophysics, Goethe University Frankfurt, 60438 Frankfurt am Main, Germany
| | - Jacomine Krijnse Locker
- Electron Microscopy of Pathogens Unit, Paul Ehrlich Institute, Paul Ehrlich Strasse 51-59, 63225 Langen, Germany.
| | - Martin Beck
- Structural and Computational Biology Unit, European Molecular Biology Laboratory (EMBL), Meyerhofstr. 1, 69117 Heidelberg, Germany.
- Department of Molecular Sociology, Max Planck Institute of Biophysics, Max-von-Laue Str. 3, 60438 Frankfurt am Main, Germany
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46
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Igaev M, Grubmüller H. Microtubule instability driven by longitudinal and lateral strain propagation. PLoS Comput Biol 2020; 16:e1008132. [PMID: 32877399 PMCID: PMC7467311 DOI: 10.1371/journal.pcbi.1008132] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2020] [Accepted: 07/09/2020] [Indexed: 12/21/2022] Open
Abstract
Tubulin dimers associate longitudinally and laterally to form metastable microtubules (MTs). MT disassembly is preceded by subtle structural changes in tubulin fueled by GTP hydrolysis. These changes render the MT lattice unstable, but it is unclear exactly how they affect lattice energetics and strain. We performed long-time atomistic simulations to interrogate the impacts of GTP hydrolysis on tubulin lattice conformation, lateral inter-dimer interactions, and (non-)local lateral coordination of dimer motions. The simulations suggest that most of the hydrolysis energy is stored in the lattice in the form of longitudinal strain. While not significantly affecting lateral bond stability, the stored elastic energy results in more strongly confined and correlated dynamics of GDP-tubulins, thereby entropically destabilizing the MT lattice.
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Affiliation(s)
- Maxim Igaev
- Max Planck Institute for Biophysical Chemistry, Am Fassberg 11, D-37077 Göttingen, Germany
| | - Helmut Grubmüller
- Max Planck Institute for Biophysical Chemistry, Am Fassberg 11, D-37077 Göttingen, Germany
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47
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Turoňová B, Sikora M, Schürmann C, Hagen WJH, Welsch S, Blanc FEC, von Bülow S, Gecht M, Bagola K, Hörner C, van Zandbergen G, Landry J, de Azevedo NTD, Mosalaganti S, Schwarz A, Covino R, Mühlebach MD, Hummer G, Krijnse Locker J, Beck M. In situ structural analysis of SARS-CoV-2 spike reveals flexibility mediated by three hinges. Science 2020; 370:203-208. [PMID: 32817270 PMCID: PMC7665311 DOI: 10.1126/science.abd5223] [Citation(s) in RCA: 434] [Impact Index Per Article: 108.5] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2020] [Accepted: 08/13/2020] [Indexed: 12/12/2022]
Abstract
The spike protein (S) of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is required for cell entry and is the primary focus for vaccine development. In this study, we combined cryo-electron tomography, subtomogram averaging, and molecular dynamics simulations to structurally analyze S in situ. Compared with the recombinant S, the viral S was more heavily glycosylated and occurred mostly in the closed prefusion conformation. We show that the stalk domain of S contains three hinges, giving the head unexpected orientational freedom. We propose that the hinges allow S to scan the host cell surface, shielded from antibodies by an extensive glycan coat. The structure of native S contributes to our understanding of SARS-CoV-2 infection and potentially to the development of safe vaccines.
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Affiliation(s)
- Beata Turoňová
- Structural and Computational Biology Unit, European Molecular Biology Laboratory (EMBL), Meyerhofstr. 1, 69117 Heidelberg, Germany.,Department of Molecular Sociology, Max Planck Institute of Biophysics, Max-von-Laue Str. 3, 60438 Frankfurt am Main, Germany
| | - Mateusz Sikora
- Department of Theoretical Biophysics, Max Planck Institute of Biophysics, Max-von-Laue Str. 3, 60438 Frankfurt am Main, Germany
| | - Christoph Schürmann
- Division of Veterinary Medicine, Paul Ehrlich Institute, Paul Ehrlich Strasse 51-59, 63225 Langen, Germany
| | - Wim J H Hagen
- Structural and Computational Biology Unit, European Molecular Biology Laboratory (EMBL), Meyerhofstr. 1, 69117 Heidelberg, Germany
| | - Sonja Welsch
- Central Electron Microscopy Facility, Max Planck Institute of Biophysics, Max-von-Laue Str. 3, 60438 Frankfurt am Main, Germany
| | - Florian E C Blanc
- Department of Theoretical Biophysics, Max Planck Institute of Biophysics, Max-von-Laue Str. 3, 60438 Frankfurt am Main, Germany
| | - Sören von Bülow
- Department of Theoretical Biophysics, Max Planck Institute of Biophysics, Max-von-Laue Str. 3, 60438 Frankfurt am Main, Germany
| | - Michael Gecht
- Department of Theoretical Biophysics, Max Planck Institute of Biophysics, Max-von-Laue Str. 3, 60438 Frankfurt am Main, Germany
| | - Katrin Bagola
- Division of Immunology, Paul Ehrlich Institute, Paul Ehrlich Strasse 51-59, 63225 Langen, Germany
| | - Cindy Hörner
- Division of Veterinary Medicine, Paul Ehrlich Institute, Paul Ehrlich Strasse 51-59, 63225 Langen, Germany.,German Center for Infection Research, Gießen-Marburg-Langen, Germany
| | - Ger van Zandbergen
- Division of Immunology, Paul Ehrlich Institute, Paul Ehrlich Strasse 51-59, 63225 Langen, Germany.,Institute for Immunology, University Medical Center, Johannes Gutenberg University Mainz, Mainz, Germany.,Research Center for Immunotherapy (FZI), University Medical Center, Johannes Gutenberg-University Mainz, Mainz, Germany
| | - Jonathan Landry
- Genomics Core Facility, EMBL, Meyerhofstr. 1, 69117 Heidelberg, Germany
| | | | - Shyamal Mosalaganti
- Structural and Computational Biology Unit, European Molecular Biology Laboratory (EMBL), Meyerhofstr. 1, 69117 Heidelberg, Germany.,Department of Molecular Sociology, Max Planck Institute of Biophysics, Max-von-Laue Str. 3, 60438 Frankfurt am Main, Germany
| | - Andre Schwarz
- Structural and Computational Biology Unit, European Molecular Biology Laboratory (EMBL), Meyerhofstr. 1, 69117 Heidelberg, Germany
| | - Roberto Covino
- Department of Theoretical Biophysics, Max Planck Institute of Biophysics, Max-von-Laue Str. 3, 60438 Frankfurt am Main, Germany.,Frankfurt Institute for Advanced Studies, Ruth-Moufang-Str. 1, 60438 Frankfurt am Main, Germany
| | - Michael D Mühlebach
- Division of Veterinary Medicine, Paul Ehrlich Institute, Paul Ehrlich Strasse 51-59, 63225 Langen, Germany.,German Center for Infection Research, Gießen-Marburg-Langen, Germany
| | - Gerhard Hummer
- Department of Theoretical Biophysics, Max Planck Institute of Biophysics, Max-von-Laue Str. 3, 60438 Frankfurt am Main, Germany. .,Institute of Biophysics, Goethe University Frankfurt, 60438 Frankfurt am Main, Germany
| | - Jacomine Krijnse Locker
- Electron Microscopy of Pathogens Unit, Paul Ehrlich Institute, Paul Ehrlich Strasse 51-59, 63225 Langen, Germany.
| | - Martin Beck
- Structural and Computational Biology Unit, European Molecular Biology Laboratory (EMBL), Meyerhofstr. 1, 69117 Heidelberg, Germany. .,Department of Molecular Sociology, Max Planck Institute of Biophysics, Max-von-Laue Str. 3, 60438 Frankfurt am Main, Germany
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48
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Vinothkumar KR, Arya CK, Ramanathan G, Subramanian R. Comparison of CryoEM and X-ray structures of dimethylformamidase. PROGRESS IN BIOPHYSICS AND MOLECULAR BIOLOGY 2020; 160:66-78. [PMID: 32735943 DOI: 10.1016/j.pbiomolbio.2020.06.008] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/01/2020] [Revised: 06/10/2020] [Accepted: 06/29/2020] [Indexed: 11/15/2022]
Abstract
Dimethylformamidase (DMFase) catalyzes the hydrolysis of dimethylformamide, an industrial solvent, introduced into the environment by humans. Recently, we determined the structures of dimethylformamidase by electron cryo microscopy and X-ray crystallography revealing a tetrameric enzyme with a mononuclear iron at the active site. DMFase from Paracoccus sp. isolated from a waste water treatment plant around the city of Kanpur in India shows maximal activity at 54 °C and is halotolerant. The structures determined by both techniques are mostly identical and the largest difference is in a loop near the active site. This loop could play a role in co-operativity between the monomers. A number of non-protein densities are observed in the EM map, which are modelled as water molecules. Comparison of the structures determined by the two methods reveals conserved water molecules that could play a structural role. The higher stability, unusual active site and negligible activity at low temperature makes this a very good model to study enzyme mechanism by cryoEM.
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Affiliation(s)
| | - Chetan Kumar Arya
- Institute for Stem Cell Science and Regenerative Medicine, GKVK Post, Bengaluru, India
| | | | - Ramaswamy Subramanian
- Institute for Stem Cell Science and Regenerative Medicine, GKVK Post, Bengaluru, India; Department of Biological Sciences and Weldon School of Biomedical Engineering, Purdue University, West Lafayette, IN, USA.
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49
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Thompson MC, Yeates TO, Rodriguez JA. Advances in methods for atomic resolution macromolecular structure determination. F1000Res 2020; 9:F1000 Faculty Rev-667. [PMID: 32676184 PMCID: PMC7333361 DOI: 10.12688/f1000research.25097.1] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 06/25/2020] [Indexed: 12/13/2022] Open
Abstract
Recent technical advances have dramatically increased the power and scope of structural biology. New developments in high-resolution cryo-electron microscopy, serial X-ray crystallography, and electron diffraction have been especially transformative. Here we highlight some of the latest advances and current challenges at the frontiers of atomic resolution methods for elucidating the structures and dynamical properties of macromolecules and their complexes.
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Affiliation(s)
- Michael C. Thompson
- Department of Chemistry and Chemical Biology, University of California, Merced, CA, USA
| | - Todd O. Yeates
- Department of Chemistry and Biochemistry, University of California Los Angeles, Los Angeles, CA, USA
- UCLA-DOE Institute for Genomics and Proteomics, Los Angeles, CA, USA
| | - Jose A. Rodriguez
- Department of Chemistry and Biochemistry, University of California Los Angeles, Los Angeles, CA, USA
- UCLA-DOE Institute for Genomics and Proteomics, Los Angeles, CA, USA
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50
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Sora V, Kumar M, Maiani E, Lambrughi M, Tiberti M, Papaleo E. Structure and Dynamics in the ATG8 Family From Experimental to Computational Techniques. Front Cell Dev Biol 2020; 8:420. [PMID: 32587856 PMCID: PMC7297954 DOI: 10.3389/fcell.2020.00420] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2020] [Accepted: 05/06/2020] [Indexed: 12/31/2022] Open
Abstract
Autophagy is a conserved and essential intracellular mechanism for the removal of damaged components. Since autophagy deregulation is linked to different kinds of pathologies, it is fundamental to gain knowledge on the fine molecular and structural details related to the core proteins of the autophagy machinery. Among these, the family of human ATG8 proteins plays a central role in recruiting other proteins to the different membrane structures involved in the autophagic pathway. Several experimental structures are available for the members of the ATG8 family alone or in complex with their different biological partners, including disordered regions of proteins containing a short linear motif called LC3 interacting motif. Recently, the first structural details of the interaction of ATG8 proteins with biological membranes came into light. The availability of structural data for human ATG8 proteins has been paving the way for studies on their structure-function-dynamic relationship using biomolecular simulations. Experimental and computational structural biology can help to address several outstanding questions on the mechanism of human ATG8 proteins, including their specificity toward different interactors, their association with membranes, the heterogeneity of their conformational ensemble, and their regulation by post-translational modifications. We here summarize the main results collected so far and discuss the future perspectives within the field and the knowledge gaps. Our review can serve as a roadmap for future structural and dynamics studies of the ATG8 family members in health and disease.
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Affiliation(s)
- Valentina Sora
- Computational Biology Laboratory, Center for Autophagy, Recycling and Disease, Danish Cancer Society Research Center, Copenhagen, Denmark
| | - Mukesh Kumar
- Computational Biology Laboratory, Center for Autophagy, Recycling and Disease, Danish Cancer Society Research Center, Copenhagen, Denmark
| | - Emiliano Maiani
- Computational Biology Laboratory, Center for Autophagy, Recycling and Disease, Danish Cancer Society Research Center, Copenhagen, Denmark
| | - Matteo Lambrughi
- Computational Biology Laboratory, Center for Autophagy, Recycling and Disease, Danish Cancer Society Research Center, Copenhagen, Denmark
| | - Matteo Tiberti
- Computational Biology Laboratory, Center for Autophagy, Recycling and Disease, Danish Cancer Society Research Center, Copenhagen, Denmark
| | - Elena Papaleo
- Computational Biology Laboratory, Center for Autophagy, Recycling and Disease, Danish Cancer Society Research Center, Copenhagen, Denmark
- Translational Disease System Biology, Faculty of Health and Medical Sciences, Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, Copenhagen, Denmark
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