1
|
Baskaran K, Ploskon E, Tejero R, Yokochi M, Harrus D, Liang Y, Peisach E, Persikova I, Ramelot TA, Sekharan M, Tolchard J, Westbrook JD, Bardiaux B, Schwieters CD, Patwardhan A, Velankar S, Burley SK, Kurisu G, Hoch JC, Montelione GT, Vuister GW, Young JY. Restraint validation of biomolecular structures determined by NMR in the Protein Data Bank. Structure 2024:S0969-2126(24)00050-9. [PMID: 38490206 DOI: 10.1016/j.str.2024.02.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2023] [Revised: 01/13/2024] [Accepted: 02/19/2024] [Indexed: 03/17/2024]
Abstract
Biomolecular structure analysis from experimental NMR studies generally relies on restraints derived from a combination of experimental and knowledge-based data. A challenge for the structural biology community has been a lack of standards for representing these restraints, preventing the establishment of uniform methods of model-vs-data structure validation against restraints and limiting interoperability between restraint-based structure modeling programs. The NEF and NMR-STAR formats provide a standardized approach for representing commonly used NMR restraints. Using these restraint formats, a standardized validation system for assessing structural models of biopolymers against restraints has been developed and implemented in the wwPDB OneDep data deposition-validation-biocuration system. The resulting wwPDB restraint violation report provides a model vs. data assessment of biomolecule structures determined using distance and dihedral restraints, with extensions to other restraint types currently being implemented. These tools are useful for assessing NMR models, as well as for assessing biomolecular structure predictions based on distance restraints.
Collapse
Affiliation(s)
- Kumaran Baskaran
- Biological Magnetic Resonance Data Bank, Department of Molecular Biology and Biophysics, UConn Health, Farmington, CT 06030-3305, USA.
| | - Eliza Ploskon
- Department of Molecular and Cell Biology, Leicester Institute of Structural and Chemical Biology, University of Leicester, Leicester LE1 7RH, UK
| | - Roberto Tejero
- Departamento de Quίmica Fίsica, Universidad de Valencia, Dr. Moliner, 50 46100 Burjassot, Valencia, Spain
| | - Masashi Yokochi
- Protein Data Bank Japan, Institute for Protein Research, Osaka University, Suita, Osaka 565-0871, Japan; Protein Data Bank Japan, Protein Research Foundation, Minoh, Osaka 562-8686, Japan
| | - Deborah Harrus
- Protein Data Bank in Europe, EMBL-EBI, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Yuhe Liang
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Ezra Peisach
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Irina Persikova
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Theresa A Ramelot
- Department of Chemistry and Chemical Biology, Center for Biotechnology and Interdisciplinary Sciences, Rensselaer Polytechnic Institute, Troy, NY 12180, USA
| | - Monica Sekharan
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - James Tolchard
- Protein Data Bank in Europe, EMBL-EBI, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - John D Westbrook
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Benjamin Bardiaux
- Department of Structural Biology and Chemistry, Institut Pasteur, Université Paris Cité, CNRS UMR3528, 75015 Paris, France
| | - Charles D Schwieters
- Computational Biomolecular Magnetic Resonance Core, National Institute of Diabetes and Digestive and Kidney Diseases, Bethesda, MD 20892, USA
| | - Ardan Patwardhan
- The Electron Microscopy Data Bank, EMBL-EBI, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Sameer Velankar
- Protein Data Bank in Europe, EMBL-EBI, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Stephen K Burley
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA; Research Collaboratory for Structural Bioinformatics Protein Data Bank, San Diego Supercomputer Center, University of California, La Jolla, La Jolla, CA, USA; Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA; Cancer Institute of New Jersey, Rutgers, The State University of New Jersey, New Brunswick, NJ 08901, USA; Department of Chemistry and Chemical Biology, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Genji Kurisu
- Protein Data Bank Japan, Institute for Protein Research, Osaka University, Suita, Osaka 565-0871, Japan; Protein Data Bank Japan, Protein Research Foundation, Minoh, Osaka 562-8686, Japan
| | - Jeffrey C Hoch
- Biological Magnetic Resonance Data Bank, Department of Molecular Biology and Biophysics, UConn Health, Farmington, CT 06030-3305, USA
| | - Gaetano T Montelione
- Department of Chemistry and Chemical Biology, Center for Biotechnology and Interdisciplinary Sciences, Rensselaer Polytechnic Institute, Troy, NY 12180, USA; Cancer Institute of New Jersey, Rutgers, The State University of New Jersey, New Brunswick, NJ 08901, USA.
| | - Geerten W Vuister
- Department of Molecular and Cell Biology, Leicester Institute of Structural and Chemical Biology, University of Leicester, Leicester LE1 7RH, UK.
| | - Jasmine Y Young
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA.
| |
Collapse
|
2
|
Gutsche I, Montelione GT. Editorial overview: Biophysical methods: Multiple structures of proteins underpin their biological functions. Curr Opin Struct Biol 2024; 84:102762. [PMID: 38217897 DOI: 10.1016/j.sbi.2023.102762] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2024]
Affiliation(s)
- Irina Gutsche
- Institut de Biologie Structurale, Univ Grenoble Alpes, CEA, CNRS, IBS, 71 Avenue des Martyrs, F-38044, Grenoble, France; Department of Chemistry, Umeå University, SE-901 87, Umeå, Sweden.
| | - Gaetano T Montelione
- Department of Chemistry and Chemical Biology, Center for Biotechnology and Interdisciplinary Sciences, Rensselaer Polytechnic Institute, Troy, NY, 12180, USA.
| |
Collapse
|
3
|
Woltz R, Schweibenz B, Tsutakawa SE, Zhao C, Ma L, Shurina B, Hura GL, John R, Vorobiev S, Swapna GVT, Solotchi M, Tainer JA, Krug RM, Patel SS, Montelione GT. The NS1 protein of influenza B virus binds 5'-triphosphorylated dsRNA to suppress RIG-I activation and the host antiviral response. bioRxiv 2024:2023.09.25.559316. [PMID: 38328244 PMCID: PMC10849492 DOI: 10.1101/2023.09.25.559316] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/09/2024]
Abstract
Influenza A and B viruses overcome the host antiviral response to cause a contagious and often severe human respiratory disease. Here, integrative structural biology and biochemistry studies on non-structural protein 1 of influenza B virus (NS1B) reveal a previously unrecognized viral mechanism for innate immune evasion. Conserved basic groups of its C-terminal domain (NS1B-CTD) bind 5'triphosphorylated double-stranded RNA (5'-ppp-dsRNA), the primary pathogen-associated feature that activates the host retinoic acid-inducible gene I protein (RIG-I) to initiate interferon synthesis and the cellular antiviral response. Like RIG-I, NS1B-CTD preferentially binds blunt-end 5'ppp-dsRNA. NS1B-CTD also competes with RIG-I for binding 5'ppp-dsRNA, and thus suppresses activation of RIG-I's ATPase activity. Although the NS1B N-terminal domain also binds dsRNA, it utilizes a different binding mode and lacks 5'ppp-dsRNA end preferences. In cells infected with wild-type influenza B virus, RIG-I activation is inhibited. In contrast, RIG-I activation and the resulting phosphorylation of transcription factor IRF-3 are not inhibited in cells infected with a mutant virus encoding NS1B with a R208A substitution it its CTD that eliminates its 5'ppp-dsRNA binding activity. These results reveal a novel mechanism in which NS1B binds 5'ppp-dsRNA to inhibit the RIG-I antiviral response during influenza B virus infection, and open the door to new avenues for antiviral drug discovery.
Collapse
Affiliation(s)
- Ryan Woltz
- Department of Molecular Biology and Biochemistry, Rutgers, The State University of New Jersey, Piscataway, NJ, 08854, USA
- Department of Biochemistry and Molecular Biology, Robert Wood Johnson Medical School, Rutgers, The State University of New Jersey, Piscataway, NJ, 08854, USA
| | - Brandon Schweibenz
- Department of Biochemistry and Molecular Biology, Robert Wood Johnson Medical School, Rutgers, The State University of New Jersey, Piscataway, NJ, 08854, USA
| | - Susan E. Tsutakawa
- Life Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - Chen Zhao
- Department of Molecular Biosciences, Center for Infectious Disease, Institute for Cellular and Molecular Biology, University of Texas at Austin, Austin, TX 78712 USA
| | - LiChung Ma
- Department of Molecular Biology and Biochemistry, Rutgers, The State University of New Jersey, Piscataway, NJ, 08854, USA
- Department of Biochemistry and Molecular Biology, Robert Wood Johnson Medical School, Rutgers, The State University of New Jersey, Piscataway, NJ, 08854, USA
| | - Ben Shurina
- Department of Chemistry and Chemical Biology, and Center for Biotechnology and Interdisciplinary Sciences, Rensselaer Polytechnic Institute, Troy, NY, 12180, USA
| | - Gregory L. Hura
- Life Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - Rachael John
- Department of Molecular Biology and Biochemistry, Rutgers, The State University of New Jersey, Piscataway, NJ, 08854, USA
- Department of Biochemistry and Molecular Biology, Robert Wood Johnson Medical School, Rutgers, The State University of New Jersey, Piscataway, NJ, 08854, USA
| | - Sergey Vorobiev
- Department of Molecular Biology and Biochemistry, Rutgers, The State University of New Jersey, Piscataway, NJ, 08854, USA
- Department of Biochemistry and Molecular Biology, Robert Wood Johnson Medical School, Rutgers, The State University of New Jersey, Piscataway, NJ, 08854, USA
| | - GVT Swapna
- Department of Molecular Biology and Biochemistry, Rutgers, The State University of New Jersey, Piscataway, NJ, 08854, USA
- Department of Biochemistry and Molecular Biology, Robert Wood Johnson Medical School, Rutgers, The State University of New Jersey, Piscataway, NJ, 08854, USA
- Department of Chemistry and Chemical Biology, and Center for Biotechnology and Interdisciplinary Sciences, Rensselaer Polytechnic Institute, Troy, NY, 12180, USA
| | - Mihai Solotchi
- Department of Biochemistry and Molecular Biology, Robert Wood Johnson Medical School, Rutgers, The State University of New Jersey, Piscataway, NJ, 08854, USA
| | - John A. Tainer
- Life Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
- Department of Molecular and Cellular Oncology, Division of Basic Science Research, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA
| | - Robert M. Krug
- Department of Molecular Biosciences, Center for Infectious Disease, Institute for Cellular and Molecular Biology, University of Texas at Austin, Austin, TX 78712 USA
| | - Smita S. Patel
- Department of Biochemistry and Molecular Biology, Robert Wood Johnson Medical School, Rutgers, The State University of New Jersey, Piscataway, NJ, 08854, USA
| | - Gaetano T. Montelione
- Department of Biochemistry and Molecular Biology, Robert Wood Johnson Medical School, Rutgers, The State University of New Jersey, Piscataway, NJ, 08854, USA
- Department of Chemistry and Chemical Biology, and Center for Biotechnology and Interdisciplinary Sciences, Rensselaer Polytechnic Institute, Troy, NY, 12180, USA
| |
Collapse
|
4
|
Mondal A, Singh B, Felkner RH, De Falco A, Swapna GVT, Montelione GT, Roth MJ, Perez A. Sifting Through the Noise: A Computational Pipeline for Accurate Prioritization of Protein-Protein Binding Candidates in High-Throughput Protein Libraries. bioRxiv 2024:2024.01.20.576374. [PMID: 38328039 PMCID: PMC10849530 DOI: 10.1101/2024.01.20.576374] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/09/2024]
Abstract
Identifying the interactome for a protein of interest is challenging due to the large number of possible binders. High-throughput experimental approaches narrow down possible binding partners, but often include false positives. Furthermore, they provide no information about what the binding region is (e.g. the binding epitope). We introduce a novel computational pipeline based on an AlphaFold2 (AF) Competition Assay (AF-CBA) to identify proteins that bind a target of interest from a pull-down experiment, along with the binding epitope. Our focus is on proteins that bind the Extraterminal (ET) domain of Bromo and Extraterminal domain (BET) proteins, but we also introduce nine additional systems to show transferability to other peptide-protein systems. We describe a series of limitations to the methodology based on intrinsic deficiencies to AF and AF-CBA, to help users identify scenarios where the approach will be most useful. Given the speed and accuracy of the methodology, we expect it to be generally applicable to facilitate target selection for experimental verification starting from high-throughput protein libraries.
Collapse
Affiliation(s)
- Arup Mondal
- Department of Chemistry and Quantum Theory Project, University of Florida, Leigh Hall 240, Gainesville, FL
| | - Bhumika Singh
- Department of Chemistry and Quantum Theory Project, University of Florida, Leigh Hall 240, Gainesville, FL
| | - Roland H. Felkner
- Department of Pharmacology, Rutgers-Robert Wood Johnson Medical School, 675 Hoes Lane Rm 636, Piscataway, NJ 08854
| | - Anna De Falco
- Department of Chemistry and Chemical Biology, Center for Biotechnology and Interdisciplinary Sciences, Rensselaer Polytechnic Institute, Troy, New York 12180, United States
| | - GVT Swapna
- Department of Chemistry and Chemical Biology, Center for Biotechnology and Interdisciplinary Sciences, Rensselaer Polytechnic Institute, Troy, New York 12180, United States
| | - Gaetano T. Montelione
- Department of Chemistry and Chemical Biology, Center for Biotechnology and Interdisciplinary Sciences, Rensselaer Polytechnic Institute, Troy, New York 12180, United States
| | - Monica J. Roth
- Department of Pharmacology, Rutgers-Robert Wood Johnson Medical School, 675 Hoes Lane Rm 636, Piscataway, NJ 08854
| | - Alberto Perez
- Department of Chemistry and Quantum Theory Project, University of Florida, Leigh Hall 240, Gainesville, FL
| |
Collapse
|
5
|
Baskaran K, Ploskon E, Tejero R, Yokochi M, Harrus D, Liang Y, Peisach E, Persikova I, Ramelot TA, Sekharan M, Tolchard J, Westbrook JD, Bardiaux B, Schwieters CD, Patwardhan A, Velankar S, Burley SK, Kurisu G, Hoch JC, Montelione GT, Vuister GW, Young JY. Restraint Validation of Biomolecular Structures Determined by NMR in the Protein Data Bank. bioRxiv 2024:2024.01.15.575520. [PMID: 38328042 PMCID: PMC10849500 DOI: 10.1101/2024.01.15.575520] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/09/2024]
Abstract
Biomolecular structure analysis from experimental NMR studies generally relies on restraints derived from a combination of experimental and knowledge-based data. A challenge for the structural biology community has been a lack of standards for representing these restraints, preventing the establishment of uniform methods of model-vs-data structure validation against restraints and limiting interoperability between restraint-based structure modeling programs. The NMR exchange (NEF) and NMR-STAR formats provide a standardized approach for representing commonly used NMR restraints. Using these restraint formats, a standardized validation system for assessing structural models of biopolymers against restraints has been developed and implemented in the wwPDB OneDep data deposition-validation-biocuration system. The resulting wwPDB Restraint Violation Report provides a model vs. data assessment of biomolecule structures determined using distance and dihedral restraints, with extensions to other restraint types currently being implemented. These tools are useful for assessing NMR models, as well as for assessing biomolecular structure predictions based on distance restraints.
Collapse
Affiliation(s)
- Kumaran Baskaran
- Biological Magnetic Resonance Data Bank, Department of Molecular Biology and Biophysics, UConn Health, Farmington, CT 06030-3305, USA
| | - Eliza Ploskon
- Department of Molecular and Cell Biology, Leicester Institute of Structural and Chemical Biology, University of Leicester, Leicester LE1 7RH, United Kingdom
| | - Roberto Tejero
- Departamento de Quίmica Fίsica, Universidad de Valencia, Dr. Moliner, 50 46100-Burjassot, Valencia, Spain
| | - Masashi Yokochi
- Protein Data Bank Japan, Institute for Protein Research, Osaka University, Suita, Osaka 565-0871, Japan
- Protein Data Bank Japan, Protein Research Foundation, Minoh, Osaka 562-8686, Japan
| | - Deborah Harrus
- Protein Data Bank in Europe, EMBL-EBI, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, United Kingdom
| | - Yuhe Liang
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Ezra Peisach
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Irina Persikova
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Theresa A Ramelot
- Dept of Chemistry and Chemical Biology, Center for Biotechnology and Interdisciplinary Sciences, Rensselaer Polytechnic Institute, Troy, New York, 12180 USA
| | - Monica Sekharan
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - James Tolchard
- Protein Data Bank in Europe, EMBL-EBI, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, United Kingdom
| | - John D Westbrook
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Benjamin Bardiaux
- Department of Structural Biology and Chemistry, Institut Pasteur, Université Paris Cité, CNRS UMR3528, 75015 Paris, France
| | - Charles D Schwieters
- Computational Biomolecular Magnetic Resonance Core, National Institute of Diabetes and Digestive and Kidney Diseases, Bethesda, MD 20892, USA
| | - Ardan Patwardhan
- The Electron Microscopy Data Bank, EMBL-EBI, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, United Kingdom
| | - Sameer Velankar
- Protein Data Bank in Europe, EMBL-EBI, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, United Kingdom
| | - Stephen K Burley
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, San Diego Supercomputer Center, University of California, La Jolla, California, USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Cancer Institute of New Jersey, Rutgers, The State University of New Jersey, New Brunswick, NJ 08901, USA
- Department of Chemistry and Chemical Biology, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Genji Kurisu
- Protein Data Bank Japan, Institute for Protein Research, Osaka University, Suita, Osaka 565-0871, Japan
- Protein Data Bank Japan, Protein Research Foundation, Minoh, Osaka 562-8686, Japan
| | - Jeffrey C Hoch
- Biological Magnetic Resonance Data Bank, Department of Molecular Biology and Biophysics, UConn Health, Farmington, CT 06030-3305, USA
| | - Gaetano T Montelione
- Dept of Chemistry and Chemical Biology, Center for Biotechnology and Interdisciplinary Sciences, Rensselaer Polytechnic Institute, Troy, New York, 12180 USA
- Cancer Institute of New Jersey, Rutgers, The State University of New Jersey, New Brunswick, NJ 08901, USA
| | - Geerten W Vuister
- Department of Molecular and Cell Biology, Leicester Institute of Structural and Chemical Biology, University of Leicester, Leicester LE1 7RH, United Kingdom
| | - Jasmine Y Young
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| |
Collapse
|
6
|
Klukowski P, Damberger FF, Allain FHT, Iwai H, Kadavath H, Ramelot TA, Montelione GT, Riek R, Güntert P. The 100-protein NMR spectra dataset: A resource for biomolecular NMR data analysis. Sci Data 2024; 11:30. [PMID: 38177162 PMCID: PMC10767026 DOI: 10.1038/s41597-023-02879-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Accepted: 12/22/2023] [Indexed: 01/06/2024] Open
Abstract
Multidimensional NMR spectra are the basis for studying proteins by NMR spectroscopy and crucial for the development and evaluation of methods for biomolecular NMR data analysis. Nevertheless, in contrast to derived data such as chemical shift assignments in the BMRB and protein structures in the PDB databases, this primary data is in general not publicly archived. To change this unsatisfactory situation, we present a standardized set of solution NMR data comprising 1329 2-4-dimensional NMR spectra and associated reference (chemical shift assignments, structures) and derived (peak lists, restraints for structure calculation, etc.) annotations. With the 100-protein NMR spectra dataset that was originally compiled for the development of the ARTINA deep learning-based spectra analysis method, 100 protein structures can be reproduced from their original experimental data. The 100-protein NMR spectra dataset is expected to help the development of computational methods for NMR spectroscopy, in particular machine learning approaches, and enable consistent and objective comparisons of these methods.
Collapse
Affiliation(s)
- Piotr Klukowski
- Institute of Molecular Physical Science, ETH Zurich, 8093, Zurich, Switzerland.
| | - Fred F Damberger
- Institute of Biochemistry, ETH Zurich, 8093, Zurich, Switzerland
| | | | - Hideo Iwai
- Institute of Biotechnology, University of Helsinki, 00100, Helsinki, Finland
| | | | - Theresa A Ramelot
- Department of Chemistry and Chemical Biology, and Center for Biotechnology and Interdisciplinary Sciences, Rensselaer Polytechnic Institute, Troy, NY, 12180, USA
| | - Gaetano T Montelione
- Department of Chemistry and Chemical Biology, and Center for Biotechnology and Interdisciplinary Sciences, Rensselaer Polytechnic Institute, Troy, NY, 12180, USA
| | - Roland Riek
- Institute of Molecular Physical Science, ETH Zurich, 8093, Zurich, Switzerland.
| | - Peter Güntert
- Institute of Molecular Physical Science, ETH Zurich, 8093, Zurich, Switzerland.
- Institute of Biophysical Chemistry, Goethe University, 60438, Frankfurt am Main, Germany.
- Department of Chemistry, Tokyo Metropolitan University, Hachioji, 192-0397, Tokyo, Japan.
| |
Collapse
|
7
|
Ramelot TA, Tejero R, Montelione GT. Representing structures of the multiple conformational states of proteins. Curr Opin Struct Biol 2023; 83:102703. [PMID: 37776602 PMCID: PMC10841472 DOI: 10.1016/j.sbi.2023.102703] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2023] [Revised: 08/18/2023] [Accepted: 08/23/2023] [Indexed: 10/02/2023]
Abstract
Biomolecules exhibit dynamic behavior that single-state models of their structures cannot fully capture. We review some recent advances for investigating multiple conformations of biomolecules, including experimental methods, molecular dynamics simulations, and machine learning. We also address the challenges associated with representing single- and multiple-state models in data archives, with a particular focus on NMR structures. Establishing standardized representations and annotations will facilitate effective communication and understanding of these complex models to the broader scientific community.
Collapse
Affiliation(s)
- Theresa A Ramelot
- Dept of Chemistry and Chemical Biology, Center for Biotechnology and Interdisciplinary Sciences, Rensselaer Polytechnic Institute, Troy, NY, 12180, USA.
| | - Roberto Tejero
- Dept of Chemistry and Chemical Biology, Center for Biotechnology and Interdisciplinary Sciences, Rensselaer Polytechnic Institute, Troy, NY, 12180, USA
| | - Gaetano T Montelione
- Dept of Chemistry and Chemical Biology, Center for Biotechnology and Interdisciplinary Sciences, Rensselaer Polytechnic Institute, Troy, NY, 12180, USA.
| |
Collapse
|
8
|
Kryshtafovych A, Montelione GT, Rigden DJ, Mesdaghi S, Karaca E, Moult J. Breaking the conformational ensemble barrier: Ensemble structure modeling challenges in CASP15. Proteins 2023; 91:1903-1911. [PMID: 37872703 PMCID: PMC10840738 DOI: 10.1002/prot.26584] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2023] [Accepted: 08/14/2023] [Indexed: 10/25/2023]
Abstract
For the first time, the 2022 CASP (Critical Assessment of Structure Prediction) community experiment included a section on computing multiple conformations for protein and RNA structures. There was full or partial success in reproducing the ensembles for four of the nine targets, an encouraging result. For protein structures, enhanced sampling with variations of the AlphaFold2 deep learning method was by far the most effective approach. One substantial conformational change caused by a single mutation across a complex interface was accurately reproduced. In two other assembly modeling cases, methods succeeded in sampling conformations near to the experimental ones even though environmental factors were not included in the calculations. An experimentally derived flexibility ensemble allowed a single accurate RNA structure model to be identified. Difficulties included how to handle sparse or low-resolution experimental data and the current lack of effective methods for modeling RNA/protein complexes. However, these and other obstacles appear addressable.
Collapse
Affiliation(s)
| | - Gaetano T Montelione
- Department of Chemistry and Chemical Biology, Center for Biotechnology and Interdisciplinary Sciences, Rensselaer Polytechnic Institute, Troy, New York, USA
| | - Daniel J Rigden
- Institute of Systems, Molecular, and Integrative Biology, University of Liverpool, Liverpool, UK
| | - Shahram Mesdaghi
- Institute of Systems, Molecular, and Integrative Biology, University of Liverpool, Liverpool, UK
- Computational Biology Facility, MerseyBio, University of Liverpool, Liverpool, UK
| | - Ezgi Karaca
- Izmir Biomedicine and Genome Center, Izmir, Turkey
- Izmir International Biomedicine and Genome Institute, Dokuz Eylul University, Izmir, Turkey
| | - John Moult
- Institute for Bioscience and Biotechnology Research, Rockville, Maryland, USA
- Department of Cell Biology and Molecular Genetics, University of Maryland, College Park, Maryland, USA
| |
Collapse
|
9
|
Li EH, Spaman LE, Tejero R, Janet Huang Y, Ramelot TA, Fraga KJ, Prestegard JH, Kennedy MA, Montelione GT. Blind assessment of monomeric AlphaFold2 protein structure models with experimental NMR data. J Magn Reson 2023; 352:107481. [PMID: 37257257 PMCID: PMC10659763 DOI: 10.1016/j.jmr.2023.107481] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/22/2023] [Revised: 05/08/2023] [Accepted: 05/15/2023] [Indexed: 06/02/2023]
Abstract
Recent advances in molecular modeling of protein structures are changing the field of structural biology. AlphaFold-2 (AF2), an AI system developed by DeepMind, Inc., utilizes attention-based deep learning to predict models of protein structures with high accuracy relative to structures determined by X-ray crystallography and cryo-electron microscopy (cryoEM). Comparing AF2 models to structures determined using solution NMR data, both high similarities and distinct differences have been observed. Since AF2 was trained on X-ray crystal and cryoEM structures, we assessed how accurately AF2 can model small, monomeric, solution protein NMR structures which (i) were not used in the AF2 training data set, and (ii) did not have homologous structures in the Protein Data Bank at the time of AF2 training. We identified nine open-source protein NMR data sets for such "blind" targets, including chemical shift, raw NMR FID data, NOESY peak lists, and (for 1 case) 15N-1H residual dipolar coupling data. For these nine small (70-108 residues) monomeric proteins, we generated AF2 prediction models and assessed how well these models fit to these experimental NMR data, using several well-established NMR structure validation tools. In most of these cases, the AF2 models fit the NMR data nearly as well, or sometimes better than, the corresponding NMR structure models previously deposited in the Protein Data Bank. These results provide benchmark NMR data for assessing new NMR data analysis and protein structure prediction methods. They also document the potential for using AF2 as a guiding tool in protein NMR data analysis, and more generally for hypothesis generation in structural biology research.
Collapse
Affiliation(s)
- Ethan H Li
- Department of Chemistry and Chemical Biology, Center for Biotechnology and Interdisciplinary Sciences, Rensselaer Polytechnic Institute, Troy, NY 12180, USA
| | - Laura E Spaman
- Department of Chemistry and Chemical Biology, Center for Biotechnology and Interdisciplinary Sciences, Rensselaer Polytechnic Institute, Troy, NY 12180, USA.
| | - Roberto Tejero
- Department of Chemistry and Chemical Biology, Center for Biotechnology and Interdisciplinary Sciences, Rensselaer Polytechnic Institute, Troy, NY 12180, USA.
| | - Yuanpeng Janet Huang
- Department of Chemistry and Chemical Biology, Center for Biotechnology and Interdisciplinary Sciences, Rensselaer Polytechnic Institute, Troy, NY 12180, USA.
| | - Theresa A Ramelot
- Department of Chemistry and Chemical Biology, Center for Biotechnology and Interdisciplinary Sciences, Rensselaer Polytechnic Institute, Troy, NY 12180, USA.
| | - Keith J Fraga
- Department of Chemistry and Chemical Biology, Center for Biotechnology and Interdisciplinary Sciences, Rensselaer Polytechnic Institute, Troy, NY 12180, USA.
| | - James H Prestegard
- Complex Carbohydrate Research Center, University of Georgia, Athens, GA 30602, USA.
| | - Michael A Kennedy
- Department of Chemistry and Biochemistry, Miami University, Oxford, OH 45056, USA.
| | - Gaetano T Montelione
- Department of Chemistry and Chemical Biology, Center for Biotechnology and Interdisciplinary Sciences, Rensselaer Polytechnic Institute, Troy, NY 12180, USA.
| |
Collapse
|
10
|
Ramelot TA, Palmer J, Montelione GT, Bhardwaj G. Cell-permeable chameleonic peptides: Exploiting conformational dynamics in de novo cyclic peptide design. Curr Opin Struct Biol 2023; 80:102603. [PMID: 37178478 PMCID: PMC10923192 DOI: 10.1016/j.sbi.2023.102603] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2023] [Accepted: 04/05/2023] [Indexed: 05/15/2023]
Abstract
Membrane-traversing peptides offer opportunities for targeting intracellular proteins and oral delivery. Despite progress in understanding the mechanisms underlying membrane traversal in natural cell-permeable peptides, there are still several challenges to designing membrane-traversing peptides with diverse shapes and sizes. Conformational flexibility appears to be a key determinant of membrane permeability of large macrocycles. We review recent developments in the design and validation of chameleonic cyclic peptides, which can switch between alternative conformations to enable improved permeability through cell membranes, while still maintaining reasonable solubility and exposed polar functional groups for target protein binding. Finally, we discuss the principles, strategies, and practical considerations for rational design, discovery, and validation of permeable chameleonic peptides.
Collapse
Affiliation(s)
- Theresa A Ramelot
- Department of Chemistry and Chemical Biology and Center for Biotechnology and Interdisciplinary Sciences, Rensselaer Polytechnic Institute, Troy, NY, 12180, USA
| | - Jonathan Palmer
- Institute for Protein Design, University of Washington, Seattle, WA, 98195, USA; Department of Medicinal Chemistry, University of Washington, Seattle, WA, 98195, USA
| | - Gaetano T Montelione
- Department of Chemistry and Chemical Biology and Center for Biotechnology and Interdisciplinary Sciences, Rensselaer Polytechnic Institute, Troy, NY, 12180, USA.
| | - Gaurav Bhardwaj
- Institute for Protein Design, University of Washington, Seattle, WA, 98195, USA; Department of Medicinal Chemistry, University of Washington, Seattle, WA, 98195, USA.
| |
Collapse
|
11
|
Mazzei L, Greene-Cramer R, Bafna K, Jovanovic A, De Falco A, Acton TB, Royer CA, Ciurli S, Montelione GT. Protocol for production and purification of SARS-CoV-2 3CL pro. STAR Protoc 2023; 4:102326. [PMID: 37235475 DOI: 10.1016/j.xpro.2023.102326] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Revised: 03/04/2023] [Accepted: 05/01/2023] [Indexed: 05/28/2023] Open
Abstract
3CLpro protease from SARS-CoV-2 is a primary target for COVID-19 antiviral drug development. Here, we present a protocol for 3CLpro production in Escherichia coli. We describe steps to purify 3CLpro, expressed as a fusion with the Saccharomyces cerevisiae SUMO protein, with yields up to 120 mg L-1 following cleavage. The protocol also provides isotope-enriched samples suitable for nuclear magnetic resonance (NMR) studies. We also present methods to characterize 3CLpro by mass spectrometry, X-ray crystallography, heteronuclear NMR, and a Förster-resonance-energy-transfer-based enzyme assay. For complete details on the use and execution of this protocol, please refer to Bafna et al.1.
Collapse
Affiliation(s)
- Luca Mazzei
- Department of Pharmacy and Biotechnology, University of Bologna, 40127 Bologna, Italy
| | - Rebecca Greene-Cramer
- Center for Biotechnology and Interdisciplinary Sciences, Rensselaer Polytechnic Institute, Troy, NY 12180, USA; Department of Chemistry and Chemical Biology, Rensselaer Polytechnic Institute, Troy, NY 12180, USA
| | - Khushboo Bafna
- Center for Biotechnology and Interdisciplinary Sciences, Rensselaer Polytechnic Institute, Troy, NY 12180, USA; Department of Chemistry and Chemical Biology, Rensselaer Polytechnic Institute, Troy, NY 12180, USA
| | - Aleksandar Jovanovic
- Department of Pharmacy and Biotechnology, University of Bologna, 40127 Bologna, Italy
| | - Anna De Falco
- Center for Biotechnology and Interdisciplinary Sciences, Rensselaer Polytechnic Institute, Troy, NY 12180, USA; Department of Chemistry and Chemical Biology, Rensselaer Polytechnic Institute, Troy, NY 12180, USA
| | - Thomas B Acton
- Center for Biotechnology and Interdisciplinary Sciences, Rensselaer Polytechnic Institute, Troy, NY 12180, USA; Department of Chemistry and Chemical Biology, Rensselaer Polytechnic Institute, Troy, NY 12180, USA
| | - Catherine Ann Royer
- Center for Biotechnology and Interdisciplinary Sciences, Rensselaer Polytechnic Institute, Troy, NY 12180, USA; Department of Biology, Rensselaer Polytechnic Institute, Troy, NY 12180, USA
| | - Stefano Ciurli
- Department of Pharmacy and Biotechnology, University of Bologna, 40127 Bologna, Italy.
| | - Gaetano T Montelione
- Center for Biotechnology and Interdisciplinary Sciences, Rensselaer Polytechnic Institute, Troy, NY 12180, USA; Department of Chemistry and Chemical Biology, Rensselaer Polytechnic Institute, Troy, NY 12180, USA.
| |
Collapse
|
12
|
Maisuradze GG, Montelione GT, Rackovsky S, Skolnick J. Protein Folding and Dynamics─An Overview on the Occasion of Harold Scheraga's 100th Birthday. J Phys Chem B 2023; 127:2879-2880. [PMID: 37021400 DOI: 10.1021/acs.jpcb.3c01417] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/07/2023]
Affiliation(s)
- Gia G Maisuradze
- Cornell University Baker Laboratory, Ithaca, New York 14853, United States
| | - Gaetano T Montelione
- Rensselaer Polytechnic Institute, 110 Eighth Street, Troy, New York 12180, United States
| | - Shalom Rackovsky
- Cornell University Baker Laboratory, Ithaca, New York 14853, United States
- University of Rochester School of Medicine and Dentistry, 601 Elmwood Avenue, Rochester, New York 14642, United States
| | - Jeffrey Skolnick
- Georgia Institute of Technology, 270 Washington Street, S.W., Atlanta, Georgia 30334, United States
| |
Collapse
|
13
|
Mondal A, Swapna GVT, Lopez MM, Klang L, Hao J, Ma L, Roth MJ, Montelione GT, Perez A. Structure Determination of Challenging Protein-Peptide Complexes Combining NMR Chemical Shift Data and Molecular Dynamics Simulations. J Chem Inf Model 2023; 63:2058-2072. [PMID: 36988562 PMCID: PMC10150588 DOI: 10.1021/acs.jcim.2c01595] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/30/2023]
Abstract
Intrinsically disordered regions of proteins often mediate important protein-protein interactions. However, the folding-upon-binding nature of many polypeptide-protein interactions limits the ability of modeling tools to predict the three-dimensional structures of such complexes. To address this problem, we have taken a tandem approach combining NMR chemical shift data and molecular simulations to determine the structures of peptide-protein complexes. Here, we use the MELD (Modeling Employing Limited Data) technique applied to polypeptide complexes formed with the extraterminal domain (ET) of bromo and extraterminal domain (BET) proteins, which exhibit a high degree of binding plasticity. This system is particularly challenging as the binding process includes allosteric changes across the ET receptor upon binding, and the polypeptide binding partners can adopt different conformations (e.g., helices and hairpins) in the complex. In a blind study, the new approach successfully modeled bound-state conformations and binding poses, using only protein receptor backbone chemical shift data, in excellent agreement with experimentally determined structures for moderately tight (Kd ∼100 nM) binders. The hybrid MELD + NMR approach required additional peptide ligand chemical shift data for weaker (Kd ∼250 μM) peptide binding partners. AlphaFold also successfully predicts the structures of some of these peptide-protein complexes. However, whereas AlphaFold can provide qualitative peptide rankings, MELD can directly estimate relative binding affinities. The hybrid MELD + NMR approach offers a powerful new tool for structural analysis of protein-polypeptide complexes involving disorder-to-order transitions upon complex formation, which are not successfully modeled with most other complex prediction methods, providing both the 3D structures of peptide-protein complexes and their relative binding affinities.
Collapse
Affiliation(s)
- Arup Mondal
- The Quantum Theory Project, Department of Chemistry, University of Florida, Gainesville, Florida 32611, United States
| | - G V T Swapna
- Department of Pharmacology, Robert Wood Johnson Medical School, Rutgers, The State University of New Jersey, Piscataway, New Jersey 08854, United States
- Department of Chemistry and Chemical Biology, Center for Biotechnology and Interdisciplinary Sciences, Rensselaer Polytechnic Institute, Troy, New York 12180, United States
| | - Maria M Lopez
- Department of Chemistry and Chemical Biology, Center for Biotechnology and Interdisciplinary Sciences, Rensselaer Polytechnic Institute, Troy, New York 12180, United States
| | - Laura Klang
- Department of Chemistry and Chemical Biology, Center for Biotechnology and Interdisciplinary Sciences, Rensselaer Polytechnic Institute, Troy, New York 12180, United States
| | - Jingzhou Hao
- Department of Chemistry and Chemical Biology, Center for Biotechnology and Interdisciplinary Sciences, Rensselaer Polytechnic Institute, Troy, New York 12180, United States
| | - LiChung Ma
- Department of Pharmacology, Robert Wood Johnson Medical School, Rutgers, The State University of New Jersey, Piscataway, New Jersey 08854, United States
| | - Monica J Roth
- Department of Pharmacology, Robert Wood Johnson Medical School, Rutgers, The State University of New Jersey, Piscataway, New Jersey 08854, United States
| | - Gaetano T Montelione
- Department of Chemistry and Chemical Biology, Center for Biotechnology and Interdisciplinary Sciences, Rensselaer Polytechnic Institute, Troy, New York 12180, United States
| | - Alberto Perez
- The Quantum Theory Project, Department of Chemistry, University of Florida, Gainesville, Florida 32611, United States
| |
Collapse
|
14
|
Mondal A, Perez A, Montelione GT. Low information NMR data guided protein-peptide complex structure determination with MELDXMD. Biophys J 2023; 122:471a. [PMID: 36784424 DOI: 10.1016/j.bpj.2022.11.2527] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/12/2023] Open
Affiliation(s)
- Arup Mondal
- Chemistry, University of Florida, Gainesville, FL, USA
| | - Alberto Perez
- Chemistry, University of Florida, Gainesville, FL, USA
| | | |
Collapse
|
15
|
Li EH, Spaman L, Tejero R, Huang YJ, Ramelot TA, Fraga KJ, Prestegard JH, Kennedy MA, Montelione GT. Blind Assessment of Monomeric AlphaFold2 Protein Structure Models with Experimental NMR Data. bioRxiv 2023:2023.01.22.525096. [PMID: 36712039 PMCID: PMC9882346 DOI: 10.1101/2023.01.22.525096] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/24/2023]
Abstract
Recent advances in molecular modeling of protein structures are changing the field of structural biology. AlphaFold-2 (AF2), an AI system developed by DeepMind, Inc., utilizes attention-based deep learning to predict models of protein structures with high accuracy relative to structures determined by X-ray crystallography and cryo-electron microscopy (cryoEM). Comparing AF2 models to structures determined using solution NMR data, both high similarities and distinct differences have been observed. Since AF2 was trained on X-ray crystal and cryoEM structures, we assessed how accurately AF2 can model small, monomeric, solution protein NMR structures which (i) were not used in the AF2 training data set, and (ii) did not have homologous structures in the Protein Data Bank at the time of AF2 training. We identified nine open source protein NMR data sets for such "blind" targets, including chemical shift, raw NMR FID data, NOESY peak lists, and (for 1 case) 15 N- 1 H residual dipolar coupling data. For these nine small (70 - 108 residues) monomeric proteins, we generated AF2 prediction models and assessed how well these models fit to these experimental NMR data, using several well-established NMR structure validation tools. In most of these cases, the AF2 models fit the NMR data nearly as well, or sometimes better than, the corresponding NMR structure models previously deposited in the Protein Data Bank. These results provide benchmark NMR data for assessing new NMR data analysis and protein structure prediction methods. They also document the potential for using AF2 as a guiding tool in protein NMR data analysis, and more generally for hypothesis generation in structural biology research. Highlights AF2 models assessed against NMR data for 9 monomeric proteins not used in training.AF2 models fit NMR data almost as well as the experimentally-determined structures. RPF-DP, PSVS , and PDBStat software provide structure quality and RDC assessment. RPF-DP analysis using AF2 models suggests multiple conformational states.
Collapse
Affiliation(s)
- Ethan H. Li
- Department of Chemistry and Chemical Biology, Center for Biotechnology and Interdisciplinary Sciences, Rensselaer Polytechnic Institute, Troy, NY 12180 USA
| | - Laura Spaman
- Department of Chemistry and Chemical Biology, Center for Biotechnology and Interdisciplinary Sciences, Rensselaer Polytechnic Institute, Troy, NY 12180 USA
| | - Roberto Tejero
- Department of Chemistry and Chemical Biology, Center for Biotechnology and Interdisciplinary Sciences, Rensselaer Polytechnic Institute, Troy, NY 12180 USA
| | - Yuanpeng Janet Huang
- Department of Chemistry and Chemical Biology, Center for Biotechnology and Interdisciplinary Sciences, Rensselaer Polytechnic Institute, Troy, NY 12180 USA
| | - Theresa A. Ramelot
- Department of Chemistry and Chemical Biology, Center for Biotechnology and Interdisciplinary Sciences, Rensselaer Polytechnic Institute, Troy, NY 12180 USA
| | - Keith J. Fraga
- Department of Chemistry and Chemical Biology, Center for Biotechnology and Interdisciplinary Sciences, Rensselaer Polytechnic Institute, Troy, NY 12180 USA
| | - James H. Prestegard
- Complex Carbohydrate Research Center, University of Georgia, Athens, GA 30602 USA
| | - Michael A. Kennedy
- Department of Chemistry and Biochemistry, Miami University, Oxford, OH 45056 USA
| | - Gaetano T. Montelione
- Department of Chemistry and Chemical Biology, Center for Biotechnology and Interdisciplinary Sciences, Rensselaer Polytechnic Institute, Troy, NY 12180 USA
| |
Collapse
|
16
|
Bafna K, Cioffi CL, Krug RM, Montelione GT. Structural similarities between SARS-CoV2 3CL pro and other viral proteases suggest potential lead molecules for developing broad spectrum antivirals. Front Chem 2022; 10:948553. [PMID: 36353143 PMCID: PMC9638714 DOI: 10.3389/fchem.2022.948553] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2022] [Accepted: 08/08/2022] [Indexed: 09/01/2023] Open
Abstract
Considering the significant impact of the recent COVID-19 outbreak, development of broad-spectrum antivirals is a high priority goal to prevent future global pandemics. Antiviral development processes generally emphasize targeting a specific protein from a particular virus. However, some antiviral agents developed for specific viral protein targets may exhibit broad spectrum antiviral activity, or at least provide useful lead molecules for broad spectrum drug development. There is significant potential for repurposing a wide range of existing viral protease inhibitors to inhibit the SARS-CoV2 3C-like protease (3CLpro). If effective even as relatively weak inhibitors of 3CLpro, these molecules can provide a diverse and novel set of scaffolds for new drug discovery campaigns. In this study, we compared the sequence- and structure-based similarity of SARS-CoV2 3CLpro with proteases from other viruses, and identified 22 proteases with similar active-site structures. This structural similarity, characterized by secondary-structure topology diagrams, is evolutionarily divergent within taxonomically related viruses, but appears to result from evolutionary convergence of protease enzymes between virus families. Inhibitors of these proteases that are structurally similar to the SARS-CoV2 3CLpro protease were identified and assessed as potential inhibitors of SARS-CoV2 3CLpro protease by virtual docking. Several of these molecules have docking scores that are significantly better than known SARS-CoV2 3CLpro inhibitors, suggesting that these molecules are also potential inhibitors of the SARS-CoV2 3CLpro protease. Some have been previously reported to inhibit SARS-CoV2 3CLpro. The results also suggest that established inhibitors of SARS-CoV2 3CLpro may be considered as potential inhibitors of other viral 3C-like proteases.
Collapse
Affiliation(s)
- Khushboo Bafna
- Department of Chemistry and Chemical Biology, Rensselaer Polytechnic Institute, Troy, NY, United States
- Center for Biotechnology and Interdisciplinary Sciences, Rensselaer Polytechnic Institute, Troy, NY, United States
| | - Christopher L. Cioffi
- Department of Chemistry and Chemical Biology, Rensselaer Polytechnic Institute, Troy, NY, United States
| | - Robert M. Krug
- Department of Molecular Biosciences, John Ring LaMontagne Center for Infectious Disease, Institute for Cellular and Molecular Biology, University of Texas at Austin, Austin, TX, United States
| | - Gaetano T. Montelione
- Department of Chemistry and Chemical Biology, Rensselaer Polytechnic Institute, Troy, NY, United States
- Center for Biotechnology and Interdisciplinary Sciences, Rensselaer Polytechnic Institute, Troy, NY, United States
| |
Collapse
|
17
|
Hu K, Lee W, Montelione GT, Sgourakis NG, Vögeli B. Editorial: Computational approaches for interpreting experimental data and understanding protein structure, dynamics and function relationships. Front Mol Biosci 2022; 9:1018149. [PMID: 36262477 PMCID: PMC9576191 DOI: 10.3389/fmolb.2022.1018149] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2022] [Accepted: 09/15/2022] [Indexed: 11/16/2022] Open
Affiliation(s)
- Kaifeng Hu
- Innovative Institute of Chinese Medicine and Pharmacy, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan, China
| | - Woonghee Lee
- Department of Chemistry, University of Colorado Denver, Denver, CO, United States
- *Correspondence: Woonghee Lee,
| | - Gaetano T. Montelione
- Department of Chemistry and Chemical Biology, Center for Biotechnology and Interdisciplinary Sciences, Rensselaer Polytechnic Institute, Troy, NY, United States
| | - Nikolaos G. Sgourakis
- Department of Pathology and Laboratory Medicine, The Children’s Hospital of Philadelphia, and Department of Biochemistry and Biophysics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Beat Vögeli
- Department of Biochemistry and Molecular Genetics, University of Colorado Anschutz Medical Campus, Aurora, CO, United States
| |
Collapse
|
18
|
Bhardwaj G, O'Connor J, Rettie S, Huang YH, Ramelot TA, Mulligan VK, Alpkilic GG, Palmer J, Bera AK, Bick MJ, Di Piazza M, Li X, Hosseinzadeh P, Craven TW, Tejero R, Lauko A, Choi R, Glynn C, Dong L, Griffin R, van Voorhis WC, Rodriguez J, Stewart L, Montelione GT, Craik D, Baker D. Accurate de novo design of membrane-traversing macrocycles. Cell 2022; 185:3520-3532.e26. [PMID: 36041435 PMCID: PMC9490236 DOI: 10.1016/j.cell.2022.07.019] [Citation(s) in RCA: 24] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2021] [Revised: 05/01/2022] [Accepted: 07/21/2022] [Indexed: 01/26/2023]
Abstract
We use computational design coupled with experimental characterization to systematically investigate the design principles for macrocycle membrane permeability and oral bioavailability. We designed 184 6-12 residue macrocycles with a wide range of predicted structures containing noncanonical backbone modifications and experimentally determined structures of 35; 29 are very close to the computational models. With such control, we show that membrane permeability can be systematically achieved by ensuring all amide (NH) groups are engaged in internal hydrogen bonding interactions. 84 designs over the 6-12 residue size range cross membranes with an apparent permeability greater than 1 × 10-6 cm/s. Designs with exposed NH groups can be made membrane permeable through the design of an alternative isoenergetic fully hydrogen-bonded state favored in the lipid membrane. The ability to robustly design membrane-permeable and orally bioavailable peptides with high structural accuracy should contribute to the next generation of designed macrocycle therapeutics.
Collapse
Affiliation(s)
- Gaurav Bhardwaj
- Institute for Protein Design, University of Washington, Seattle, WA 98195, USA; Department of Medicinal Chemistry, University of Washington, Seattle, WA 98195, USA; Biological Physics, Structure and Design program, University of Washington, Seattle, WA 98195, USA.
| | - Jacob O'Connor
- Institute for Protein Design, University of Washington, Seattle, WA 98195, USA; Biological Physics, Structure and Design program, University of Washington, Seattle, WA 98195, USA; Department of Biochemistry, University of Washington, Seattle, WA 98195, USA
| | - Stephen Rettie
- Institute for Protein Design, University of Washington, Seattle, WA 98195, USA; Molecular Cell and Biology program, University of Washington, Seattle, WA 98195, USA
| | - Yen-Hua Huang
- Institute for Molecular Bioscience, Australian Research Council Centre of Excellence for Innovations in Peptide and Protein Science, The University of Queensland, Brisbane, QLD 4072, Australia
| | - Theresa A Ramelot
- Department of Chemistry and Chemical Biology and Center for Biotechnology and Interdisciplinary Sciences, Rensselaer Polytechnic Institute, Troy, NY 12180, USA
| | | | - Gizem Gokce Alpkilic
- Institute for Protein Design, University of Washington, Seattle, WA 98195, USA; Department of Medicinal Chemistry, University of Washington, Seattle, WA 98195, USA; Molecular Engineering and Sciences Program, University of Washington, Seattle, WA 98195, USA
| | - Jonathan Palmer
- Institute for Protein Design, University of Washington, Seattle, WA 98195, USA; Department of Medicinal Chemistry, University of Washington, Seattle, WA 98195, USA
| | - Asim K Bera
- Institute for Protein Design, University of Washington, Seattle, WA 98195, USA
| | - Matthew J Bick
- Institute for Protein Design, University of Washington, Seattle, WA 98195, USA
| | - Maddalena Di Piazza
- Institute for Protein Design, University of Washington, Seattle, WA 98195, USA; Department of Medicinal Chemistry, University of Washington, Seattle, WA 98195, USA
| | - Xinting Li
- Institute for Protein Design, University of Washington, Seattle, WA 98195, USA
| | - Parisa Hosseinzadeh
- Institute for Protein Design, University of Washington, Seattle, WA 98195, USA
| | - Timothy W Craven
- Institute for Protein Design, University of Washington, Seattle, WA 98195, USA; Department of Biochemistry, University of Washington, Seattle, WA 98195, USA
| | - Roberto Tejero
- Departamento de Quίmica Fίsica, Universidad de Valencia, Avenida Dr. Moliner 50, Burjassot, 46100 Valencia, Spain
| | - Anna Lauko
- Institute for Protein Design, University of Washington, Seattle, WA 98195, USA; Biological Physics, Structure and Design program, University of Washington, Seattle, WA 98195, USA; Department of Biochemistry, University of Washington, Seattle, WA 98195, USA
| | - Ryan Choi
- Department of Medicine, Division of Allergy and Infectious Disease, University of Washington, Seattle, WA, USA
| | - Calina Glynn
- Department of Chemistry and Biochemistry, University of California-Los Angeles, Los Angeles, CA, USA
| | - Linlin Dong
- Takeda Pharmaceuticals Inc., Cambridge, MA, USA
| | | | - Wesley C van Voorhis
- Department of Medicine, Division of Allergy and Infectious Disease, University of Washington, Seattle, WA, USA
| | - Jose Rodriguez
- Department of Chemistry and Biochemistry, University of California-Los Angeles, Los Angeles, CA, USA
| | - Lance Stewart
- Institute for Protein Design, University of Washington, Seattle, WA 98195, USA
| | - Gaetano T Montelione
- Department of Chemistry and Chemical Biology and Center for Biotechnology and Interdisciplinary Sciences, Rensselaer Polytechnic Institute, Troy, NY 12180, USA
| | - David Craik
- Institute for Molecular Bioscience, Australian Research Council Centre of Excellence for Innovations in Peptide and Protein Science, The University of Queensland, Brisbane, QLD 4072, Australia
| | - David Baker
- Institute for Protein Design, University of Washington, Seattle, WA 98195, USA; Department of Biochemistry, University of Washington, Seattle, WA 98195, USA; Howard Hughes Medical Institute, University of Washington, Seattle, WA 98195, USA.
| |
Collapse
|
19
|
Fraga KJ, Huang YJ, Ramelot TA, Swapna GVT, Lashawn Anak Kendary A, Li E, Korf I, Montelione GT. SpecDB: A relational database for archiving biomolecular NMR spectral data. J Magn Reson 2022; 342:107268. [PMID: 35930941 PMCID: PMC9922030 DOI: 10.1016/j.jmr.2022.107268] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/25/2022] [Revised: 06/16/2022] [Accepted: 07/06/2022] [Indexed: 05/11/2023]
Abstract
NMR is a valuable experimental tool in the structural biologist's toolkit to elucidate the structures, functions, and motions of biomolecules. The progress of machine learning, particularly in structural biology, reveals the critical importance of large, diverse, and reliable datasets in developing new methods and understanding in structural biology and science more broadly. Biomolecular NMR research groups produce large amounts of data, and there is renewed interest in organizing these data to train new, sophisticated machine learning architectures and to improve biomolecular NMR analysis pipelines. The foundational data type in NMR is the free-induction decay (FID). There are opportunities to build sophisticated machine learning methods to tackle long-standing problems in NMR data processing, resonance assignment, dynamics analysis, and structure determination using NMR FIDs. Our goal in this study is to provide a lightweight, broadly available tool for archiving FID data as it is generated at the spectrometer, and grow a new resource of FID data and associated metadata. This study presents a relational schema for storing and organizing the metadata items that describe an NMR sample and FID data, which we call Spectral Database (SpecDB). SpecDB is implemented in SQLite and includes a Python software library providing a command-line application to create, organize, query, backup, share, and maintain the database. This set of software tools and database schema allow users to store, organize, share, and learn from NMR time domain data. SpecDB is freely available under an open source license at https://github.rpi.edu/RPIBioinformatics/SpecDB.
Collapse
Affiliation(s)
- Keith J Fraga
- Department of Molecular and Cellular Biology, University of California, Davis, CA 95616, USA.
| | - Yuanpeng J Huang
- Department of Chemistry and Chemical Biology, Center for Biotechnology and Interdisciplinary Sciences, Rensselaer Polytechnic Institute, Troy, NY 12180 USA.
| | - Theresa A Ramelot
- Department of Chemistry and Chemical Biology, Center for Biotechnology and Interdisciplinary Sciences, Rensselaer Polytechnic Institute, Troy, NY 12180 USA.
| | - G V T Swapna
- Department of Chemistry and Chemical Biology, Center for Biotechnology and Interdisciplinary Sciences, Rensselaer Polytechnic Institute, Troy, NY 12180 USA; Department of Pharmacology, Robert Wood Johnson Medical School, Rutgers The State University of New Jersey, Piscataway, NJ 08854, USA.
| | | | - Ethan Li
- Department of Chemistry and Chemical Biology, Center for Biotechnology and Interdisciplinary Sciences, Rensselaer Polytechnic Institute, Troy, NY 12180 USA.
| | - Ian Korf
- Department of Molecular and Cellular Biology, University of California, Davis, CA 95616, USA.
| | - Gaetano T Montelione
- Department of Chemistry and Chemical Biology, Center for Biotechnology and Interdisciplinary Sciences, Rensselaer Polytechnic Institute, Troy, NY 12180 USA.
| |
Collapse
|
20
|
Li Y, Zhang R, Wang C, Forouhar F, Clarke OB, Vorobiev S, Singh S, Montelione GT, Szyperski T, Xu Y, Hunt JF. Oligomeric interactions maintain active-site structure in a noncooperative enzyme family. EMBO J 2022; 41:e108368. [PMID: 35801308 DOI: 10.15252/embj.2021108368] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2021] [Revised: 04/07/2022] [Accepted: 04/16/2022] [Indexed: 11/09/2022] Open
Abstract
The evolutionary benefit accounting for widespread conservation of oligomeric structures in proteins lacking evidence of intersubunit cooperativity remains unclear. Here, crystal and cryo-EM structures, and enzymological data, demonstrate that a conserved tetramer interface maintains the active-site structure in one such class of proteins, the short-chain dehydrogenase/reductase (SDR) superfamily. Phylogenetic comparisons support a significantly longer polypeptide being required to maintain an equivalent active-site structure in the context of a single subunit. Oligomerization therefore enhances evolutionary fitness by reducing the metabolic cost of enzyme biosynthesis. The large surface area of the structure-stabilizing oligomeric interface yields a synergistic gain in fitness by increasing tolerance to activity-enhancing yet destabilizing mutations. We demonstrate that two paralogous SDR superfamily enzymes with different specificities can form mixed heterotetramers that combine their individual enzymological properties. This suggests that oligomerization can also diversify the functions generated by a given metabolic investment, enhancing the fitness advantage provided by this architectural strategy.
Collapse
Affiliation(s)
- Yaohui Li
- Key Laboratory of Industrial Biotechnology of Ministry of Education and School of Biotechnology, Jiangnan University, Wuxi, China.,Department of Biological Sciences, 702 Sherman Fairchild Center, MC2434, Columbia University, New York, NY, USA
| | - Rongzhen Zhang
- Key Laboratory of Industrial Biotechnology of Ministry of Education and School of Biotechnology, Jiangnan University, Wuxi, China
| | - Chi Wang
- Department of Biological Sciences, 702 Sherman Fairchild Center, MC2434, Columbia University, New York, NY, USA.,Cryo-Electron Microscopy Center, Columbia University Irving Medical Center, New York, NY, USA
| | - Farhad Forouhar
- Department of Biological Sciences, 702 Sherman Fairchild Center, MC2434, Columbia University, New York, NY, USA.,Macromolecular Crystallography Shared Resource, Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, New York, NY, USA
| | - Oliver B Clarke
- Department of Physiology and Cellular Biophysics and Department of Anesthesiology, Columbia University Irving Medical Center, New York, NY, USA
| | - Sergey Vorobiev
- Department of Biological Sciences, 702 Sherman Fairchild Center, MC2434, Columbia University, New York, NY, USA
| | - Shikha Singh
- Department of Biological Sciences, 702 Sherman Fairchild Center, MC2434, Columbia University, New York, NY, USA
| | - Gaetano T Montelione
- Department of Chemistry & Chemical Biology and Center for Biotechnology & Interdisciplinary Sciences, Rensselaer Polytechnic Institute, Troy, NY, USA
| | - Thomas Szyperski
- Department of Chemistry, State University of New York at Buffalo, Buffalo, NY, USA
| | - Yan Xu
- Key Laboratory of Industrial Biotechnology of Ministry of Education and School of Biotechnology, Jiangnan University, Wuxi, China
| | - John F Hunt
- Department of Biological Sciences, 702 Sherman Fairchild Center, MC2434, Columbia University, New York, NY, USA
| |
Collapse
|
21
|
Tejero R, Huang YJ, Ramelot TA, Montelione GT. AlphaFold Models of Small Proteins Rival the Accuracy of Solution NMR Structures. Front Mol Biosci 2022; 9:877000. [PMID: 35769913 PMCID: PMC9234698 DOI: 10.3389/fmolb.2022.877000] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2022] [Accepted: 04/25/2022] [Indexed: 11/13/2022] Open
Abstract
Recent advances in molecular modeling using deep learning have the potential to revolutionize the field of structural biology. In particular, AlphaFold has been observed to provide models of protein structures with accuracies rivaling medium-resolution X-ray crystal structures, and with excellent atomic coordinate matches to experimental protein NMR and cryo-electron microscopy structures. Here we assess the hypothesis that AlphaFold models of small, relatively rigid proteins have accuracies (based on comparison against experimental data) similar to experimental solution NMR structures. We selected six representative small proteins with structures determined by both NMR and X-ray crystallography, and modeled each of them using AlphaFold. Using several structure validation tools integrated under the Protein Structure Validation Software suite (PSVS), we then assessed how well these models fit to experimental NMR data, including NOESY peak lists (RPF-DP scores), comparisons between predicted rigidity and chemical shift data (ANSURR scores), and 15N-1H residual dipolar coupling data (RDC Q factors) analyzed by software tools integrated in the PSVS suite. Remarkably, the fits to NMR data for the protein structure models predicted with AlphaFold are generally similar, or better, than for the corresponding experimental NMR or X-ray crystal structures. Similar conclusions were reached in comparing AlphaFold2 predictions and NMR structures for three targets from the Critical Assessment of Protein Structure Prediction (CASP). These results contradict the widely held misperception that AlphaFold cannot accurately model solution NMR structures. They also document the value of PSVS for model vs. data assessment of protein NMR structures, and the potential for using AlphaFold models for guiding analysis of experimental NMR data and more generally in structural biology.
Collapse
Affiliation(s)
- Roberto Tejero
- Departamento de Química Física, Universidad de Valencia, Valencia, Spain
- *Correspondence: Roberto Tejero, ; Gaetano T. Montelione,
| | - Yuanpeng Janet Huang
- Department of Chemistry and Chemical Biology, Center for Biotechnology and Interdisciplinary Sciences, Rensselaer Polytechnic Institute, Troy, NY, United States
| | - Theresa A. Ramelot
- Department of Chemistry and Chemical Biology, Center for Biotechnology and Interdisciplinary Sciences, Rensselaer Polytechnic Institute, Troy, NY, United States
| | - Gaetano T. Montelione
- Department of Chemistry and Chemical Biology, Center for Biotechnology and Interdisciplinary Sciences, Rensselaer Polytechnic Institute, Troy, NY, United States
- *Correspondence: Roberto Tejero, ; Gaetano T. Montelione,
| |
Collapse
|
22
|
Colman DR, Labesse G, Swapna G, Stefanakis J, Montelione GT, Boyd ES, Royer CA. Structural evolution of the ancient enzyme, dissimilatory sulfite reductase. Proteins 2022; 90:1331-1345. [PMID: 35122336 PMCID: PMC9018543 DOI: 10.1002/prot.26315] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2022] [Accepted: 01/29/2022] [Indexed: 07/21/2023]
Abstract
Dissimilatory sulfite reductase is an ancient enzyme that has linked the global sulfur and carbon biogeochemical cycles since at least 3.47 Gya. While much has been learned about the phylogenetic distribution and diversity of DsrAB across environmental gradients, far less is known about the structural changes that occurred to maintain DsrAB function as the enzyme accompanied diversification of sulfate/sulfite reducing organisms (SRO) into new environments. Analyses of available crystal structures of DsrAB from Archaeoglobus fulgidus and Desulfovibrio vulgaris, representing early and late evolving lineages, respectively, show that certain features of DsrAB are structurally conserved, including active siro-heme binding motifs. Whether such structural features are conserved among DsrAB recovered from varied environments, including hot spring environments that host representatives of the earliest evolving SRO lineage (e.g., MV2-Eury), is not known. To begin to overcome these gaps in our understanding of the evolution of DsrAB, structural models from MV2.Eury were generated and evolutionary sequence co-variance analyses were conducted on a curated DsrAB database. Phylogenetically diverse DsrAB harbor many conserved functional residues including those that ligate active siro-heme(s). However, evolutionary co-variance analysis of monomeric DsrAB subunits revealed several False Positive Evolutionary Couplings (FPEC) that correspond to residues that have co-evolved despite being too spatially distant in the monomeric structure to allow for direct contact. One set of FPECs corresponds to residues that form a structural path between the two active siro-heme moieties across the interface between heterodimers, suggesting the potential for allostery or electron transfer within the enzyme complex. Other FPECs correspond to structural loops and gaps that may have been selected to stabilize enzyme function in different environments. These structural bioinformatics results suggest that DsrAB has maintained allosteric communication pathways between subunits as SRO diversified into new environments. The observations outlined here provide a framework for future biochemical and structural analyses of DsrAB to examine potential allosteric control of this enzyme.
Collapse
Affiliation(s)
- Daniel R. Colman
- Department of Microbiology and Cell Biology, Montana State University, Bozeman, Montana 59717
| | - Gilles Labesse
- Centre de Biochimie Structurale, CNRS UMR 5048, Montpellier, France 34090
| | - G.V.T. Swapna
- Dept of Biochemistry and Molecular Biology, Robert Wood Johnson Medical School, Rutgers The State University of New Jersey, Piscataway, NJ, 08854 USA
| | | | - Gaetano T. Montelione
- Department of Chemistry and Chemical Biology, and Center for Biotechnology and Interdisciplinary Studies, Rensselaer Polytechnic Institute, Troy, NY 12180
| | - Eric S. Boyd
- Department of Microbiology and Cell Biology, Montana State University, Bozeman, Montana 59717
| | - Catherine A. Royer
- Department of Biological Sciences, Rensselaer Polytechnic Institute, Troy, NY 12180
| |
Collapse
|
23
|
Wang Y, Liu B, Lu H, Liu J, Romanienko PJ, Montelione GT, Shen Z. SETD4-mediated KU70 methylation suppresses apoptosis. Cell Rep 2022; 39:110794. [PMID: 35545041 PMCID: PMC9201767 DOI: 10.1016/j.celrep.2022.110794] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2021] [Revised: 12/28/2021] [Accepted: 04/15/2022] [Indexed: 12/12/2022] Open
Abstract
The mammalian KU70 is a pleiotropic protein functioning in DNA repair and cytoplasmic suppression of apoptosis. We report a regulatory mechanism by which KU70’s cytoplasmic function is enabled due to a methylation at K570 of KU70 by SET-domain-containing protein 4 (SETD4). While SETD4 silencing reduces the level of methylated KU70, over-expression of SETD4 enhances methylation of KU70. Mutations of Y272 and Y284 of SETD4 abrogate methylation of KU70. Although SETD4 is predominantly a nuclear protein, the methylated KU70 is enriched in the cytoplasm. SETD4 knockdown enhances staurosporine (STS)-induced apoptosis and cell killing. Over-expression of the wild-type (WT) SETD4, but not the SETD4-Y272/Y284F mutant, suppresses STS-induced apoptosis. The KU70-K570R (mouse Ku70-K568R) mutation dampens the anti-apoptosis activity of KU70. Our study identifies KU70 as a non-histone substrate of SETD4, discovers a post-translational modification of KU70, and uncovers a role for SETD4 and KU70-K570 methylation in the suppression of apoptosis. Wang et al. identify the methylation of mammalian KU70 by SETD4. This post-translational modification is critical for KU70 localization to the cytoplasm and subsequent suppression of apoptosis.
Collapse
Affiliation(s)
- Yuan Wang
- Department of Radiation Oncology, Rutgers Cancer Institute of New Jersey, Rutgers Robert Wood Johnson Medical School, 195 Little Albany Street, New Brunswick, NJ 08901, USA
| | - Bochao Liu
- Department of Radiation Oncology, Rutgers Cancer Institute of New Jersey, Rutgers Robert Wood Johnson Medical School, 195 Little Albany Street, New Brunswick, NJ 08901, USA
| | - Huimei Lu
- Department of Radiation Oncology, Rutgers Cancer Institute of New Jersey, Rutgers Robert Wood Johnson Medical School, 195 Little Albany Street, New Brunswick, NJ 08901, USA
| | - Jingmei Liu
- Department of Radiation Oncology, Rutgers Cancer Institute of New Jersey, Rutgers Robert Wood Johnson Medical School, 195 Little Albany Street, New Brunswick, NJ 08901, USA
| | - Peter J Romanienko
- Genome Editing Shared Resource, Rutgers Cancer Institute of New Jersey, Rutgers Robert Wood Johnson Medical School, 195 Little Albany Street, New Brunswick, NJ 08901, USA
| | - Gaetano T Montelione
- Department of Chemistry and Chemical Biology, and Center for Biotechnology and Interdisciplinary Sciences, Rensselaer Polytechnic Institute, 110 8th St, Troy, NY 12180, USA
| | - Zhiyuan Shen
- Department of Radiation Oncology, Rutgers Cancer Institute of New Jersey, Rutgers Robert Wood Johnson Medical School, 195 Little Albany Street, New Brunswick, NJ 08901, USA.
| |
Collapse
|
24
|
Janet Huang Y, Zhang N, Bersch B, Fidelis K, Inouye M, Ishida Y, Kryshtafovych A, Kobayashi N, Kuroda Y, Liu G, LiWang A, Swapna G, Wu N, Yamazaki T, Montelione GT. Assessment of prediction methods for protein structures determined by NMR in CASP14: impact of AlphaFold2. Biophys J 2022. [DOI: 10.1016/j.bpj.2021.11.2475] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022] Open
|
25
|
Greene-Cramer R, Bafna K, White K, Harish B, Rosales R, Ramelot T, Acton TB, Del Olmo EM, Kehrer T, Miorin L, Royer CA, Garcia-Sastre A, Krug R, Montelione GT. Structure function characterization of SARS CoV2 proteases for COVID19 antiviral development. Biophys J 2022. [PMCID: PMC8833058 DOI: 10.1016/j.bpj.2021.11.2568] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
|
26
|
Huang YJ, Zhang N, Bersch B, Fidelis K, Inouye M, Ishida Y, Kryshtafovych A, Kobayashi N, Kuroda Y, Liu G, LiWang A, Swapna GVT, Wu N, Yamazaki T, Montelione GT. Assessment of prediction methods for protein structures determined by NMR in CASP14: Impact of AlphaFold2. Proteins 2021; 89:1959-1976. [PMID: 34559429 PMCID: PMC8616817 DOI: 10.1002/prot.26246] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2021] [Revised: 09/09/2021] [Accepted: 09/14/2021] [Indexed: 12/26/2022]
Abstract
NMR studies can provide unique information about protein conformations in solution. In CASP14, three reference structures provided by solution NMR methods were available (T1027, T1029, and T1055), as well as a fourth data set of NMR‐derived contacts for an integral membrane protein (T1088). For the three targets with NMR‐based structures, the best prediction results ranged from very good (GDT_TS = 0.90, for T1055) to poor (GDT_TS = 0.47, for T1029). We explored the basis of these results by comparing all CASP14 prediction models against experimental NMR data. For T1027, NMR data reveal extensive internal dynamics, presenting a unique challenge for protein structure prediction methods. The analysis of T1029 motivated exploration of a novel method of “inverse structure determination,” in which an AlphaFold2 model was used to guide NMR data analysis. NMR data provided to CASP predictor groups for target T1088, a 238‐residue integral membrane porin, was also used to assess several NMR‐assisted prediction methods. Most groups involved in this exercise generated similar beta‐barrel models, with good agreement with the experimental data. However, as was also observed in CASP13, some pure prediction groups that did not use any NMR data generated models for T1088 that better fit the NMR data than the models generated using these experimental data. These results demonstrate the remarkable power of modern methods to predict structures of proteins with accuracies rivaling solution NMR structures, and that it is now possible to reliably use prediction models to guide and complement experimental NMR data analysis.
Collapse
Affiliation(s)
- Yuanpeng Janet Huang
- Department of Chemistry and Chemical Biology, Center for Biotechnology and Interdisciplinary Sciences, Rensselaer Polytechnic Institute, Troy, New York, USA
| | - Ning Zhang
- Department of Chemistry and Biochemistry, University of California, Merced, California, USA
| | - Beate Bersch
- Biomolecular NMR Spectroscopy Group, Institut de Biologie Structurale, UMD-5075, CNRS-CEA-UJF, Grenoble, France
| | | | - Masayori Inouye
- Department of Biochemistry, Robert Wood Johnson Medical School, Rutgers University, Piscataway, New Jersey, USA.,Center for Advanced Biotechnology and Medicine, Rutgers University, Piscataway, New Jersey, USA
| | - Yojiro Ishida
- Department of Biochemistry, Robert Wood Johnson Medical School, Rutgers University, Piscataway, New Jersey, USA.,Center for Advanced Biotechnology and Medicine, Rutgers University, Piscataway, New Jersey, USA
| | | | - Naohiro Kobayashi
- NMR Science and Development Division, RSC, RIKEN, Yokohama, Kanagawa, Japan
| | - Yutaka Kuroda
- Department of Biotechnology and Life Science, Graduate School of Engineering, Tokyo University of Agriculture and Technology (TUAT), Tokyo, Japan
| | - Gaohua Liu
- Nexomics Biosciences, Inc., Rocky Hill, New Jersey, USA
| | - Andy LiWang
- Department of Chemistry and Biochemistry, University of California, Merced, California, USA.,Center for Cellular and Biomolecular Machines and Health Sciences Research Institute, University of California, Merced, California, USA
| | - G V T Swapna
- Department of Pharmacology, Robert Wood Johnson Medical School, Rutgers University, Piscataway, New Jersey, USA
| | - Nan Wu
- College of Food and Bioengineering, Zhengzhou University of Light Industry, Zhengzhou, China
| | - Toshio Yamazaki
- NMR Science and Development Division, RSC, RIKEN, Yokohama, Kanagawa, Japan
| | - Gaetano T Montelione
- Department of Chemistry and Chemical Biology, Center for Biotechnology and Interdisciplinary Sciences, Rensselaer Polytechnic Institute, Troy, New York, USA
| |
Collapse
|
27
|
Koga N, Koga R, Liu G, Castellanos J, Montelione GT, Baker D. Role of backbone strain in de novo design of complex α/β protein structures. Nat Commun 2021; 12:3921. [PMID: 34168113 PMCID: PMC8225619 DOI: 10.1038/s41467-021-24050-7] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2020] [Accepted: 05/28/2021] [Indexed: 12/24/2022] Open
Abstract
We previously elucidated principles for designing ideal proteins with completely consistent local and non-local interactions which have enabled the design of a wide range of new αβ-proteins with four or fewer β-strands. The principles relate local backbone structures to supersecondary-structure packing arrangements of α-helices and β-strands. Here, we test the generality of the principles by employing them to design larger proteins with five- and six- stranded β-sheets flanked by α-helices. The initial designs were monomeric in solution with high thermal stability, and the nuclear magnetic resonance (NMR) structure of one was close to the design model, but for two others the order of strands in the β-sheet was swapped. Investigation into the origins of this strand swapping suggested that the global structures of the design models were more strained than the NMR structures. We incorporated explicit consideration of global backbone strain into the design methodology, and succeeded in designing proteins with the intended unswapped strand arrangements. These results illustrate the value of experimental structure determination in guiding improvement of de novo design, and the importance of consistency between local, supersecondary, and global tertiary interactions in determining protein topology. The augmented set of principles should inform the design of larger functional proteins.
Collapse
Affiliation(s)
- Nobuyasu Koga
- University of Washington, Department of Biochemistry and Howard Hughes Medical Institute, Seattle, Washington, WA, USA. .,Research Center of Integrative Molecular Systems, Institute for Molecular Science, National Institutes of Natural Sciences, Okazaki, Aichi, Japan. .,Protein Design Group, Exploratory Research Center on Life and Living Systems (ExCELLS), National Institutes of Natural Sciences, Okazaki, Aichi, Japan. .,SOKENDAI, The Graduate University for Advanced Studies, Hayama, Kanagawa, Japan.
| | - Rie Koga
- University of Washington, Department of Biochemistry and Howard Hughes Medical Institute, Seattle, Washington, WA, USA.,Protein Design Group, Exploratory Research Center on Life and Living Systems (ExCELLS), National Institutes of Natural Sciences, Okazaki, Aichi, Japan
| | - Gaohua Liu
- Nexomics Biosciences, Rocky Hill, NJ, USA
| | - Javier Castellanos
- University of Washington, Department of Biochemistry and Howard Hughes Medical Institute, Seattle, Washington, WA, USA
| | - Gaetano T Montelione
- Department of Chemistry and Chemical Biology, and Center for Biotechnology and Interdisciplinary Sciences, Rensselaer Polytechnic Institute, Troy, New York, NY, USA.
| | - David Baker
- University of Washington, Department of Biochemistry and Howard Hughes Medical Institute, Seattle, Washington, WA, USA.
| |
Collapse
|
28
|
Bafna K, White K, Harish B, Rosales R, Ramelot TA, Acton TB, Moreno E, Kehrer T, Miorin L, Royer CA, García-Sastre A, Krug RM, Montelione GT. Hepatitis C virus drugs that inhibit SARS-CoV-2 papain-like protease synergize with remdesivir to suppress viral replication in cell culture. Cell Rep 2021; 35:109133. [PMID: 33984267 PMCID: PMC8075848 DOI: 10.1016/j.celrep.2021.109133] [Citation(s) in RCA: 47] [Impact Index Per Article: 15.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2020] [Revised: 03/18/2021] [Accepted: 04/23/2021] [Indexed: 02/07/2023] Open
Abstract
Effective control of COVID-19 requires antivirals directed against SARS-CoV-2. We assessed 10 hepatitis C virus (HCV) protease-inhibitor drugs as potential SARS-CoV-2 antivirals. There is a striking structural similarity of the substrate binding clefts of SARS-CoV-2 main protease (Mpro) and HCV NS3/4A protease. Virtual docking experiments show that these HCV drugs can potentially bind into the Mpro substrate-binding cleft. We show that seven HCV drugs inhibit both SARS-CoV-2 Mpro protease activity and SARS-CoV-2 virus replication in Vero and/or human cells. However, their Mpro inhibiting activities did not correlate with their antiviral activities. This conundrum is resolved by demonstrating that four HCV protease inhibitor drugs, simeprevir, vaniprevir, paritaprevir, and grazoprevir inhibit the SARS CoV-2 papain-like protease (PLpro). HCV drugs that inhibit PLpro synergize with the viral polymerase inhibitor remdesivir to inhibit virus replication, increasing remdesivir's antiviral activity as much as 10-fold, while those that only inhibit Mpro do not synergize with remdesivir.
Collapse
Affiliation(s)
- Khushboo Bafna
- Department of Chemistry and Chemical Biology, and Center for Biotechnology and Interdisciplinary Sciences, Rensselaer Polytechnic Institute, Troy, NY 12180, USA
| | - Kris White
- Department of Microbiology, and Global Health and Emerging Pathogens Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Balasubramanian Harish
- Department of Biology, and Center for Biotechnology and Interdisciplinary Sciences, Rensselaer Polytechnic Institute, Troy, NY 12180, USA
| | - Romel Rosales
- Department of Microbiology, and Global Health and Emerging Pathogens Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Theresa A Ramelot
- Department of Chemistry and Chemical Biology, and Center for Biotechnology and Interdisciplinary Sciences, Rensselaer Polytechnic Institute, Troy, NY 12180, USA
| | - Thomas B Acton
- Department of Chemistry and Chemical Biology, and Center for Biotechnology and Interdisciplinary Sciences, Rensselaer Polytechnic Institute, Troy, NY 12180, USA
| | - Elena Moreno
- Department of Microbiology, and Global Health and Emerging Pathogens Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Thomas Kehrer
- Department of Microbiology, and Global Health and Emerging Pathogens Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Lisa Miorin
- Department of Microbiology, and Global Health and Emerging Pathogens Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Catherine A Royer
- Department of Biology, and Center for Biotechnology and Interdisciplinary Sciences, Rensselaer Polytechnic Institute, Troy, NY 12180, USA
| | - Adolfo García-Sastre
- Department of Microbiology, and Global Health and Emerging Pathogens Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; Department of Medicine, Division of Infectious Diseases, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; The Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.
| | - Robert M Krug
- Department of Molecular Biosciences, John Ring LaMontagne Center for Infectious Disease, Institute for Cellular and Molecular Biology, University of Texas at Austin, Austin, TX 78712, USA.
| | - Gaetano T Montelione
- Department of Chemistry and Chemical Biology, and Center for Biotechnology and Interdisciplinary Sciences, Rensselaer Polytechnic Institute, Troy, NY 12180, USA.
| |
Collapse
|
29
|
Bafna K, White K, Harish B, Rosales R, Ramelot TA, Acton TB, Moreno E, Kehrer T, Miorin L, Royer CA, García-Sastre A, Krug RM, Montelione GT. Hepatitis C virus drugs that inhibit SARS-CoV-2 papain-like protease synergize with remdesivir to suppress viral replication in cell culture. Cell Rep 2021. [PMID: 33984267 DOI: 10.1101/2020.12.13.422511] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/13/2023] Open
Abstract
Effective control of COVID-19 requires antivirals directed against SARS-CoV-2. We assessed 10 hepatitis C virus (HCV) protease-inhibitor drugs as potential SARS-CoV-2 antivirals. There is a striking structural similarity of the substrate binding clefts of SARS-CoV-2 main protease (Mpro) and HCV NS3/4A protease. Virtual docking experiments show that these HCV drugs can potentially bind into the Mpro substrate-binding cleft. We show that seven HCV drugs inhibit both SARS-CoV-2 Mpro protease activity and SARS-CoV-2 virus replication in Vero and/or human cells. However, their Mpro inhibiting activities did not correlate with their antiviral activities. This conundrum is resolved by demonstrating that four HCV protease inhibitor drugs, simeprevir, vaniprevir, paritaprevir, and grazoprevir inhibit the SARS CoV-2 papain-like protease (PLpro). HCV drugs that inhibit PLpro synergize with the viral polymerase inhibitor remdesivir to inhibit virus replication, increasing remdesivir's antiviral activity as much as 10-fold, while those that only inhibit Mpro do not synergize with remdesivir.
Collapse
Affiliation(s)
- Khushboo Bafna
- Department of Chemistry and Chemical Biology, and Center for Biotechnology and Interdisciplinary Sciences, Rensselaer Polytechnic Institute, Troy, NY 12180, USA
| | - Kris White
- Department of Microbiology, and Global Health and Emerging Pathogens Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Balasubramanian Harish
- Department of Biology, and Center for Biotechnology and Interdisciplinary Sciences, Rensselaer Polytechnic Institute, Troy, NY 12180, USA
| | - Romel Rosales
- Department of Microbiology, and Global Health and Emerging Pathogens Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Theresa A Ramelot
- Department of Chemistry and Chemical Biology, and Center for Biotechnology and Interdisciplinary Sciences, Rensselaer Polytechnic Institute, Troy, NY 12180, USA
| | - Thomas B Acton
- Department of Chemistry and Chemical Biology, and Center for Biotechnology and Interdisciplinary Sciences, Rensselaer Polytechnic Institute, Troy, NY 12180, USA
| | - Elena Moreno
- Department of Microbiology, and Global Health and Emerging Pathogens Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Thomas Kehrer
- Department of Microbiology, and Global Health and Emerging Pathogens Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Lisa Miorin
- Department of Microbiology, and Global Health and Emerging Pathogens Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Catherine A Royer
- Department of Biology, and Center for Biotechnology and Interdisciplinary Sciences, Rensselaer Polytechnic Institute, Troy, NY 12180, USA
| | - Adolfo García-Sastre
- Department of Microbiology, and Global Health and Emerging Pathogens Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; Department of Medicine, Division of Infectious Diseases, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; The Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.
| | - Robert M Krug
- Department of Molecular Biosciences, John Ring LaMontagne Center for Infectious Disease, Institute for Cellular and Molecular Biology, University of Texas at Austin, Austin, TX 78712, USA.
| | - Gaetano T Montelione
- Department of Chemistry and Chemical Biology, and Center for Biotechnology and Interdisciplinary Sciences, Rensselaer Polytechnic Institute, Troy, NY 12180, USA.
| |
Collapse
|
30
|
Mehla J, Liechti G, Morgenstein RM, Caufield JH, Hosseinnia A, Gagarinova A, Phanse S, Goodacre N, Brockett M, Sakhawalkar N, Babu M, Xiao R, Montelione GT, Vorobiev S, den Blaauwen T, Hunt JF, Uetz P. ZapG (YhcB/DUF1043), a novel cell division protein in gamma-proteobacteria linking the Z-ring to septal peptidoglycan synthesis. J Biol Chem 2021; 296:100700. [PMID: 33895137 PMCID: PMC8163987 DOI: 10.1016/j.jbc.2021.100700] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2021] [Revised: 04/14/2021] [Accepted: 04/21/2021] [Indexed: 01/26/2023] Open
Abstract
YhcB, a poorly understood protein conserved across gamma-proteobacteria, contains a domain of unknown function (DUF1043) and an N-terminal transmembrane domain. Here, we used an integrated approach including X-ray crystallography, genetics, and molecular biology to investigate the function and structure of YhcB. The Escherichia coli yhcB KO strain does not grow at 45 °C and is hypersensitive to cell wall–acting antibiotics, even in the stationary phase. The deletion of yhcB leads to filamentation, abnormal FtsZ ring formation, and aberrant septum development. The Z-ring is essential for the positioning of the septa and the initiation of cell division. We found that YhcB interacts with proteins of the divisome (e.g., FtsI, FtsQ) and elongasome (e.g., RodZ, RodA). Seven of these interactions are also conserved in Yersinia pestis and/or Vibrio cholerae. Furthermore, we mapped the amino acid residues likely involved in the interactions of YhcB with FtsI and RodZ. The 2.8 Å crystal structure of the cytosolic domain of Haemophilus ducreyi YhcB shows a unique tetrameric α-helical coiled-coil structure likely to be involved in linking the Z-ring to the septal peptidoglycan-synthesizing complexes. In summary, YhcB is a conserved and conditionally essential protein that plays a role in cell division and consequently affects envelope biogenesis. Based on these findings, we propose to rename YhcB to ZapG (Z-ring-associated protein G). This study will serve as a starting point for future studies on this protein family and on how cells transit from exponential to stationary survival.
Collapse
Affiliation(s)
- Jitender Mehla
- Center for the Study of Biological Complexity, Virginia Commonwealth University, Richmond, Virginia, USA.
| | - George Liechti
- Department of Microbiology and Immunology, Henry Jackson Foundation, Uniformed Services University of the Health Sciences, Bethesda, Maryland, USA
| | - Randy M Morgenstein
- Department of Microbiology and Molecular Genetics, Oklahoma State University, Stillwater, Oklahoma, USA
| | - J Harry Caufield
- Center for the Study of Biological Complexity, Virginia Commonwealth University, Richmond, Virginia, USA
| | - Ali Hosseinnia
- Department of Biochemistry, Research and Innovation Centre, University of Regina, Regina, Saskatchewan, Canada
| | - Alla Gagarinova
- Department of Biochemistry, College of Medicine, University of Saskatchewan, Saskatoon, Saskatchewan, Canada
| | - Sadhna Phanse
- Department of Biochemistry, Research and Innovation Centre, University of Regina, Regina, Saskatchewan, Canada
| | - Norman Goodacre
- Center for the Study of Biological Complexity, Virginia Commonwealth University, Richmond, Virginia, USA
| | - Mary Brockett
- Department of Microbiology and Immunology, Henry Jackson Foundation, Uniformed Services University of the Health Sciences, Bethesda, Maryland, USA
| | - Neha Sakhawalkar
- Center for the Study of Biological Complexity, Virginia Commonwealth University, Richmond, Virginia, USA
| | - Mohan Babu
- Department of Biochemistry, Research and Innovation Centre, University of Regina, Regina, Saskatchewan, Canada
| | - Rong Xiao
- Nexomics Biosciences Inc., Rocky Hill, New Jersey, USA; Department of Chemistry and Chemical Biology, and Center for Biotechnology and Interdisciplinary Sciences, Rensselaer Polytechnic Institute, Troy, New York, USA
| | - Gaetano T Montelione
- Department of Chemistry and Chemical Biology, and Center for Biotechnology and Interdisciplinary Sciences, Rensselaer Polytechnic Institute, Troy, New York, USA; Department of Chemistry and Chemical Biology, Rensselaer Polytechnic Institute, Troy, New York, USA
| | - Sergey Vorobiev
- Department of Chemistry and Chemical Biology, and Center for Biotechnology and Interdisciplinary Sciences, Rensselaer Polytechnic Institute, Troy, New York, USA; Department of Biological Sciences, Columbia University, New York, New York, USA
| | - Tanneke den Blaauwen
- Bacterial Cell Biology & Physiology, Swammerdam Institute for Life Sciences, University of Amsterdam, Amsterdam, Netherlands
| | - John F Hunt
- Department of Chemistry and Chemical Biology, and Center for Biotechnology and Interdisciplinary Sciences, Rensselaer Polytechnic Institute, Troy, New York, USA; Department of Biological Sciences, Columbia University, New York, New York, USA
| | - Peter Uetz
- Center for the Study of Biological Complexity, Virginia Commonwealth University, Richmond, Virginia, USA.
| |
Collapse
|
31
|
Aiyer S, Swapna GVT, Ma LC, Liu G, Hao J, Chalmers G, Jacobs BC, Montelione GT, Roth MJ. A common binding motif in the ET domain of BRD3 forms polymorphic structural interfaces with host and viral proteins. Structure 2021; 29:886-898.e6. [PMID: 33592170 DOI: 10.1016/j.str.2021.01.010] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2020] [Revised: 12/22/2020] [Accepted: 01/21/2021] [Indexed: 12/23/2022]
Abstract
The extraterminal (ET) domain of BRD3 is conserved among BET proteins (BRD2, BRD3, BRD4), interacting with multiple host and viral protein-protein networks. Solution NMR structures of complexes formed between the BRD3 ET domain and either the 79-residue murine leukemia virus integrase (IN) C-terminal domain (IN329-408) or its 22-residue IN tail peptide (IN386-407) alone reveal similar intermolecular three-stranded β-sheet formations. 15N relaxation studies reveal a 10-residue linker region (IN379-388) tethering the SH3 domain (IN329-378) to the ET-binding motif (IN389-405):ET complex. This linker has restricted flexibility, affecting its potential range of orientations in the IN:nucleosome complex. The complex of the ET-binding peptide of the host NSD3 protein (NSD3148-184) and the BRD3 ET domain includes a similar three-stranded β-sheet interaction, but the orientation of the β hairpin is flipped compared with the two IN:ET complexes. These studies expand our understanding of molecular recognition polymorphism in complexes of ET-binding motifs with viral and host proteins.
Collapse
Affiliation(s)
- Sriram Aiyer
- Department of Pharmacology, Robert Wood Johnson Medical School, Rutgers University, Piscataway, NJ 08854, USA; Department of Biochemistry and Molecular Biology, Robert Wood Johnson Medical School, Rutgers University, Piscataway, NJ 08854, USA
| | - G V T Swapna
- Department of Pharmacology, Robert Wood Johnson Medical School, Rutgers University, Piscataway, NJ 08854, USA; Department of Biochemistry and Molecular Biology, Robert Wood Johnson Medical School, Rutgers University, Piscataway, NJ 08854, USA
| | - Li-Chung Ma
- Department of Pharmacology, Robert Wood Johnson Medical School, Rutgers University, Piscataway, NJ 08854, USA; Department of Biochemistry and Molecular Biology, Robert Wood Johnson Medical School, Rutgers University, Piscataway, NJ 08854, USA
| | - Gaohua Liu
- Department of Biochemistry and Molecular Biology, Robert Wood Johnson Medical School, Rutgers University, Piscataway, NJ 08854, USA
| | - Jingzhou Hao
- Department of Biochemistry and Molecular Biology, Robert Wood Johnson Medical School, Rutgers University, Piscataway, NJ 08854, USA; Department of Chemistry and Chemical Biology, Center for Biotechnology and Interdisciplinary Sciences, Rensselaer Polytechnic Institute, Troy, NY 12180, USA
| | - Gordon Chalmers
- Department of Chemistry and Chemical Biology, Center for Biotechnology and Interdisciplinary Sciences, Rensselaer Polytechnic Institute, Troy, NY 12180, USA
| | - Brian C Jacobs
- Department of Pharmacology, Robert Wood Johnson Medical School, Rutgers University, Piscataway, NJ 08854, USA
| | - Gaetano T Montelione
- Department of Biochemistry and Molecular Biology, Robert Wood Johnson Medical School, Rutgers University, Piscataway, NJ 08854, USA; Department of Chemistry and Chemical Biology, Center for Biotechnology and Interdisciplinary Sciences, Rensselaer Polytechnic Institute, Troy, NY 12180, USA.
| | - Monica J Roth
- Department of Pharmacology, Robert Wood Johnson Medical School, Rutgers University, Piscataway, NJ 08854, USA; Department of Biochemistry and Molecular Biology, Robert Wood Johnson Medical School, Rutgers University, Piscataway, NJ 08854, USA.
| |
Collapse
|
32
|
Maisuradze GG, Montelione GT, Rackovsky S, Skolnick J. Tribute to Harold A. Scheraga. J Phys Chem B 2020; 124:10301-10302. [DOI: 10.1021/acs.jpcb.0c08867] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
|
33
|
Bafna K, Krug RM, Montelione GT. Structural Similarity of SARS-CoV2 M pro and HCV NS3/4A Proteases Suggests New Approaches for Identifying Existing Drugs Useful as COVID-19 Therapeutics. ChemRxiv 2020:10.26434/chemrxiv.12153615.v1. [PMID: 32511291 PMCID: PMC7263768 DOI: 10.26434/chemrxiv.12153615] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/20/2023]
Abstract
During the current COVID-19 pandemic more than 160,000 people have died worldwide as of mid-April 2020, and the global economy has been crippled. Effective control of the SARS-CoV2 virus that causes the COVID-19 pandemic requires both vaccines and antivirals. Antivirals are particularly crucial to treat infected people during the period of time that an effective vaccine is being developed and deployed. Because the development of specific antiviral drugs can take a considerable length of time, an important approach is to identify existing drugs already approved for use in humans which could be repurposed as COVID-19 therapeutics. Here we focus on antivirals directed against the SARS-CoV2 Mpro protease, which is required for virus replication. A structural similarity search showed that the Hepatitis C virus (HCV) NS3/4A protease has a striking three-dimensional structural similarity to the SARS-CoV2 Mpro protease, particularly in the arrangement of key active site residues. We used virtual docking predictions to assess the hypothesis that existing drugs already approved for human use or clinical testing that are directed at the HCV NS3/4A protease might fit well into the active-site cleft of the SARS-CoV2 protease (Mpro). AutoDock docking scores for 12 HCV protease inhibitors and 9 HIV-1 protease inhibitors were determined and compared to the docking scores for an α-ketoamide inhibitor of Mpro, which has recently been shown to inhibit SARS-CoV2 virus replication in cell culture. We identified eight HCV protease inhibitors that bound to the Mpro active site with higher docking scores than the α-ketoamide inhibitor, suggesting that these protease inhibitors may effectively bind to the Mpro active site. These results provide the rationale for us to test the identified HCV protease inhibitors as inhibitors of the SARS-CoV2 protease, and as inhibitors of SARS-CoV2 virus replication. Subsequently these repurposed drugs could be evaluated as COVID-19 therapeutics.
Collapse
Affiliation(s)
- Khushboo Bafna
- Department of Chemistry and Chemical Biology, Center for Biotechnology and Interdisciplinary Sciences, Rensselaer Polytechnic Institute, Troy, New York, 12180
| | - Robert M. Krug
- Department of Molecular Biosciences, John Ring LaMontagne Center for Infectious Disease, Institute for Cellular and Molecular Biology, University of Texas at Austin, Austin, Texas 78712 USA
| | - Gaetano T. Montelione
- Department of Chemistry and Chemical Biology, Center for Biotechnology and Interdisciplinary Sciences, Rensselaer Polytechnic Institute, Troy, New York, 12180
| |
Collapse
|
34
|
Bafna K, Krug RM, Montelione GT. Structural Similarity of SARS-CoV2 M pro and HCV NS3/4A Proteases Suggests New Approaches for Identifying Existing Drugs Useful as COVID-19 Therapeutics. ChemRxiv 2020:10.26434/chemrxiv.12153615.v1. [PMID: 32511291 PMCID: PMC7263768 DOI: 10.26434/chemrxiv.12153615.v1+10.26434/chemrxiv.12153615] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 01/20/2024]
Abstract
During the current COVID-19 pandemic more than 160,000 people have died worldwide as of mid-April 2020, and the global economy has been crippled. Effective control of the SARS-CoV2 virus that causes the COVID-19 pandemic requires both vaccines and antivirals. Antivirals are particularly crucial to treat infected people during the period of time that an effective vaccine is being developed and deployed. Because the development of specific antiviral drugs can take a considerable length of time, an important approach is to identify existing drugs already approved for use in humans which could be repurposed as COVID-19 therapeutics. Here we focus on antivirals directed against the SARS-CoV2 Mpro protease, which is required for virus replication. A structural similarity search showed that the Hepatitis C virus (HCV) NS3/4A protease has a striking three-dimensional structural similarity to the SARS-CoV2 Mpro protease, particularly in the arrangement of key active site residues. We used virtual docking predictions to assess the hypothesis that existing drugs already approved for human use or clinical testing that are directed at the HCV NS3/4A protease might fit well into the active-site cleft of the SARS-CoV2 protease (Mpro). AutoDock docking scores for 12 HCV protease inhibitors and 9 HIV-1 protease inhibitors were determined and compared to the docking scores for an α-ketoamide inhibitor of Mpro, which has recently been shown to inhibit SARS-CoV2 virus replication in cell culture. We identified eight HCV protease inhibitors that bound to the Mpro active site with higher docking scores than the α-ketoamide inhibitor, suggesting that these protease inhibitors may effectively bind to the Mpro active site. These results provide the rationale for us to test the identified HCV protease inhibitors as inhibitors of the SARS-CoV2 protease, and as inhibitors of SARS-CoV2 virus replication. Subsequently these repurposed drugs could be evaluated as COVID-19 therapeutics.
Collapse
Affiliation(s)
- Khushboo Bafna
- Department of Chemistry and Chemical Biology, Center for Biotechnology and Interdisciplinary Sciences, Rensselaer Polytechnic Institute, Troy, New York, 12180
| | - Robert M. Krug
- Department of Molecular Biosciences, John Ring LaMontagne Center for Infectious Disease, Institute for Cellular and Molecular Biology, University of Texas at Austin, Austin, Texas 78712 USA
| | - Gaetano T. Montelione
- Department of Chemistry and Chemical Biology, Center for Biotechnology and Interdisciplinary Sciences, Rensselaer Polytechnic Institute, Troy, New York, 12180
| |
Collapse
|
35
|
Bafna K, Krug RM, Montelione GT. Structural Similarity of SARS-CoV2 M pro and HCV NS3/4A Proteases Suggests New Approaches for Identifying Existing Drugs Useful as COVID-19 Therapeutics. ChemRxiv 2020:10.26434/chemrxiv.12153615.v1. [PMID: 32511291 PMCID: PMC7263768 DOI: 10.26434/chemrxiv.12153615.v1 10.26434/chemrxiv.12153615] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
During the current COVID-19 pandemic more than 160,000 people have died worldwide as of mid-April 2020, and the global economy has been crippled. Effective control of the SARS-CoV2 virus that causes the COVID-19 pandemic requires both vaccines and antivirals. Antivirals are particularly crucial to treat infected people during the period of time that an effective vaccine is being developed and deployed. Because the development of specific antiviral drugs can take a considerable length of time, an important approach is to identify existing drugs already approved for use in humans which could be repurposed as COVID-19 therapeutics. Here we focus on antivirals directed against the SARS-CoV2 Mpro protease, which is required for virus replication. A structural similarity search showed that the Hepatitis C virus (HCV) NS3/4A protease has a striking three-dimensional structural similarity to the SARS-CoV2 Mpro protease, particularly in the arrangement of key active site residues. We used virtual docking predictions to assess the hypothesis that existing drugs already approved for human use or clinical testing that are directed at the HCV NS3/4A protease might fit well into the active-site cleft of the SARS-CoV2 protease (Mpro). AutoDock docking scores for 12 HCV protease inhibitors and 9 HIV-1 protease inhibitors were determined and compared to the docking scores for an α-ketoamide inhibitor of Mpro, which has recently been shown to inhibit SARS-CoV2 virus replication in cell culture. We identified eight HCV protease inhibitors that bound to the Mpro active site with higher docking scores than the α-ketoamide inhibitor, suggesting that these protease inhibitors may effectively bind to the Mpro active site. These results provide the rationale for us to test the identified HCV protease inhibitors as inhibitors of the SARS-CoV2 protease, and as inhibitors of SARS-CoV2 virus replication. Subsequently these repurposed drugs could be evaluated as COVID-19 therapeutics.
Collapse
Affiliation(s)
- Khushboo Bafna
- Department of Chemistry and Chemical Biology, Center for Biotechnology and Interdisciplinary Sciences, Rensselaer Polytechnic Institute, Troy, New York, 12180
| | - Robert M. Krug
- Department of Molecular Biosciences, John Ring LaMontagne Center for Infectious Disease, Institute for Cellular and Molecular Biology, University of Texas at Austin, Austin, Texas 78712 USA
| | - Gaetano T. Montelione
- Department of Chemistry and Chemical Biology, Center for Biotechnology and Interdisciplinary Sciences, Rensselaer Polytechnic Institute, Troy, New York, 12180
| |
Collapse
|
36
|
Bafna K, Krug RM, Montelione GT. Structural Similarity of SARS-CoV2 M pro and HCV NS3/4A Proteases Suggests New Approaches for Identifying Existing Drugs Useful as COVID-19 Therapeutics. ChemRxiv 2020. [PMID: 32511291 DOI: 10.26434/chemrxiv.12153615.v1] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
Abstract
During the current COVID-19 pandemic more than 160,000 people have died worldwide as of mid-April 2020, and the global economy has been crippled. Effective control of the SARS-CoV2 virus that causes the COVID-19 pandemic requires both vaccines and antivirals. Antivirals are particularly crucial to treat infected people during the period of time that an effective vaccine is being developed and deployed. Because the development of specific antiviral drugs can take a considerable length of time, an important approach is to identify existing drugs already approved for use in humans which could be repurposed as COVID-19 therapeutics. Here we focus on antivirals directed against the SARS-CoV2 Mpro protease, which is required for virus replication. A structural similarity search showed that the Hepatitis C virus (HCV) NS3/4A protease has a striking three-dimensional structural similarity to the SARS-CoV2 Mpro protease, particularly in the arrangement of key active site residues. We used virtual docking predictions to assess the hypothesis that existing drugs already approved for human use or clinical testing that are directed at the HCV NS3/4A protease might fit well into the active-site cleft of the SARS-CoV2 protease (Mpro). AutoDock docking scores for 12 HCV protease inhibitors and 9 HIV-1 protease inhibitors were determined and compared to the docking scores for an α-ketoamide inhibitor of Mpro, which has recently been shown to inhibit SARS-CoV2 virus replication in cell culture. We identified eight HCV protease inhibitors that bound to the Mpro active site with higher docking scores than the α-ketoamide inhibitor, suggesting that these protease inhibitors may effectively bind to the Mpro active site. These results provide the rationale for us to test the identified HCV protease inhibitors as inhibitors of the SARS-CoV2 protease, and as inhibitors of SARS-CoV2 virus replication. Subsequently these repurposed drugs could be evaluated as COVID-19 therapeutics.
Collapse
Affiliation(s)
- Khushboo Bafna
- Department of Chemistry and Chemical Biology, Center for Biotechnology and Interdisciplinary Sciences, Rensselaer Polytechnic Institute, Troy, New York, 12180
| | - Robert M Krug
- Department of Molecular Biosciences, John Ring LaMontagne Center for Infectious Disease, Institute for Cellular and Molecular Biology, University of Texas at Austin, Austin, Texas 78712 USA
| | - Gaetano T Montelione
- Department of Chemistry and Chemical Biology, Center for Biotechnology and Interdisciplinary Sciences, Rensselaer Polytechnic Institute, Troy, New York, 12180
| |
Collapse
|
37
|
Chen G, Ma LC, Wang S, Woltz RL, Grasso EM, Montelione GT, Krug RM. A double-stranded RNA platform is required for the interaction between a host restriction factor and the NS1 protein of influenza A virus. Nucleic Acids Res 2020; 48:304-315. [PMID: 31754723 PMCID: PMC6943125 DOI: 10.1093/nar/gkz1094] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2019] [Revised: 10/18/2019] [Accepted: 11/05/2019] [Indexed: 01/02/2023] Open
Abstract
Influenza A viruses cause widespread human respiratory disease. The viral multifunctional NS1 protein inhibits host antiviral responses. This inhibition results from the binding of specific cellular antiviral proteins at various positions on the NS1 protein. Remarkably, binding of several proteins also requires the two amino-acid residues in the NS1 N-terminal RNA-binding domain (RBD) that are required for binding double-stranded RNA (dsRNA). Here we focus on the host restriction factor DHX30 helicase that is countered by the NS1 protein, and establish why the dsRNA-binding activity of NS1 is required for its binding to DHX30. We show that the N-terminal 152 amino-acid residue segment of DHX30, denoted DHX30N, possesses all the antiviral activity of DHX30 and contains a dsRNA-binding domain, and that the NS1-DHX30 interaction in vivo requires the dsRNA-binding activity of both DHX30N and the NS1 RBD. We demonstrate why this is the case using bacteria-expressed proteins: the DHX30N-NS1 RBD interaction in vitro requires the presence of a dsRNA platform that binds both NS1 RBD and DHX30N. We propose that a similar dsRNA platform functions in interactions of the NS1 protein with other proteins that requires these same two amino-acid residues required for NS1 RBD dsRNA-binding activity.
Collapse
Affiliation(s)
- Guifang Chen
- Department of Molecular Biosciences, John Ring LaMontagne Center for Infectious Disease, Institute for Cellular and Molecular Biology, University of Texas at Austin, Austin, TX 78712, USA
| | - Li-Chung Ma
- Center for Advanced Biotechnology and Medicine, Department of Molecular Biology and Biochemistry, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA.,Department of Biochemistry and Molecular Biology, Robert Wood Johnson Medical School, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Shanshan Wang
- Department of Molecular Biosciences, John Ring LaMontagne Center for Infectious Disease, Institute for Cellular and Molecular Biology, University of Texas at Austin, Austin, TX 78712, USA
| | - Ryan L Woltz
- Center for Advanced Biotechnology and Medicine, Department of Molecular Biology and Biochemistry, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA.,Department of Biochemistry and Molecular Biology, Robert Wood Johnson Medical School, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Emily M Grasso
- Center for Advanced Biotechnology and Medicine, Department of Molecular Biology and Biochemistry, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA.,Department of Biochemistry and Molecular Biology, Robert Wood Johnson Medical School, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Gaetano T Montelione
- Center for Advanced Biotechnology and Medicine, Department of Molecular Biology and Biochemistry, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA.,Department of Biochemistry and Molecular Biology, Robert Wood Johnson Medical School, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA.,Center for Biotechnology and Interdisciplinary Studies, and Department of Chemistry and Chemical Biology, Rensselaer Polytechnic Institute, Troy, NY 12180, USA
| | - Robert M Krug
- Department of Molecular Biosciences, John Ring LaMontagne Center for Infectious Disease, Institute for Cellular and Molecular Biology, University of Texas at Austin, Austin, TX 78712, USA
| |
Collapse
|
38
|
Grime GW, Zeldin OB, Snell ME, Lowe ED, Hunt JF, Montelione GT, Tong L, Snell EH, Garman EF. High-Throughput PIXE as an Essential Quantitative Assay for Accurate Metalloprotein Structural Analysis: Development and Application. J Am Chem Soc 2019; 142:185-197. [PMID: 31794207 DOI: 10.1021/jacs.9b09186] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
Metalloproteins comprise over one-third of proteins, with approximately half of all enzymes requiring metal to function. Accurate identification of these metal atoms and their environment is a prerequisite to understanding biological mechanism. Using ion beam analysis through particle induced X-ray emission (PIXE), we have quantitatively identified the metal atoms in 30 previously structurally characterized proteins using minimal sample volume and a high-throughput approach. Over half of these metals had been misidentified in the deposited structural models. Some of the PIXE detected metals not seen in the models were explainable as artifacts from promiscuous crystallization reagents. For others, using the correct metal improved the structural models. For multinuclear sites, anomalous diffraction signals enabled the positioning of the correct metals to reveal previously obscured biological information. PIXE is insensitive to the chemical environment, but coupled with experimental diffraction data deposited alongside the structural model it enables validation and potential remediation of metalloprotein models, improving structural and, more importantly, mechanistic knowledge.
Collapse
Affiliation(s)
- Geoffrey W Grime
- Ion Beam Centre, Advanced Technology Institute , University of Surrey , Guildford, Surrey GU2 7XH , United Kingdom
| | - Oliver B Zeldin
- Department of Biochemistry , University of Oxford , South Parks Road , Oxford OX1 3QU , United Kingdom
| | - Mary E Snell
- Hauptman-Woodward Medical Research Institute , 700 Ellicott St. , Buffalo , New York 14203 , United States
| | - Edward D Lowe
- Department of Biochemistry , University of Oxford , South Parks Road , Oxford OX1 3QU , United Kingdom
| | - John F Hunt
- Department of Biological Sciences , Columbia University , New York , New York 10027 , United States
| | - Gaetano T Montelione
- Department of Chemistry and Chemical Biology, Center for Biotechnology and Interdisciplinary Sciences , Rensselaer Polytechnic Institute , Troy New York 12180 United States
| | - Liang Tong
- Department of Biological Sciences , Columbia University , New York , New York 10027 , United States
| | - Edward H Snell
- Hauptman-Woodward Medical Research Institute , 700 Ellicott St. , Buffalo , New York 14203 , United States.,Materials Design and Innovation , SUNY Buffalo , 700 Ellicott St. , Buffalo , New York 14203 , United States
| | - Elspeth F Garman
- Department of Biochemistry , University of Oxford , South Parks Road , Oxford OX1 3QU , United Kingdom
| |
Collapse
|
39
|
Berman HM, Adams PD, Bonvin AA, Burley SK, Carragher B, Chiu W, DiMaio F, Ferrin TE, Gabanyi MJ, Goddard TD, Griffin PR, Haas J, Hanke CA, Hoch JC, Hummer G, Kurisu G, Lawson CL, Leitner A, Markley JL, Meiler J, Montelione GT, Phillips GN, Prisner T, Rappsilber J, Schriemer DC, Schwede T, Seidel CAM, Strutzenberg TS, Svergun DI, Tajkhorshid E, Trewhella J, Vallat B, Velankar S, Vuister GW, Webb B, Westbrook JD, White KL, Sali A. Federating Structural Models and Data: Outcomes from A Workshop on Archiving Integrative Structures. Structure 2019; 27:1745-1759. [PMID: 31780431 DOI: 10.1016/j.str.2019.11.002] [Citation(s) in RCA: 33] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2019] [Revised: 10/31/2019] [Accepted: 11/06/2019] [Indexed: 12/23/2022]
Abstract
Structures of biomolecular systems are increasingly computed by integrative modeling. In this approach, a structural model is constructed by combining information from multiple sources, including varied experimental methods and prior models. In 2019, a Workshop was held as a Biophysical Society Satellite Meeting to assess progress and discuss further requirements for archiving integrative structures. The primary goal of the Workshop was to build consensus for addressing the challenges involved in creating common data standards, building methods for federated data exchange, and developing mechanisms for validating integrative structures. The summary of the Workshop and the recommendations that emerged are presented here.
Collapse
Affiliation(s)
- Helen M Berman
- Department of Chemistry and Chemical Biology, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA; Department of Biological Sciences, University of Southern California, Los Angeles, CA 90089, USA; Bridge Institute, Michelson Center, University of Southern California, Los Angeles, CA 90089, USA.
| | - Paul D Adams
- Physical Biosciences Division, Lawrence Berkeley Laboratory, Berkeley, CA 94720-8235, USA; Department of Bioengineering, University of California-Berkeley, Berkeley, CA 94720, USA
| | - Alexandre A Bonvin
- Bijvoet Center for Biomolecular Research, Faculty of Science - Chemistry, Utrecht University, Padualaan 8, 3584 CH Utrecht, the Netherlands
| | - Stephen K Burley
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, The State University of New Jersey, Piscataway, NJ 08854, USA; Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA; Skaggs School of Pharmacy and Pharmaceutical Sciences and San Diego Supercomputer Center, University of California, San Diego, La Jolla, CA 92093, USA; Rutgers Cancer Institute of New Jersey, Rutgers, The State University of New Jersey, New Brunswick, NJ 08903, USA
| | - Bridget Carragher
- Simons Electron Microscopy Center, New York Structural Biology Center, New York, NY 10027, USA; Department of Biochemistry and Molecular Biophysics, Columbia University, New York, NY 10032, USA
| | - Wah Chiu
- Department of Bioengineering, Department of Microbiology and Immunology, Stanford University, Stanford, CA 94305-5447, USA; SLAC National Accelerator Laboratory, Menlo Park, CA 94025, USA
| | - Frank DiMaio
- Department of Biochemistry and Institute for Protein Design, University of Washington, Seattle, WA 98195, USA
| | - Thomas E Ferrin
- Department of Pharmaceutical Chemistry, University of California, San Francisco, CA 94158, USA
| | - Margaret J Gabanyi
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Thomas D Goddard
- Department of Pharmaceutical Chemistry, University of California, San Francisco, CA 94158, USA
| | | | - Juergen Haas
- Swiss Institute of Bioinformatics and Biozentrum, University of Basel, 4056 Basel, Switzerland
| | - Christian A Hanke
- Molecular Physical Chemistry, Heinrich Heine University Düsseldorf, 40225 Düsseldorf, Germany
| | - Jeffrey C Hoch
- Department of Molecular Biology and Biophysics, UConn Health, Farmington, CT 06030, USA
| | - Gerhard Hummer
- Department of Theoretical Biophysics, Max Planck Institute of Biophysics, 60438 Frankfurt am Main, Germany; Institute for Biophysics, Goethe University Frankfurt, 60438 Frankfurt am Main, Germany
| | - Genji Kurisu
- Protein Data Bank Japan (PDBj), Institute for Protein Research, Osaka University, Osaka 565-0871, Japan
| | - Catherine L Lawson
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Alexander Leitner
- Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, 8093 Zurich, Switzerland
| | - John L Markley
- BioMagResBank (BMRB), Biochemistry Department, University of Wisconsin-Madison, Madison, WI 53706, USA
| | - Jens Meiler
- Center for Structural Biology, Vanderbilt University, 465 21st Avenue South, Nashville, TN 37221, USA
| | - Gaetano T Montelione
- Center for Advanced Biotechnology and Medicine, Department of Molecular Biology and Biochemistry, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA; Department of Biochemistry, Robert Wood Johnson Medical School, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA; Center for Biotechnology and Interdisciplinary Studies, Rensselaer Polytech Institute, Troy, NY 12180, USA
| | - George N Phillips
- BioSciences at Rice and Department of Chemistry, Rice University, Houston, TX 77251, USA
| | - Thomas Prisner
- Institute of Physical and Theoretical Chemistry and Center of Biomolecular Magnetic Resonance, Goethe University Frankfurt, 60438 Frankfurt am Main, Germany
| | - Juri Rappsilber
- Wellcome Trust Centre for Cell Biology, Edinburgh EH9 3JR, Scotland
| | - David C Schriemer
- Department of Biochemistry & Molecular Biology, Robson DNA Science Centre, University of Calgary, Calgary, AB T2N 4N1, Canada
| | - Torsten Schwede
- Swiss Institute of Bioinformatics and Biozentrum, University of Basel, 4056 Basel, Switzerland
| | - Claus A M Seidel
- Molecular Physical Chemistry, Heinrich Heine University Düsseldorf, 40225 Düsseldorf, Germany
| | | | - Dmitri I Svergun
- European Molecular Biology Laboratory (EMBL), Hamburg Outstation, Notkestrasse 85, 22607 Hamburg, Germany
| | - Emad Tajkhorshid
- Department of Biochemistry, NIH Center for Macromolecular Modeling and Bioinformatics, Center for Biophysics and Quantitative Biology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA; Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
| | - Jill Trewhella
- School of Life and Environmental Sciences, The University of Sydney, Sydney, NSW 2006, Australia; Department of Chemistry, University of Utah, Salt Lake City, UT 84112, USA
| | - Brinda Vallat
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Sameer Velankar
- Protein Data Bank in Europe (PDBe), European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, Cambridgeshire CB10 1SD, UK
| | - Geerten W Vuister
- Department of Molecular and Cell Biology, Leicester Institute of Structural and Chemical Biology, University of Leicester, Leicester LE1 9HN, UK
| | - Benjamin Webb
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA 94158, USA
| | - John D Westbrook
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, The State University of New Jersey, Piscataway, NJ 08854, USA; Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Kate L White
- Department of Biological Sciences, University of Southern California, Los Angeles, CA 90089, USA; Bridge Institute, Michelson Center, University of Southern California, Los Angeles, CA 90089, USA
| | - Andrej Sali
- Department of Pharmaceutical Chemistry, University of California, San Francisco, CA 94158, USA; Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA 94158, USA; California Institute for Quantitative Biosciences, University of California, San Francisco, San Francisco, CA 94158, USA.
| |
Collapse
|
40
|
Wang X, Jing X, Deng Y, Nie Y, Xu F, Xu Y, Zhao YL, Hunt JF, Montelione GT, Szyperski T. Evolutionary coupling saturation mutagenesis: Coevolution-guided identification of distant sites influencing Bacillus naganoensis pullulanase activity. FEBS Lett 2019; 594:799-812. [PMID: 31665817 DOI: 10.1002/1873-3468.13652] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2019] [Revised: 10/15/2019] [Accepted: 10/25/2019] [Indexed: 01/20/2023]
Abstract
Pullulanases are well-known debranching enzymes hydrolyzing α-1,6-glycosidic linkages. To date, engineering of pullulanase is mainly focused on catalytic pocket or domain tailoring based on structure/sequence information. Saturation mutagenesis-involved directed evolution is, however, limited by the low number of mutational sites compatible with combinatorial libraries of feasible size. Using Bacillus naganoensis pullulanase as a target protein, here we introduce the 'evolutionary coupling saturation mutagenesis' (ECSM) approach: residue pair covariances are calculated to identify residues for saturation mutagenesis, focusing directed evolution on residue pairs playing important roles in natural evolution. Evolutionary coupling (EC) analysis identified seven residue pairs as evolutionary mutational hotspots. Subsequent saturation mutagenesis yielded variants with enhanced catalytic activity. The functional pairs apparently represent distant sites affecting enzyme activity.
Collapse
Affiliation(s)
- Xinye Wang
- School of Biotechnology and Key Laboratory of Industrial Biotechnology, Ministry of Education, Jiangnan University, Wuxi, China
| | - Xiaoran Jing
- School of Biotechnology and Key Laboratory of Industrial Biotechnology, Ministry of Education, Jiangnan University, Wuxi, China
| | - Yi Deng
- School of Biotechnology and Key Laboratory of Industrial Biotechnology, Ministry of Education, Jiangnan University, Wuxi, China
| | - Yao Nie
- School of Biotechnology and Key Laboratory of Industrial Biotechnology, Ministry of Education, Jiangnan University, Wuxi, China
| | - Fei Xu
- School of Biotechnology and Key Laboratory of Industrial Biotechnology, Ministry of Education, Jiangnan University, Wuxi, China
| | - Yan Xu
- School of Biotechnology and Key Laboratory of Industrial Biotechnology, Ministry of Education, Jiangnan University, Wuxi, China.,State Key Laboratory of Food Science and Technology, Jiangnan University, Wuxi, China
| | - Yi-Lei Zhao
- State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic and Developmental Sciences, MOE-LSB & MOE-LSC, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, China
| | - John F Hunt
- Department of Biological Sciences, Columbia University, New York, NY, USA
| | - Gaetano T Montelione
- Center for Advanced Biotechnology and Medicine, Department of Molecular Biology and Biochemistry, Rutgers, The State University of New Jersey, Piscataway, NJ, USA.,Department of Biochemistry and Molecular Biology, Robert Wood Johnson Medical School, Rutgers, The State University of New Jersey, Piscataway, NJ, USA.,Department of Chemistry and Chemical Biology, and Center for Biotechnology and Integrative Studies, Rensselaer Polytechnic Institute, Troy, NY, USA
| | - Thomas Szyperski
- Department of Chemistry, The State University of New York at Buffalo, NY, USA
| |
Collapse
|
41
|
Sala D, Huang YJ, Cole CA, Snyder DA, Liu G, Ishida Y, Swapna GVT, Brock KP, Sander C, Fidelis K, Kryshtafovych A, Inouye M, Tejero R, Valafar H, Rosato A, Montelione GT. Protein structure prediction assisted with sparse NMR data in CASP13. Proteins 2019; 87:1315-1332. [PMID: 31603581 DOI: 10.1002/prot.25837] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2019] [Revised: 09/26/2019] [Accepted: 09/27/2019] [Indexed: 01/05/2023]
Abstract
CASP13 has investigated the impact of sparse NMR data on the accuracy of protein structure prediction. NOESY and 15 N-1 H residual dipolar coupling data, typical of that obtained for 15 N,13 C-enriched, perdeuterated proteins up to about 40 kDa, were simulated for 11 CASP13 targets ranging in size from 80 to 326 residues. For several targets, two prediction groups generated models that are more accurate than those produced using baseline methods. Real NMR data collected for a de novo designed protein were also provided to predictors, including one data set in which only backbone resonance assignments were available. Some NMR-assisted prediction groups also did very well with these data. CASP13 also assessed whether incorporation of sparse NMR data improves the accuracy of protein structure prediction relative to nonassisted regular methods. In most cases, incorporation of sparse, noisy NMR data results in models with higher accuracy. The best NMR-assisted models were also compared with the best regular predictions of any CASP13 group for the same target. For six of 13 targets, the most accurate model provided by any NMR-assisted prediction group was more accurate than the most accurate model provided by any regular prediction group; however, for the remaining seven targets, one or more regular prediction method provided a more accurate model than even the best NMR-assisted model. These results suggest a novel approach for protein structure determination, in which advanced prediction methods are first used to generate structural models, and sparse NMR data is then used to validate and/or refine these models.
Collapse
Affiliation(s)
- Davide Sala
- Magnetic Resonance Center, University of Florence, Sesto Fiorentino, Italy.,Department of Chemistry, University of Florence, Sesto Fiorentino, Italy
| | - Yuanpeng Janet Huang
- Center for Advanced Biotechnology and Medicine, and Department of Molecular Biology and Biochemistry, Rutgers, The State University of New Jersey, Piscataway, New Jersey.,Department of Chemistry and Center for Biotechnology and Interdisciplinary Studies, Rensselaer Polytechnic Institute, Troy, New York
| | - Casey A Cole
- Department of Computer Science & Engineering, University of South Carolina, Columbia, South Carolina
| | - David A Snyder
- Department of Chemistry, College of Science and Health, William Paterson University, Wayne, New Jersey
| | - Gaohua Liu
- Center for Advanced Biotechnology and Medicine, and Department of Molecular Biology and Biochemistry, Rutgers, The State University of New Jersey, Piscataway, New Jersey.,Nexomics Biosciences, Bordentown, New Jersey
| | - Yojiro Ishida
- Center for Advanced Biotechnology and Medicine, and Department of Molecular Biology and Biochemistry, Rutgers, The State University of New Jersey, Piscataway, New Jersey.,Department of Biochemistry and Molecular Biology, The Robert Wood Johnson Medical School, Rutgers, The State University of New Jersey, Piscataway, New Jersey
| | - G V T Swapna
- Center for Advanced Biotechnology and Medicine, and Department of Molecular Biology and Biochemistry, Rutgers, The State University of New Jersey, Piscataway, New Jersey
| | - Kelly P Brock
- Department of Systems Biology, Harvard Medical School, Boston, Massachusetts
| | - Chris Sander
- Department of Cell Biology, Harvard Medical School, Boston, Massachusetts.,cBio Center, Dana-Farber Cancer Institute, Boston, Massachusetts
| | | | | | - Masayori Inouye
- Department of Biochemistry and Molecular Biology, The Robert Wood Johnson Medical School, Rutgers, The State University of New Jersey, Piscataway, New Jersey
| | - Roberto Tejero
- Departamento de Quimica Fisica, Universidad de Valencia, Valencia, Spain
| | - Homayoun Valafar
- Department of Computer Science & Engineering, University of South Carolina, Columbia, South Carolina
| | - Antonio Rosato
- Magnetic Resonance Center, University of Florence, Sesto Fiorentino, Italy.,Department of Chemistry, University of Florence, Sesto Fiorentino, Italy
| | - Gaetano T Montelione
- Center for Advanced Biotechnology and Medicine, and Department of Molecular Biology and Biochemistry, Rutgers, The State University of New Jersey, Piscataway, New Jersey.,Department of Chemistry and Center for Biotechnology and Interdisciplinary Studies, Rensselaer Polytechnic Institute, Troy, New York.,Department of Biochemistry and Molecular Biology, The Robert Wood Johnson Medical School, Rutgers, The State University of New Jersey, Piscataway, New Jersey
| |
Collapse
|
42
|
Zhang M, Yu XW, Xu Y, Guo RT, Swapna GVT, Szyperski T, Hunt JF, Montelione GT. Structural Basis by Which the N-Terminal Polypeptide Segment of Rhizopus chinensis Lipase Regulates Its Substrate Binding Affinity. Biochemistry 2019; 58:3943-3954. [PMID: 31436959 PMCID: PMC7195698 DOI: 10.1021/acs.biochem.9b00462] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Members of an important group of industrial enzymes, Rhizopus lipases, exhibit valuable hydrolytic features that underlie their biological functions. Particularly important is their N-terminal polypeptide segment (NTPS), which is required for secretion and proper folding but is removed in the process of enzyme maturation. A second common feature of this class of lipases is the α-helical "lid", which regulates the accessibility of the substrate to the enzyme active site. Some Rhizopus lipases also exhibit "interfacial activation" by micelle and/or aggregate surfaces. While it has long been recognized that the NTPS is critical for function, its dynamic features have frustrated efforts to characterize its structure by X-ray crystallography. Here, we combine nuclear magnetic resonance spectroscopy and X-ray crystallography to determine the structure and dynamics of Rhizopus chinensis lipase (RCL) with its 27-residue NTPS prosequence (r27RCL). Both r27RCL and the truncated mature form of RCL (mRCL) exhibit biphasic interfacial activation kinetics with p-nitrophenyl butyrate (pNPB). r27RCL exhibits a substrate binding affinity significantly lower than that of mRCL due to stabilization of the closed lid conformation by the NTPS. In contrast to previous predictions, the NTPS does not enhance lipase activity by increasing surface hydrophobicity but rather inhibits activity by forming conserved interactions with both the closed lid and the core protein structure. Single-site mutations and kinetic studies were used to confirm that the NTPS serves as internal competitive inhibitor and to develop a model of the associated process of interfacial activation. These structure-function studies provide the basis for engineering RCL lipases with enhanced catalytic activities.
Collapse
Affiliation(s)
- Meng Zhang
- Key Laboratory of Industrial Biotechnology, Ministry of Education, School of Biotechnology, Jiangnan University, Wuxi 214122, People’s Republic of China
| | - Xiao-Wei Yu
- Key Laboratory of Industrial Biotechnology, Ministry of Education, School of Biotechnology, Jiangnan University, Wuxi 214122, People’s Republic of China
| | - Yan Xu
- Key Laboratory of Industrial Biotechnology, Ministry of Education, School of Biotechnology, Jiangnan University, Wuxi 214122, People’s Republic of China
| | - Rey-Ting Guo
- Industrial Enzyme National Engineering Laboratory, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, People’s Republic of China
| | - G. V. T. Swapna
- Center for Advanced Biotechnology and Medicine, and Department of Molecular Biology and Biochemistry, Rutgers, The State University of New Jersey, Piscataway, New Jersey, 08854, USA
| | - Thomas Szyperski
- Department of Chemistry, State University of New York at Buffalo, Buffalo, New York, 14260. USA
| | - John F. Hunt
- Department of Biological Science, Columbia University, New York, New York 10027, USA
| | - Gaetano T. Montelione
- Center for Advanced Biotechnology and Medicine, and Department of Molecular Biology and Biochemistry, Rutgers, The State University of New Jersey, Piscataway, New Jersey, 08854, USA
- Department of Biochemistry and Molecular Biology, Robert Wood Johnson Medical School, Rutgers, The State University of New Jersey, Piscataway, New Jersey, 08854, USA
| |
Collapse
|
43
|
Ranaivoson FM, Turk LS, Ozgul S, Kakehi S, von Daake S, Lopez N, Trobiani L, De Jaco A, Denissova N, Demeler B, Özkan E, Montelione GT, Comoletti D. A Proteomic Screen of Neuronal Cell-Surface Molecules Reveals IgLONs as Structurally Conserved Interaction Modules at the Synapse. Structure 2019; 27:893-906.e9. [PMID: 30956130 DOI: 10.1016/j.str.2019.03.004] [Citation(s) in RCA: 32] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2018] [Revised: 01/10/2019] [Accepted: 03/07/2019] [Indexed: 12/21/2022]
Abstract
In the developing brain, cell-surface proteins play crucial roles, but their protein-protein interaction network remains largely unknown. A proteomic screen identified 200 interactions, 89 of which were not previously published. Among these interactions, we find that the IgLONs, a family of five cell-surface neuronal proteins implicated in various human disorders, interact as homo- and heterodimers. We reveal their interaction patterns and report the dimeric crystal structures of Neurotrimin (NTRI), IgLON5, and the neuronal growth regulator 1 (NEGR1)/IgLON5 complex. We show that IgLONs maintain an extended conformation and that their dimerization occurs through the first Ig domain of each monomer and is Ca2+ independent. Cell aggregation shows that NTRI and NEGR1 homo- and heterodimerize in trans. Taken together, we report 89 unpublished cell-surface ligand-receptor pairs and describe structural models of trans interactions of IgLONs, showing that their structures are compatible with a model of interaction across the synaptic cleft.
Collapse
Affiliation(s)
| | - Liam S Turk
- Child Health Institute of New Jersey, New Brunswick, NJ 08901, USA
| | - Sinem Ozgul
- Child Health Institute of New Jersey, New Brunswick, NJ 08901, USA
| | - Sumie Kakehi
- Child Health Institute of New Jersey, New Brunswick, NJ 08901, USA
| | | | - Nicole Lopez
- Child Health Institute of New Jersey, New Brunswick, NJ 08901, USA
| | - Laura Trobiani
- Department of Biology and Biotechnology "Charles Darwin" and Pasteur Institute - Cenci Bolognetti Foundation, Sapienza University of Rome, Piazzale Aldo Moro 5, 00185 Rome, Italy
| | - Antonella De Jaco
- Department of Biology and Biotechnology "Charles Darwin" and Pasteur Institute - Cenci Bolognetti Foundation, Sapienza University of Rome, Piazzale Aldo Moro 5, 00185 Rome, Italy
| | - Natalia Denissova
- Department of Molecular Biology and Biochemistry and Center for Advanced Biotechnology and Medicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Borries Demeler
- Department of Chemistry and Biochemistry, University of Lethbridge, 4401 University Drive, Lethbridge, AB T1K 3M4, Canada
| | - Engin Özkan
- Department of Biochemistry and Molecular Biology, University of Chicago, Chicago, IL 60637, USA
| | - Gaetano T Montelione
- Department of Molecular Biology and Biochemistry and Center for Advanced Biotechnology and Medicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA; Department of Biochemistry and Molecular Biology, Robert Wood Johnson Medical School, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Davide Comoletti
- Child Health Institute of New Jersey, New Brunswick, NJ 08901, USA; Departments of Neuroscience and Cell Biology Robert Wood Johnson Medical School, Rutgers, The State University of New Jersey, New Brunswick, NJ 08901, USA; Department of Pediatrics, Robert Wood Johnson Medical School, Rutgers, The State University of New Jersey, New Brunswick, NJ 08901, USA; School of Biological Sciences, Victoria University of Wellington, Wellington 6140, New Zealand.
| |
Collapse
|
44
|
Rosario-Cruz Z, Eletsky A, Daigham NS, Al-Tameemi H, Swapna GVT, Kahn PC, Szyperski T, Montelione GT, Boyd JM. The copBL operon protects Staphylococcus aureus from copper toxicity: CopL is an extracellular membrane-associated copper-binding protein. J Biol Chem 2019; 294:4027-4044. [PMID: 30655293 PMCID: PMC6422080 DOI: 10.1074/jbc.ra118.004723] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2018] [Revised: 01/08/2019] [Indexed: 12/22/2022] Open
Abstract
As complications associated with antibiotic resistance have intensified, copper (Cu) is attracting attention as an antimicrobial agent. Recent studies have shown that copper surfaces decrease microbial burden, and host macrophages use Cu to increase bacterial killing. Not surprisingly, microbes have evolved mechanisms to tightly control intracellular Cu pools and protect against Cu toxicity. Here, we identified two genes (copB and copL) encoded within the Staphylococcus aureus arginine-catabolic mobile element (ACME) that we hypothesized function in Cu homeostasis. Supporting this hypothesis, mutational inactivation of copB or copL increased copper sensitivity. We found that copBL are co-transcribed and that their transcription is increased during copper stress and in a strain in which csoR, encoding a Cu-responsive transcriptional repressor, was mutated. Moreover, copB displayed genetic synergy with copA, suggesting that CopB functions in Cu export. We further observed that CopL functions independently of CopB or CopA in Cu toxicity protection and that CopL from the S. aureus clone USA300 is a membrane-bound and surface-exposed lipoprotein that binds up to four Cu+ ions. Solution NMR structures of the homologous Bacillus subtilis CopL, together with phylogenetic analysis and chemical-shift perturbation experiments, identified conserved residues potentially involved in Cu+ coordination. The solution NMR structure also revealed a novel Cu-binding architecture. Of note, a CopL variant with defective Cu+ binding did not protect against Cu toxicity in vivo Taken together, these findings indicate that the ACME-encoded CopB and CopL proteins are additional factors utilized by the highly successful S. aureus USA300 clone to suppress copper toxicity.
Collapse
Affiliation(s)
- Zuelay Rosario-Cruz
- From the Department of Biochemistry and Microbiology, Rutgers, the State University of New Jersey, New Brunswick, New Jersey 08901
| | - Alexander Eletsky
- the Department of Chemistry, State University of New York at Buffalo and Northeast Structural Genomics Consortium, Buffalo, New York 14260, and
| | - Nourhan S Daigham
- the Department of Molecular Biology and Biochemistry, Center for Advanced Biotechnology and Medicine, and Northeast Structural Genomics Consortium, Rutgers, the State University of New Jersey, Piscataway, New Jersey 08854
| | - Hassan Al-Tameemi
- From the Department of Biochemistry and Microbiology, Rutgers, the State University of New Jersey, New Brunswick, New Jersey 08901
| | - G V T Swapna
- the Department of Molecular Biology and Biochemistry, Center for Advanced Biotechnology and Medicine, and Northeast Structural Genomics Consortium, Rutgers, the State University of New Jersey, Piscataway, New Jersey 08854
| | - Peter C Kahn
- From the Department of Biochemistry and Microbiology, Rutgers, the State University of New Jersey, New Brunswick, New Jersey 08901
| | - Thomas Szyperski
- the Department of Chemistry, State University of New York at Buffalo and Northeast Structural Genomics Consortium, Buffalo, New York 14260, and
| | - Gaetano T Montelione
- the Department of Molecular Biology and Biochemistry, Center for Advanced Biotechnology and Medicine, and Northeast Structural Genomics Consortium, Rutgers, the State University of New Jersey, Piscataway, New Jersey 08854,
- the Department of Biochemistry and Molecular Biology, Robert Wood Johnson Medical School, Rutgers, the State University of New Jersey, Piscataway, New Jersey 08854
| | - Jeffrey M Boyd
- From the Department of Biochemistry and Microbiology, Rutgers, the State University of New Jersey, New Brunswick, New Jersey 08901,
| |
Collapse
|
45
|
Ozgul S, von Daake S, Kakehi S, Sereni D, Denissova N, Hanlon C, Huang YJ, Everett JK, Yin C, Montelione GT, Comoletti D. An ELISA-Based Screening Platform for Ligand–Receptor Discovery. Methods Enzymol 2019; 615:453-475. [DOI: 10.1016/bs.mie.2018.10.001] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023]
|
46
|
Huang YJ, Brock KP, Ishida Y, Swapna GVT, Inouye M, Marks DS, Sander C, Montelione GT. Combining Evolutionary Covariance and NMR Data for Protein Structure Determination. Methods Enzymol 2018; 614:363-392. [PMID: 30611430 PMCID: PMC6640129 DOI: 10.1016/bs.mie.2018.11.004] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Accurate protein structure determination by solution-state NMR is challenging for proteins greater than about 20kDa, for which extensive perdeuteration is generally required, providing experimental data that are incomplete (sparse) and ambiguous. However, the massive increase in evolutionary sequence information coupled with advances in methods for sequence covariance analysis can provide reliable residue-residue contact information for a protein from sequence data alone. These "evolutionary couplings (ECs)" can be combined with sparse NMR data to determine accurate 3D protein structures. This hybrid "EC-NMR" method has been developed using NMR data for several soluble proteins and validated by comparison with corresponding reference structures determined by X-ray crystallography and/or conventional NMR methods. For small proteins, only backbone resonance assignments are utilized, while for larger proteins both backbone and some sidechain methyl resonance assignments are generally required. ECs can be combined with sparse NMR data obtained on deuterated, selectively protonated protein samples to provide structures that are more accurate and complete than those obtained using such sparse NMR data alone. EC-NMR also has significant potential for analysis of protein structures from solid-state NMR data and for studies of integral membrane proteins. The requirement that ECs are consistent with NMR data recorded on a specific member of a protein family, under specific conditions, also allows identification of ECs that reflect alternative allosteric or excited states of the protein structure.
Collapse
Affiliation(s)
- Yuanpeng Janet Huang
- Center for Advanced Biotechnology and Medicine, Rutgers, The State University of New Jersey, Piscataway, NJ, United States; Department of Molecular Biology and Biochemistry, Rutgers, The State University of New Jersey, Piscataway, NJ, United States
| | - Kelly P Brock
- Department of Systems Biology, Harvard Medical School, Boston, MA, United States
| | - Yojiro Ishida
- Center for Advanced Biotechnology and Medicine, Rutgers, The State University of New Jersey, Piscataway, NJ, United States; Department of Biochemistry and Molecular Biology, Robert Wood Johnson Medical School, Rutgers, The State University of New Jersey, Piscataway, NJ, United States
| | - Gurla V T Swapna
- Center for Advanced Biotechnology and Medicine, Rutgers, The State University of New Jersey, Piscataway, NJ, United States; Department of Molecular Biology and Biochemistry, Rutgers, The State University of New Jersey, Piscataway, NJ, United States
| | - Masayori Inouye
- Center for Advanced Biotechnology and Medicine, Rutgers, The State University of New Jersey, Piscataway, NJ, United States; Department of Biochemistry and Molecular Biology, Robert Wood Johnson Medical School, Rutgers, The State University of New Jersey, Piscataway, NJ, United States
| | - Debora S Marks
- Department of Systems Biology, Harvard Medical School, Boston, MA, United States
| | - Chris Sander
- Department of Cell Biology, Harvard Medical School and cBio Center, Dana-Farber Cancer Institute, Boston, MA, United States
| | - Gaetano T Montelione
- Center for Advanced Biotechnology and Medicine, Rutgers, The State University of New Jersey, Piscataway, NJ, United States; Department of Molecular Biology and Biochemistry, Rutgers, The State University of New Jersey, Piscataway, NJ, United States; Department of Biochemistry and Molecular Biology, Robert Wood Johnson Medical School, Rutgers, The State University of New Jersey, Piscataway, NJ, United States.
| |
Collapse
|
47
|
Song F, Li M, Liu G, Swapna GVT, Daigham NS, Xia B, Montelione GT, Bunting SF. Antiparallel Coiled-Coil Interactions Mediate the Homodimerization of the DNA Damage-Repair Protein PALB2. Biochemistry 2018; 57:6581-6591. [PMID: 30289697 DOI: 10.1021/acs.biochem.8b00789] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Abstract
Deficits in DNA damage-repair pathways are the root cause of several human cancers. In mammalian cells, DNA double-strand break repair is carried out by multiple mechanisms, including homologous recombination (HR). The partner and localizer of BRCA2 (PALB2), which is an essential factor for HR, binds to the breast cancer susceptibility 1 (BRCA1) protein at DNA double-strand breaks. At the break site, PALB2 also associates with the breast cancer susceptibility 2 (BRCA2) protein to form a multiprotein complex that facilitates HR. The BRCA1-PALB2 interaction is mediated by association of predicted helical coiled-coil regions in both proteins. PALB2 can also homodimerize through the formation of a coiled coil by the self-association of helical elements at the N-terminus of the PALB2 protein, and this homodimerization has been proposed to regulate the efficiency of HR. We have produced a segment of PALB2, designated PALB2cc (PALB2 coiled coil segment) that forms α-helical structures, which assemble into stable homodimers. PALB2cc also forms heterodimers with a helical segment of BRCA1, called BRCA1cc (BRCA1 coiled coil segment). The three-dimensional structure of the homodimer formed by PALB2cc was determined by solution NMR spectroscopy. This PALB2cc homodimer is a classical antiparallel coiled-coil leucine zipper. NMR chemical-shift perturbation studies were used to study dimer formation for both the PALB2cc homodimer and the PALB2cc/BRCA1cc heterodimer. The mutation of residue Leu24 of PALB2cc significantly reduces its homodimer stability, but has a more modest effect on the stability of the heterodimer formed between PALB2cc and BRCA1cc. We show that mutation of Leu24 leads to genomic instability and reduced cell viability after treatment with agents that induce DNA double-strand breaks. These studies may allow the identification of distinct mutations of PALB2cc that selectively disrupt homodimeric versus heterodimeric interactions, and reveal the specific role of PALB2cc homodimerization in HR.
Collapse
Affiliation(s)
| | | | | | | | | | - Bing Xia
- Department of Radiation Oncology, Rutgers Cancer Institute of New Jersey, Rutgers , The State University of New Jersey , New Brunswick , New Jersey 08901 , United States
| | | | | |
Collapse
|
48
|
Kim JD, Pike DH, Tyryshkin AM, Swapna GVT, Raanan H, Montelione GT, Nanda V, Falkowski PG. Minimal Heterochiral de Novo Designed 4Fe-4S Binding Peptide Capable of Robust Electron Transfer. J Am Chem Soc 2018; 140:11210-11213. [PMID: 30141918 DOI: 10.1021/jacs.8b07553] [Citation(s) in RCA: 31] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Ambidoxin is a designed, minimal dodecapeptide consisting of alternating L and D amino acids that binds a 4Fe-4S cluster through ligand-metal interactions and an extensive network of second-shell hydrogen bonds. The peptide can withstand hundreds of oxidation-reduction cycles at room temperature. Ambidoxin suggests how simple, prebiotic peptides may have achieved robust redox catalysis on the early Earth.
Collapse
Affiliation(s)
- J Dongun Kim
- Environmental Biophysics and Molecular Ecology Program, Department of Marine and Coastal Sciences, Rutgers , the State University of New Jersey , New Brunswick , New Jersey 08901 , United States
| | - Douglas H Pike
- Center for Advanced Biotechnology and Medicine , Rutgers, the State University of New Jersey , Piscataway , New Jersey 08854 , United States
| | - Alexei M Tyryshkin
- Environmental Biophysics and Molecular Ecology Program, Department of Marine and Coastal Sciences, Rutgers , the State University of New Jersey , New Brunswick , New Jersey 08901 , United States
| | - G V T Swapna
- Center for Advanced Biotechnology and Medicine , Rutgers, the State University of New Jersey , Piscataway , New Jersey 08854 , United States.,Department of Molecular Biology and Biochemistry, Rutgers , the State University of New Jersey , Piscataway , New Jersey 08854 , United States
| | - Hagai Raanan
- Environmental Biophysics and Molecular Ecology Program, Department of Marine and Coastal Sciences, Rutgers , the State University of New Jersey , New Brunswick , New Jersey 08901 , United States
| | - Gaetano T Montelione
- Center for Advanced Biotechnology and Medicine , Rutgers, the State University of New Jersey , Piscataway , New Jersey 08854 , United States.,Department of Molecular Biology and Biochemistry, Rutgers , the State University of New Jersey , Piscataway , New Jersey 08854 , United States.,Department of Biochemistry and Molecular Biology , Robert Wood Johnson Medical School, Rutgers, the State University of New Jersey , Piscataway , New Jersey 08854 , United States
| | - Vikas Nanda
- Center for Advanced Biotechnology and Medicine , Rutgers, the State University of New Jersey , Piscataway , New Jersey 08854 , United States.,Department of Biochemistry and Molecular Biology , Robert Wood Johnson Medical School, Rutgers, the State University of New Jersey , Piscataway , New Jersey 08854 , United States
| | - Paul G Falkowski
- Environmental Biophysics and Molecular Ecology Program, Department of Marine and Coastal Sciences, Rutgers , the State University of New Jersey , New Brunswick , New Jersey 08901 , United States.,Department of Earth and Planetary Sciences, Rutgers , the State University of New Jersey , Piscataway , New Jersey 08854 , United States
| |
Collapse
|
49
|
Nie Y, Wang S, Xu Y, Luo S, Zhao YL, Xiao R, Montelione GT, Hunt JF, Szyperski T. Enzyme Engineering Based on X-ray Structures and Kinetic Profiling of Substrate Libraries: Alcohol Dehydrogenases for Stereospecific Synthesis of a Broad Range of Chiral Alcohols. ACS Catal 2018. [DOI: 10.1021/acscatal.8b00364] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
Affiliation(s)
- Yao Nie
- School of Biotechnology, Key laboratory of Industrial Biotechnology, Ministry of Education, Jiangnan University, Wuxi 214122, People’s Republic of China
| | - Shanshan Wang
- School of Biotechnology, Key laboratory of Industrial Biotechnology, Ministry of Education, Jiangnan University, Wuxi 214122, People’s Republic of China
- School of Biological Science and Engineering, Shannxi University of Technology, Hanzhong 723001, People’s Republic of China
| | - Yan Xu
- School of Biotechnology, Key laboratory of Industrial Biotechnology, Ministry of Education, Jiangnan University, Wuxi 214122, People’s Republic of China
- State Key Laboratory of Food Science and Technology, Jiangnan University, Wuxi 214122, People’s Republic of China
| | - Shenggan Luo
- State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic and Developmental Sciences, MOE-LSB & MOE-LSC, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, People’s Republic of China
| | - Yi-Lei Zhao
- State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic and Developmental Sciences, MOE-LSB & MOE-LSC, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, People’s Republic of China
| | - Rong Xiao
- Center for Advanced Biotechnology and Medicine, Department of Molecular Biology and Biochemistry, Rutgers, The State University of New Jersey, Piscataway, New Jersey 08854, United States
| | - Gaetano T. Montelione
- Center for Advanced Biotechnology and Medicine, Department of Molecular Biology and Biochemistry, Rutgers, The State University of New Jersey, Piscataway, New Jersey 08854, United States
| | - John F. Hunt
- Department of Biological Sciences, Columbia University, New York, New York 10027, United States
| | - Thomas Szyperski
- Department of Chemistry, The State University of New York at Buffalo, Buffalo, New York 14260, United States
| |
Collapse
|
50
|
Zhang M, Yu XW, Swapna GVT, Liu G, Xiao R, Xu Y, Montelione GT. Backbone and Ile-δ1, Leu, Val methyl 1H, 15N, and 13C, chemical shift assignments for Rhizopus chinensis lipase. Biomol NMR Assign 2018; 12:63-68. [PMID: 28929427 DOI: 10.1007/s12104-017-9781-4] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/16/2017] [Accepted: 09/14/2017] [Indexed: 06/07/2023]
Abstract
Lipase r27RCL is a 296-residue, 33 kDa monomeric enzyme with high ester hydrolysis activity, which has significant applications in the baking, paper and leather industries. The lipase gene proRCL from Rhizopus microsporus var. chinensis (also Rhizopus chinensis) CCTCC M201021 was cloned as a fusion construct C-terminal to a maltose-binding protein (MBP) tag, and expressed as MBP-proRCL in an Escherichia coli BL21 trxB (DE3) expression system with uniform 2H,13C,15N-enrichment and Ile-δ1, Leu, and Val 13CH3 methyl labeling. The fusion protein was hydrolyzed by Kex2 protease at the recognition site Lys-Arg between residues -29 and -28 of the prosequence, producing the enzyme form called r27RCL. Here we report extensive backbone 1H, 15N, and 13C, as well as Ile-δ1, Leu, and Val side chain methyl, NMR resonance assignments for r27RCL.
Collapse
Affiliation(s)
- Meng Zhang
- The Key Laboratory of Industrial Biotechnology, Ministry of Education, School of Biotechnology, Jiangnan University, 1800 Lihu Avenue, Wuxi, 214122, Jiangsu, China
| | - Xiao-Wei Yu
- The Key Laboratory of Industrial Biotechnology, Ministry of Education, School of Biotechnology, Jiangnan University, 1800 Lihu Avenue, Wuxi, 214122, Jiangsu, China.
- State Key Laboratory of Food Science and Technology, Jiangnan University, 1800 Lihu Avenue, Wuxi, 214122, Jiangsu, China.
| | - G V T Swapna
- Center for Advanced Biotechnology and Medicine, Department of Molecular Biology and Biochemistry, Rutgers, The State University of New Jersey, Piscataway, NJ, USA
- Department of Biochemistry and Molecular Biology, Robert Wood Johnson Medical School, Rutgers, The State University of New Jersey, Piscataway, NJ, 08854, USA
- Northeast Structural Genomics Consortium, Piscataway, NJ, USA
| | - Gaohua Liu
- Center for Advanced Biotechnology and Medicine, Department of Molecular Biology and Biochemistry, Rutgers, The State University of New Jersey, Piscataway, NJ, USA
- Department of Biochemistry and Molecular Biology, Robert Wood Johnson Medical School, Rutgers, The State University of New Jersey, Piscataway, NJ, 08854, USA
- Northeast Structural Genomics Consortium, Piscataway, NJ, USA
| | - Rong Xiao
- Center for Advanced Biotechnology and Medicine, Department of Molecular Biology and Biochemistry, Rutgers, The State University of New Jersey, Piscataway, NJ, USA
- Department of Biochemistry and Molecular Biology, Robert Wood Johnson Medical School, Rutgers, The State University of New Jersey, Piscataway, NJ, 08854, USA
- Northeast Structural Genomics Consortium, Piscataway, NJ, USA
| | - Yan Xu
- The Key Laboratory of Industrial Biotechnology, Ministry of Education, School of Biotechnology, Jiangnan University, 1800 Lihu Avenue, Wuxi, 214122, Jiangsu, China.
- State Key Laboratory of Food Science and Technology, Jiangnan University, 1800 Lihu Avenue, Wuxi, 214122, Jiangsu, China.
| | - Gaetano T Montelione
- Center for Advanced Biotechnology and Medicine, Department of Molecular Biology and Biochemistry, Rutgers, The State University of New Jersey, Piscataway, NJ, USA.
- Department of Biochemistry and Molecular Biology, Robert Wood Johnson Medical School, Rutgers, The State University of New Jersey, Piscataway, NJ, 08854, USA.
- Northeast Structural Genomics Consortium, Piscataway, NJ, USA.
| |
Collapse
|