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Koehler Leman J, Künze G. Recent Advances in NMR Protein Structure Prediction with ROSETTA. Int J Mol Sci 2023; 24:ijms24097835. [PMID: 37175539 PMCID: PMC10178863 DOI: 10.3390/ijms24097835] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2023] [Revised: 04/15/2023] [Accepted: 04/21/2023] [Indexed: 05/15/2023] Open
Abstract
Nuclear magnetic resonance (NMR) spectroscopy is a powerful method for studying the structure and dynamics of proteins in their native state. For high-resolution NMR structure determination, the collection of a rich restraint dataset is necessary. This can be difficult to achieve for proteins with high molecular weight or a complex architecture. Computational modeling techniques can complement sparse NMR datasets (<1 restraint per residue) with additional structural information to elucidate protein structures in these difficult cases. The Rosetta software for protein structure modeling and design is used by structural biologists for structure determination tasks in which limited experimental data is available. This review gives an overview of the computational protocols available in the Rosetta framework for modeling protein structures from NMR data. We explain the computational algorithms used for the integration of different NMR data types in Rosetta. We also highlight new developments, including modeling tools for data from paramagnetic NMR and hydrogen-deuterium exchange, as well as chemical shifts in CS-Rosetta. Furthermore, strategies are discussed to complement and improve structure predictions made by the current state-of-the-art AlphaFold2 program using NMR-guided Rosetta modeling.
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Affiliation(s)
- Julia Koehler Leman
- Center for Computational Biology, Flatiron Institute, Simons Foundation, New York, NY 10010, USA
| | - Georg Künze
- Institute for Drug Discovery, Medical Faculty, University of Leipzig, Brüderstr. 34, D-04103 Leipzig, Germany
- Interdisciplinary Center for Bioinformatics, University of Leipzig, Härtelstr. 16-18, D-04107 Leipzig, Germany
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2
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Colautti J, Bullen NP, Whitney JC. Lack of evidence that Pseudomonas aeruginosa AmpDh3-PA0808 constitute a type VI secretion system effector-immunity pair. Mol Microbiol 2023; 119:262-274. [PMID: 36577706 DOI: 10.1111/mmi.15021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2022] [Revised: 12/19/2022] [Accepted: 12/20/2022] [Indexed: 12/29/2022]
Abstract
Type VI secretion systems (T6SSs) are cell envelope-spanning protein complexes that Gram-negative bacteria use to inject a diverse arsenal of antibacterial toxins into competitor cells. Recently, Wang et al. reported that the H2-T6SS of Pseudomonas aeruginosa delivers the peptidoglycan recycling amidase, AmpDh3, into the periplasm of recipient cells where it is proposed to act as a peptidoglycan degrading toxin. They further reported that PA0808, the open reading frame downstream of AmpDh3, encodes an immunity protein that localizes to the periplasm where it binds to and inactivates intercellularly delivered AmpDh3, thus protecting against its toxic activity. Given that AmpDh3 has an established role in cell wall homeostasis and that no precedent exists for cytosolic enzymes moonlighting as T6SS effectors, we attempted to replicate these findings. We found that cells lacking PA0808 are not susceptible to bacterial killing by AmpDh3 and that PA0808 and AmpDh3 do not physically interact in vitro or in vivo. Additionally, we found no evidence that AmpDh3 is exported from cells, including by strains with a constitutively active H2-T6SS. Finally, subcellular fractionation experiments and a 1.97 Å crystal structure reveal that PA0808 does not contain a canonical signal peptide or localize to the correct cellular compartment to confer protection against a cell wall targeting toxin. Taken together, these results cast doubt on the assertion that AmpDh3-PA0808 constitutes an H2-T6SS effector-immunity pair.
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Affiliation(s)
- Jake Colautti
- Michael DeGroote Institute for Infectious Disease Research, McMaster University, Hamilton, Ontario, Canada.,Department of Biochemistry and Biomedical Sciences, McMaster University, Hamilton, Ontario, Canada
| | - Nathan P Bullen
- Michael DeGroote Institute for Infectious Disease Research, McMaster University, Hamilton, Ontario, Canada.,Department of Biochemistry and Biomedical Sciences, McMaster University, Hamilton, Ontario, Canada
| | - John C Whitney
- Michael DeGroote Institute for Infectious Disease Research, McMaster University, Hamilton, Ontario, Canada.,Department of Biochemistry and Biomedical Sciences, McMaster University, Hamilton, Ontario, Canada.,David Braley Center for Antibiotic Discovery, McMaster University, Hamilton, Ontario, Canada
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3
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Trindade IB, Hernandez G, Lebègue E, Barrière F, Cordeiro T, Piccioli M, Louro RO. Conjuring up a ghost: structural and functional characterization of FhuF, a ferric siderophore reductase from E. coli. J Biol Inorg Chem 2021; 26:313-326. [PMID: 33559753 PMCID: PMC8068687 DOI: 10.1007/s00775-021-01854-y] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2020] [Accepted: 01/23/2021] [Indexed: 10/27/2022]
Abstract
Iron is a fundamental element for virtually all forms of life. Despite its abundance, its bioavailability is limited, and thus, microbes developed siderophores, small molecules, which are synthesized inside the cell and then released outside for iron scavenging. Once inside the cell, iron removal does not occur spontaneously, instead this process is mediated by siderophore-interacting proteins (SIP) and/or by ferric-siderophore reductases (FSR). In the past two decades, representatives of the SIP subfamily have been structurally and biochemically characterized; however, the same was not achieved for the FSR subfamily. Here, we initiate the structural and functional characterization of FhuF, the first and only FSR ever isolated. FhuF is a globular monomeric protein mainly composed by α-helices sheltering internal cavities in a fold resembling the "palm" domain found in siderophore biosynthetic enzymes. Paramagnetic NMR spectroscopy revealed that the core of the cluster has electronic properties in line with those of previously characterized 2Fe-2S ferredoxins and differences appear to be confined to the coordination of Fe(III) in the reduced protein. In particular, the two cysteines coordinating this iron appear to have substantially different bond strengths. In similarity with the proteins from the SIP subfamily, FhuF binds both the iron-loaded and the apo forms of ferrichrome in the micromolar range and cyclic voltammetry reveals the presence of redox-Bohr effect, which broadens the range of ferric-siderophore substrates that can be thermodynamically accessible for reduction. This study suggests that despite the structural differences between FSR and SIP proteins, mechanistic similarities exist between the two classes of proteins.
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Affiliation(s)
- I B Trindade
- Instituto de Tecnologia Química e Biológica António Xavier (ITQB‑NOVA), Universidade Nova de Lisboa, Av. da República (EAN), 2780‑157, Oeiras, Portugal
| | - G Hernandez
- Instituto de Tecnologia Química e Biológica António Xavier (ITQB‑NOVA), Universidade Nova de Lisboa, Av. da República (EAN), 2780‑157, Oeiras, Portugal
| | - E Lebègue
- Université de Nantes, CNRS, CEISAM UMR 6230, 44000, Nantes, France
| | - F Barrière
- Institut des Sciences Chimiques de Rennes-UMR 6226, Université Rennes, CNRS, 35000, Rennes, France
| | - T Cordeiro
- Instituto de Tecnologia Química e Biológica António Xavier (ITQB‑NOVA), Universidade Nova de Lisboa, Av. da República (EAN), 2780‑157, Oeiras, Portugal
| | - M Piccioli
- Department of Chemistry, Magnetic Resonance Center (CERM), University of Florence, Via L. Sacconi 6, 50019, Sesto Fiorentino, Italy
- Consorzio Interuniversitario Risonanze Magnetiche di Metallo Proteine (CIRMMP), Via L. Sacconi 6, 50019, Sesto Fiorentino, Italy
| | - R O Louro
- Instituto de Tecnologia Química e Biológica António Xavier (ITQB‑NOVA), Universidade Nova de Lisboa, Av. da República (EAN), 2780‑157, Oeiras, Portugal.
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4
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Leman JK, Weitzner BD, Lewis SM, Adolf-Bryfogle J, Alam N, Alford RF, Aprahamian M, Baker D, Barlow KA, Barth P, Basanta B, Bender BJ, Blacklock K, Bonet J, Boyken SE, Bradley P, Bystroff C, Conway P, Cooper S, Correia BE, Coventry B, Das R, De Jong RM, DiMaio F, Dsilva L, Dunbrack R, Ford AS, Frenz B, Fu DY, Geniesse C, Goldschmidt L, Gowthaman R, Gray JJ, Gront D, Guffy S, Horowitz S, Huang PS, Huber T, Jacobs TM, Jeliazkov JR, Johnson DK, Kappel K, Karanicolas J, Khakzad H, Khar KR, Khare SD, Khatib F, Khramushin A, King IC, Kleffner R, Koepnick B, Kortemme T, Kuenze G, Kuhlman B, Kuroda D, Labonte JW, Lai JK, Lapidoth G, Leaver-Fay A, Lindert S, Linsky T, London N, Lubin JH, Lyskov S, Maguire J, Malmström L, Marcos E, Marcu O, Marze NA, Meiler J, Moretti R, Mulligan VK, Nerli S, Norn C, Ó'Conchúir S, Ollikainen N, Ovchinnikov S, Pacella MS, Pan X, Park H, Pavlovicz RE, Pethe M, Pierce BG, Pilla KB, Raveh B, Renfrew PD, Burman SSR, Rubenstein A, Sauer MF, Scheck A, Schief W, Schueler-Furman O, Sedan Y, Sevy AM, Sgourakis NG, Shi L, Siegel JB, Silva DA, Smith S, Song Y, Stein A, Szegedy M, Teets FD, Thyme SB, Wang RYR, Watkins A, Zimmerman L, Bonneau R. Macromolecular modeling and design in Rosetta: recent methods and frameworks. Nat Methods 2020; 17:665-680. [PMID: 32483333 PMCID: PMC7603796 DOI: 10.1038/s41592-020-0848-2] [Citation(s) in RCA: 413] [Impact Index Per Article: 103.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2019] [Accepted: 04/22/2020] [Indexed: 12/12/2022]
Abstract
The Rosetta software for macromolecular modeling, docking and design is extensively used in laboratories worldwide. During two decades of development by a community of laboratories at more than 60 institutions, Rosetta has been continuously refactored and extended. Its advantages are its performance and interoperability between broad modeling capabilities. Here we review tools developed in the last 5 years, including over 80 methods. We discuss improvements to the score function, user interfaces and usability. Rosetta is available at http://www.rosettacommons.org.
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Affiliation(s)
- Julia Koehler Leman
- Center for Computational Biology, Flatiron Institute, Simons Foundation, New York, NY, USA.
- Department of Biology, New York University, New York, New York, USA.
| | - Brian D Weitzner
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, USA
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
- Lyell Immunopharma Inc., Seattle, WA, USA
| | - Steven M Lewis
- Department of Biochemistry and Biophysics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Department of Biochemistry, Duke University, Durham, NC, USA
- Cyrus Biotechnology, Seattle, WA, USA
| | - Jared Adolf-Bryfogle
- Department of Immunology and Microbiology, The Scripps Research Institute, La Jolla, CA, USA
| | - Nawsad Alam
- Department of Microbiology and Molecular Genetics, IMRIC, Ein Kerem Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel
| | - Rebecca F Alford
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Melanie Aprahamian
- Department of Chemistry and Biochemistry, Ohio State University, Columbus, OH, USA
| | - David Baker
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Kyle A Barlow
- Graduate Program in Bioinformatics, University of California San Francisco, San Francisco, CA, USA
| | - Patrick Barth
- Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
- Baylor College of Medicine, Department of Pharmacology, Houston, TX, USA
| | - Benjamin Basanta
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Biological Physics Structure and Design PhD Program, University of Washington, Seattle, WA, USA
| | - Brian J Bender
- Department of Pharmacology, Vanderbilt University, Nashville, TN, USA
| | - Kristin Blacklock
- Institute of Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ, USA
| | - Jaume Bonet
- Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Scott E Boyken
- Institute for Protein Design, University of Washington, Seattle, WA, USA
- Lyell Immunopharma Inc., Seattle, WA, USA
| | - Phil Bradley
- Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Chris Bystroff
- Department of Biological Sciences, Rensselaer Polytechnic Institute, Troy, NY, USA
| | - Patrick Conway
- Department of Biochemistry, University of Washington, Seattle, WA, USA
| | - Seth Cooper
- Khoury College of Computer Sciences, Northeastern University, Boston, MA, USA
| | - Bruno E Correia
- Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Brian Coventry
- Department of Biochemistry, University of Washington, Seattle, WA, USA
| | - Rhiju Das
- Department of Biochemistry, Stanford University School of Medicine, Stanford, CA, USA
| | | | - Frank DiMaio
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Lorna Dsilva
- Khoury College of Computer Sciences, Northeastern University, Boston, MA, USA
| | - Roland Dunbrack
- Institute for Cancer Research, Fox Chase Cancer Center, Philadelphia, PA, USA
| | - Alexander S Ford
- Department of Biochemistry, University of Washington, Seattle, WA, USA
| | - Brandon Frenz
- Institute for Protein Design, University of Washington, Seattle, WA, USA
- Cyrus Biotechnology, Seattle, WA, USA
| | - Darwin Y Fu
- Department of Chemistry, Vanderbilt University, Nashville, TN, USA
| | - Caleb Geniesse
- Department of Biochemistry, Stanford University School of Medicine, Stanford, CA, USA
| | | | - Ragul Gowthaman
- University of Maryland Institute for Bioscience and Biotechnology Research, Rockville, MD, USA
- Department of Cell Biology and Molecular Genetics, University of Maryland, College Park, MD, USA
| | - Jeffrey J Gray
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, USA
- Program in Molecular Biophysics, Johns Hopkins University, Baltimore, MD, USA
| | - Dominik Gront
- Faculty of Chemistry, Biological and Chemical Research Centre, University of Warsaw, Warsaw, Poland
| | - Sharon Guffy
- Department of Biochemistry and Biophysics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Scott Horowitz
- Department of Chemistry & Biochemistry, University of Denver, Denver, CO, USA
- The Knoebel Institute for Healthy Aging, University of Denver, Denver, CO, USA
| | - Po-Ssu Huang
- Department of Biochemistry, University of Washington, Seattle, WA, USA
| | - Thomas Huber
- Research School of Chemistry, Australian National University, Canberra, Australian Capital Territory, Australia
| | - Tim M Jacobs
- Program in Bioinformatics and Computational Biology, Department of Biochemistry and Biophysics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | | | - David K Johnson
- Center for Computational Biology, University of Kansas, Lawrence, KS, USA
| | - Kalli Kappel
- Biophysics Program, Stanford University, Stanford, CA, USA
| | - John Karanicolas
- Institute for Cancer Research, Fox Chase Cancer Center, Philadelphia, PA, USA
| | - Hamed Khakzad
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
- Institute for Computational Science, University of Zurich, Zurich, Switzerland
- S3IT, University of Zurich, Zurich, Switzerland
| | - Karen R Khar
- Cyrus Biotechnology, Seattle, WA, USA
- Center for Computational Biology, University of Kansas, Lawrence, KS, USA
| | - Sagar D Khare
- Institute of Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ, USA
- Department of Chemistry and Chemical Biology, Rutgers, The State University of New Jersey, Piscataway, NJ, USA
- Department of Chemistry and Chemical Biology, The State University of New Jersey, Piscataway, NJ, USA
- Center for Integrative Proteomics Research, Rutgers, The State University of New Jersey, Piscataway, NJ, USA
- Computational Biology and Molecular Biophysics Program, Rutgers, The State University of New Jersey, Piscataway, NJ, USA
| | - Firas Khatib
- Department of Computer and Information Science, University of Massachusetts Dartmouth, Dartmouth, MA, USA
| | - Alisa Khramushin
- Department of Microbiology and Molecular Genetics, IMRIC, Ein Kerem Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel
| | - Indigo C King
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Cyrus Biotechnology, Seattle, WA, USA
| | - Robert Kleffner
- Khoury College of Computer Sciences, Northeastern University, Boston, MA, USA
| | - Brian Koepnick
- Department of Biochemistry, University of Washington, Seattle, WA, USA
| | - Tanja Kortemme
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, USA
| | - Georg Kuenze
- Department of Chemistry, Vanderbilt University, Nashville, TN, USA
- Center for Structural Biology, Vanderbilt University, Nashville, TN, USA
| | - Brian Kuhlman
- Department of Biochemistry and Biophysics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Daisuke Kuroda
- Medical Device Development and Regulation Research Center, School of Engineering, University of Tokyo, Tokyo, Japan
- Department of Bioengineering, School of Engineering, University of Tokyo, Tokyo, Japan
| | - Jason W Labonte
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, USA
- Department of Chemistry, Franklin & Marshall College, Lancaster, PA, USA
| | - Jason K Lai
- Baylor College of Medicine, Department of Pharmacology, Houston, TX, USA
| | - Gideon Lapidoth
- Department of Biomolecular Sciences, Weizmann Institute of Science, Rehovot, Israel
| | - Andrew Leaver-Fay
- Department of Biochemistry and Biophysics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Steffen Lindert
- Department of Chemistry and Biochemistry, Ohio State University, Columbus, OH, USA
| | - Thomas Linsky
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Nir London
- Department of Microbiology and Molecular Genetics, IMRIC, Ein Kerem Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel
| | - Joseph H Lubin
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Sergey Lyskov
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Jack Maguire
- Program in Bioinformatics and Computational Biology, Department of Biochemistry and Biophysics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Lars Malmström
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
- Institute for Computational Science, University of Zurich, Zurich, Switzerland
- S3IT, University of Zurich, Zurich, Switzerland
- Division of Infection Medicine, Department of Clinical Sciences Lund, Faculty of Medicine, Lund University, Lund, Sweden
| | - Enrique Marcos
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Research in Biomedicine Barcelona, The Barcelona Institute of Science and Technology, Barcelona, Spain
| | - Orly Marcu
- Department of Microbiology and Molecular Genetics, IMRIC, Ein Kerem Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel
| | - Nicholas A Marze
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Jens Meiler
- Center for Structural Biology, Vanderbilt University, Nashville, TN, USA
- Departments of Chemistry, Pharmacology and Biomedical Informatics, Vanderbilt University, Nashville, TN, USA
- Institute for Chemical Biology, Vanderbilt University, Nashville, TN, USA
| | - Rocco Moretti
- Department of Chemistry, Vanderbilt University, Nashville, TN, USA
| | - Vikram Khipple Mulligan
- Center for Computational Biology, Flatiron Institute, Simons Foundation, New York, NY, USA
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Santrupti Nerli
- Department of Computer Science, University of California Santa Cruz, Santa Cruz, CA, USA
| | - Christoffer Norn
- Department of Biomolecular Sciences, Weizmann Institute of Science, Rehovot, Israel
| | - Shane Ó'Conchúir
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, USA
| | - Noah Ollikainen
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, USA
| | - Sergey Ovchinnikov
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
- Molecular and Cellular Biology Program, University of Washington, Seattle, WA, USA
| | - Michael S Pacella
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Xingjie Pan
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, USA
| | - Hahnbeom Park
- Department of Biochemistry, University of Washington, Seattle, WA, USA
| | - Ryan E Pavlovicz
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
- Cyrus Biotechnology, Seattle, WA, USA
| | - Manasi Pethe
- Department of Chemistry and Chemical Biology, The State University of New Jersey, Piscataway, NJ, USA
- Center for Integrative Proteomics Research, Rutgers, The State University of New Jersey, Piscataway, NJ, USA
| | - Brian G Pierce
- University of Maryland Institute for Bioscience and Biotechnology Research, Rockville, MD, USA
- Department of Cell Biology and Molecular Genetics, University of Maryland, College Park, MD, USA
| | - Kala Bharath Pilla
- Research School of Chemistry, Australian National University, Canberra, Australian Capital Territory, Australia
| | - Barak Raveh
- Department of Microbiology and Molecular Genetics, IMRIC, Ein Kerem Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel
| | - P Douglas Renfrew
- Center for Computational Biology, Flatiron Institute, Simons Foundation, New York, NY, USA
| | - Shourya S Roy Burman
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Aliza Rubenstein
- Institute of Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ, USA
- Computational Biology and Molecular Biophysics Program, Rutgers, The State University of New Jersey, Piscataway, NJ, USA
| | - Marion F Sauer
- Chemical and Physical Biology Program, Vanderbilt Vaccine Center, Vanderbilt University, Nashville, TN, USA
| | - Andreas Scheck
- Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - William Schief
- Department of Immunology and Microbiology, The Scripps Research Institute, La Jolla, CA, USA
| | - Ora Schueler-Furman
- Department of Microbiology and Molecular Genetics, IMRIC, Ein Kerem Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel
| | - Yuval Sedan
- Department of Microbiology and Molecular Genetics, IMRIC, Ein Kerem Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel
| | - Alexander M Sevy
- Chemical and Physical Biology Program, Vanderbilt Vaccine Center, Vanderbilt University, Nashville, TN, USA
| | - Nikolaos G Sgourakis
- Department of Chemistry and Biochemistry, University of California Santa Cruz, Santa Cruz, CA, USA
| | - Lei Shi
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Justin B Siegel
- Department of Chemistry, University of California, Davis, Davis, CA, USA
- Department of Biochemistry and Molecular Medicine, University of California, Davis, Davis, California, USA
- Genome Center, University of California, Davis, Davis, CA, USA
| | | | - Shannon Smith
- Department of Chemistry, Vanderbilt University, Nashville, TN, USA
| | - Yifan Song
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
- Cyrus Biotechnology, Seattle, WA, USA
| | - Amelie Stein
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, USA
| | - Maria Szegedy
- Department of Chemistry and Chemical Biology, Rutgers, The State University of New Jersey, Piscataway, NJ, USA
| | - Frank D Teets
- Department of Biochemistry and Biophysics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Summer B Thyme
- Department of Biochemistry, University of Washington, Seattle, WA, USA
| | - Ray Yu-Ruei Wang
- Department of Biochemistry, University of Washington, Seattle, WA, USA
| | - Andrew Watkins
- Department of Biochemistry, Stanford University School of Medicine, Stanford, CA, USA
| | - Lior Zimmerman
- Department of Microbiology and Molecular Genetics, IMRIC, Ein Kerem Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel
| | - Richard Bonneau
- Center for Computational Biology, Flatiron Institute, Simons Foundation, New York, NY, USA.
- Department of Biology, New York University, New York, New York, USA.
- Department of Computer Science, New York University, New York, NY, USA.
- Center for Data Science, New York University, New York, NY, USA.
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5
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Borges PT, Brissos V, Hernandez G, Masgrau L, Lucas MF, Monza E, Frazão C, Cordeiro TN, Martins LO. Methionine-Rich Loop of Multicopper Oxidase McoA Follows Open-to-Close Transitions with a Role in Enzyme Catalysis. ACS Catal 2020. [DOI: 10.1021/acscatal.0c01623] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Affiliation(s)
- Patrícia T. Borges
- Instituto de Tecnologia Química e Biológica António Xavier, Universidade Nova de Lisboa, Av. da República, 2780-157 Oeiras, Portugal
| | - Vânia Brissos
- Instituto de Tecnologia Química e Biológica António Xavier, Universidade Nova de Lisboa, Av. da República, 2780-157 Oeiras, Portugal
| | - Guillem Hernandez
- Instituto de Tecnologia Química e Biológica António Xavier, Universidade Nova de Lisboa, Av. da República, 2780-157 Oeiras, Portugal
| | - Laura Masgrau
- Zymvol Biomodeling, Carrer Roc Boronat, 117, 08018 Barcelona, Spain
- Department of Chemistry, Universitat Autònoma de Barcelona, 08193 Bellaterra, Spain
| | | | - Emanuele Monza
- Zymvol Biomodeling, Carrer Roc Boronat, 117, 08018 Barcelona, Spain
| | - Carlos Frazão
- Instituto de Tecnologia Química e Biológica António Xavier, Universidade Nova de Lisboa, Av. da República, 2780-157 Oeiras, Portugal
| | - Tiago N. Cordeiro
- Instituto de Tecnologia Química e Biológica António Xavier, Universidade Nova de Lisboa, Av. da República, 2780-157 Oeiras, Portugal
| | - Lígia O. Martins
- Instituto de Tecnologia Química e Biológica António Xavier, Universidade Nova de Lisboa, Av. da República, 2780-157 Oeiras, Portugal
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6
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Delhommel F, Gabel F, Sattler M. Current approaches for integrating solution NMR spectroscopy and small-angle scattering to study the structure and dynamics of biomolecular complexes. J Mol Biol 2020; 432:2890-2912. [DOI: 10.1016/j.jmb.2020.03.014] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2019] [Revised: 02/27/2020] [Accepted: 03/10/2020] [Indexed: 01/24/2023]
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7
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Kuenze G, Bonneau R, Leman JK, Meiler J. Integrative Protein Modeling in RosettaNMR from Sparse Paramagnetic Restraints. Structure 2019; 27:1721-1734.e5. [PMID: 31522945 DOI: 10.1016/j.str.2019.08.012] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2019] [Revised: 08/05/2019] [Accepted: 08/23/2019] [Indexed: 12/17/2022]
Abstract
Computational methods to predict protein structure from nuclear magnetic resonance (NMR) restraints that only require assignment of backbone signals, hold great potential to study larger proteins. Ideally, computational methods designed to work with sparse data need to add atomic detail that is missing in the experimental restraints. We introduce a comprehensive framework into the Rosetta suite that uses NMR restraints derived from paramagnetic labeling. Specifically, RosettaNMR incorporates pseudocontact shifts, residual dipolar couplings, and paramagnetic relaxation enhancements. It continues to use backbone chemical shifts and nuclear Overhauser effect distance restraints. We assess RosettaNMR for protein structure prediction by folding 28 monomeric proteins and 8 homo-oligomeric proteins. Furthermore, the general applicability of RosettaNMR is demonstrated on two protein-protein and three protein-ligand docking examples. Paramagnetic restraints generated more accurate models for 85% of the benchmark proteins and, when combined with chemical shifts, sampled high-accuracy models (≤2Å) in 50% of the cases.
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Affiliation(s)
- Georg Kuenze
- Department of Chemistry, Vanderbilt University, Nashville, TN 37232, USA; Center for Structural Biology, Vanderbilt University, Nashville, TN 37240, USA.
| | - Richard Bonneau
- Center for Computational Biology, Flatiron Institute, Simons Foundation, New York, NY 10010, USA; Department of Biology and Center for Genomics and Systems Biology, New York University, New York, NY 10003, USA; Department of Computer Science, New York University, New York, NY 10012, USA
| | - Julia Koehler Leman
- Center for Computational Biology, Flatiron Institute, Simons Foundation, New York, NY 10010, USA; Department of Biology and Center for Genomics and Systems Biology, New York University, New York, NY 10003, USA.
| | - Jens Meiler
- Department of Chemistry, Vanderbilt University, Nashville, TN 37232, USA; Center for Structural Biology, Vanderbilt University, Nashville, TN 37240, USA.
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8
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Fast NMR method to probe solvent accessibility and disordered regions in proteins. Sci Rep 2019; 9:1647. [PMID: 30733478 PMCID: PMC6367444 DOI: 10.1038/s41598-018-37599-z] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2018] [Accepted: 12/10/2018] [Indexed: 01/12/2023] Open
Abstract
Understanding protein structure and dynamics, which govern key cellular processes, is crucial for basic and applied research. Intrinsically disordered protein (IDP) regions display multifunctionality via alternative transient conformations, being key players in disease mechanisms. IDP regions are abundant, namely in small viruses, allowing a large number of functions out of a small proteome. The relation between protein function and structure is thus now seen from a different perspective: as IDP regions enable transient structural arrangements, each conformer can play different roles within the cell. However, as IDP regions are hard and time-consuming to study via classical techniques (optimized for globular proteins with unique conformations), new methods are required. Here, employing the dengue virus (DENV) capsid (C) protein and the immunoglobulin-binding domain of streptococcal protein G, we describe a straightforward NMR method to differentiate the solvent accessibility of single amino acid N-H groups in structured and IDP regions. We also gain insights into DENV C flexible fold region biological activity. The method, based on minimal pH changes, uses the well-established 1H-15N HSQC pulse sequence and is easily implementable in current protein NMR routines. The data generated are simple to interpret, with this rapid approach being an useful first-choice IDPs characterization method.
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9
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10
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Chen PC, Hennig J. The role of small-angle scattering in structure-based screening applications. Biophys Rev 2018; 10:1295-1310. [PMID: 30306530 PMCID: PMC6233350 DOI: 10.1007/s12551-018-0464-x] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2018] [Accepted: 09/04/2018] [Indexed: 12/16/2022] Open
Abstract
In many biomolecular interactions, changes in the assembly states and structural conformations of participants can act as a complementary reporter of binding to functional and thermodynamic assays. This structural information is captured by a number of structural biology and biophysical techniques that are viable either as primary screens in small-scale applications or as secondary screens to complement higher throughput methods. In particular, small-angle X-ray scattering (SAXS) reports the average distance distribution between all atoms after orientational averaging. Such information is important when for example investigating conformational changes involved in inhibitory and regulatory mechanisms where binding events do not necessarily cause functional changes. Thus, we summarise here the current and prospective capabilities of SAXS-based screening in the context of other methods that yield structural information. Broad guidelines are also provided to assist readers in preparing screening protocols that are tailored to available X-ray sources.
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Affiliation(s)
- Po-Chia Chen
- Structural and Computational Biology Unit, European Molecular Biology Laboratory Heidelberg, Meyerhofstrasse 1, 69126, Heidelberg, Germany.
| | - Janosch Hennig
- Structural and Computational Biology Unit, European Molecular Biology Laboratory Heidelberg, Meyerhofstrasse 1, 69126, Heidelberg, Germany.
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11
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Aprahamian ML, Chea EE, Jones LM, Lindert S. Rosetta Protein Structure Prediction from Hydroxyl Radical Protein Footprinting Mass Spectrometry Data. Anal Chem 2018; 90:7721-7729. [PMID: 29874044 DOI: 10.1021/acs.analchem.8b01624] [Citation(s) in RCA: 43] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
In recent years mass spectrometry-based covalent labeling techniques such as hydroxyl radical footprinting (HRF) have emerged as valuable structural biology techniques, yielding information on protein tertiary structure. These data, however, are not sufficient to predict protein structure unambiguously, as they provide information only on the relative solvent exposure of certain residues. Despite some recent advances, no software currently exists that can utilize covalent labeling mass spectrometry data to predict protein tertiary structure. We have developed the first such tool, which incorporates mass spectrometry derived protection factors from HRF labeling as a new centroid score term for the Rosetta scoring function to improve the prediction of protein tertiary structures. We tested our method on a set of four soluble benchmark proteins with known crystal structures and either published HRF experimental results or internally acquired data. Using the HRF labeling data, we rescored large decoy sets of structures predicted with Rosetta for each of the four benchmark proteins. As a result, the model quality improved for all benchmark proteins as compared to when scored with Rosetta alone. For two of the four proteins we were even able to identify atomic resolution models with the addition of HRF data.
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Affiliation(s)
- Melanie L Aprahamian
- Department of Chemistry and Biochemistry , Ohio State University , Columbus , Ohio 43210 , United States
| | - Emily E Chea
- Department of Pharmaceutical Sciences , University of Maryland , Baltimore , Maryland 21201 , United States
| | - Lisa M Jones
- Department of Pharmaceutical Sciences , University of Maryland , Baltimore , Maryland 21201 , United States
| | - Steffen Lindert
- Department of Chemistry and Biochemistry , Ohio State University , Columbus , Ohio 43210 , United States
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12
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Monneau YR, Luo L, Sankaranarayanan NV, Nagarajan B, Vivès RR, Baleux F, Desai UR, Arenzana-Seidedos F, Lortat-Jacob H. Solution structure of CXCL13 and heparan sulfate binding show that GAG binding site and cellular signalling rely on distinct domains. Open Biol 2018; 7:rsob.170133. [PMID: 29070611 PMCID: PMC5666081 DOI: 10.1098/rsob.170133] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2017] [Accepted: 09/19/2017] [Indexed: 12/13/2022] Open
Abstract
Chemokines promote directional cell migration through binding to G-protein-coupled receptors, and as such are involved in a large array of developmental, homeostatic and pathological processes. They also interact with heparan sulfate (HS), the functional consequences of which depend on the respective location of the receptor- and the HS-binding sites, a detail that remains elusive for most chemokines. Here, to set up a biochemical framework to investigate how HS can regulate CXCL13 activity, we solved the solution structure of CXCL13. We showed that it comprises an unusually long and disordered C-terminal domain, appended to a classical chemokine-like structure. Using three independent experimental approaches, we found that it displays a unique association mode to HS, involving two clusters located in the α-helix and the C-terminal domain. Computational approaches were used to analyse the HS sequences preferentially recognized by the protein and gain atomic-level understanding of the CXCL13 dimerization induced upon HS binding. Starting with four sets of 254 HS tetrasaccharides, we identified 25 sequences that bind to CXCL13 monomer, among which a single one bound to CXCL13 dimer with high consistency. Importantly, we found that CXCL13 can be functionally presented to its receptor in a HS-bound form, suggesting that it can promote adhesion-dependent cell migration. Consistently, we designed CXCL13 mutations that preclude interaction with HS without affecting CXCR5-dependent cell signalling, opening the possibility to unambiguously demonstrate the role of HS in the biological function of this chemokine.
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Affiliation(s)
- Yoan R Monneau
- University of Grenoble Alpes, CNRS, CEA, IBS, 38000 Grenoble, France
| | - Lingjie Luo
- Institut Pasteur, INSERM U1108, Paris, France
| | - Nehru Viji Sankaranarayanan
- Institute for Structural Biology, Drug Discovery and Development, Virginia Commonwealth University, Richmond, VA, USA.,Department of Medicinal Chemistry, Virginia Commonwealth University, Richmond, VA, USA
| | - Balaji Nagarajan
- Institute for Structural Biology, Drug Discovery and Development, Virginia Commonwealth University, Richmond, VA, USA.,Department of Medicinal Chemistry, Virginia Commonwealth University, Richmond, VA, USA
| | - Romain R Vivès
- University of Grenoble Alpes, CNRS, CEA, IBS, 38000 Grenoble, France
| | - Françoise Baleux
- Institut Pasteur, Unité de Chimie des Biomolécules, UMR CNRS 3523, Paris, France
| | - Umesh R Desai
- Institute for Structural Biology, Drug Discovery and Development, Virginia Commonwealth University, Richmond, VA, USA.,Department of Medicinal Chemistry, Virginia Commonwealth University, Richmond, VA, USA
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13
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Nerli S, McShan AC, Sgourakis NG. Chemical shift-based methods in NMR structure determination. PROGRESS IN NUCLEAR MAGNETIC RESONANCE SPECTROSCOPY 2018; 106-107:1-25. [PMID: 31047599 PMCID: PMC6788782 DOI: 10.1016/j.pnmrs.2018.03.002] [Citation(s) in RCA: 34] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/25/2018] [Revised: 03/09/2018] [Accepted: 03/09/2018] [Indexed: 05/08/2023]
Abstract
Chemical shifts are highly sensitive probes harnessed by NMR spectroscopists and structural biologists as conformational parameters to characterize a range of biological molecules. Traditionally, assignment of chemical shifts has been a labor-intensive process requiring numerous samples and a suite of multidimensional experiments. Over the past two decades, the development of complementary computational approaches has bolstered the analysis, interpretation and utilization of chemical shifts for elucidation of high resolution protein and nucleic acid structures. Here, we review the development and application of chemical shift-based methods for structure determination with a focus on ab initio fragment assembly, comparative modeling, oligomeric systems, and automated assignment methods. Throughout our discussion, we point out practical uses, as well as advantages and caveats, of using chemical shifts in structure modeling. We additionally highlight (i) hybrid methods that employ chemical shifts with other types of NMR restraints (residual dipolar couplings, paramagnetic relaxation enhancements and pseudocontact shifts) that allow for improved accuracy and resolution of generated 3D structures, (ii) the utilization of chemical shifts to model the structures of sparsely populated excited states, and (iii) modeling of sidechain conformations. Finally, we briefly discuss the advantages of contemporary methods that employ sparse NMR data recorded using site-specific isotope labeling schemes for chemical shift-driven structure determination of larger molecules. With this review, we aim to emphasize the accessibility and versatility of chemical shifts for structure determination of challenging biological systems, and to point out emerging areas of development that lead us towards the next generation of tools.
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Affiliation(s)
- Santrupti Nerli
- Department of Chemistry and Biochemistry, University of California Santa Cruz, Santa Cruz, CA 95064, United States; Department of Computer Science, University of California Santa Cruz, Santa Cruz, CA 95064, United States
| | - Andrew C McShan
- Department of Chemistry and Biochemistry, University of California Santa Cruz, Santa Cruz, CA 95064, United States
| | - Nikolaos G Sgourakis
- Department of Chemistry and Biochemistry, University of California Santa Cruz, Santa Cruz, CA 95064, United States.
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14
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Zhang GJ, Zhou XG, Yu XF, Hao XH, Yu L. Enhancing Protein Conformational Space Sampling Using Distance Profile-Guided Differential Evolution. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2017; 14:1288-1301. [PMID: 28113726 DOI: 10.1109/tcbb.2016.2566617] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Abstract
De novo protein structure prediction aims to search for low-energy conformations as it follows the thermodynamics hypothesis that places native conformations at the global minimum of the protein energy surface. However, the native conformation is not necessarily located in the lowest-energy regions owing to the inaccuracies of the energy model. This study presents a differential evolution algorithm using distance profile-based selection strategy to sample conformations with reasonable structure effectively. In the proposed algorithm, besides energy, the residue-residue distance is considered another measure of the conformation. The average distance errors of decoys between the distance of each residue pair and the corresponding distance in the distance profiles are first calculated when the trial conformation yields a larger energy value than that of the target. Then, the distance acceptance probability of the trial conformation is designed based on distance profiles if the trial conformation obtains a lower average distance error compared with that of the target conformation. The trial conformation is accepted to the next generation in accordance with its distance acceptance probability. By using the dual constraints of energy and distance in guiding sampling, the algorithm can sample conformations with lower energies and more reasonable structures. Experimental results of 28 benchmark proteins show that the proposed algorithm can effectively predict near-native protein structures.
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15
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Sønderby P, Rinnan Å, Madsen JJ, Harris P, Bukrinski JT, Peters GHJ. Small-Angle X-ray Scattering Data in Combination with RosettaDock Improves the Docking Energy Landscape. J Chem Inf Model 2017; 57:2463-2475. [DOI: 10.1021/acs.jcim.6b00789] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/14/2023]
Affiliation(s)
- Pernille Sønderby
- Department
of Chemistry, Technical University of Denmark, DK-2800 Kongens
Lyngby, Denmark
| | - Åsmund Rinnan
- Department
of Food Science, Faculty of Science, University of Copenhagen, DK-1958 Frederiksberg C, Denmark
| | - Jesper J. Madsen
- Department
of Chemistry, The University of Chicago, Chicago, Illinois 60637, United States
| | - Pernille Harris
- Department
of Chemistry, Technical University of Denmark, DK-2800 Kongens
Lyngby, Denmark
| | | | - Günther H. J. Peters
- Department
of Chemistry, Technical University of Denmark, DK-2800 Kongens
Lyngby, Denmark
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16
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Combining NMR and small angle X-ray scattering for the study of biomolecular structure and dynamics. Arch Biochem Biophys 2017; 628:33-41. [PMID: 28501583 PMCID: PMC5553349 DOI: 10.1016/j.abb.2017.05.005] [Citation(s) in RCA: 35] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2016] [Revised: 05/05/2017] [Accepted: 05/08/2017] [Indexed: 01/25/2023]
Abstract
Small-angle X-ray scattering (SAXS) and Nuclear Magnetic Resonance (NMR) are established methods to analyze the structure and structural transitions of biological macromolecules in solution. Both methods are directly applicable to near-native macromolecular solutions and allow one to study structural responses to physical and chemical changes or ligand additions. Whereas SAXS is applied to elucidate overall structure, interactions and flexibility over a wide range of particle sizes, NMR yields atomic resolution detail for moderately sized macromolecules. NMR is arguably the most powerful technique for the experimental analysis of dynamics. The joint application of these two highly complementary techniques provides an extremely useful approach that facilitates comprehensive characterization of biomacromolecular solutions. SAXS and NMR are effective and highly complementary techniques in structural biology. Constraints from SAXS can be readily incorporated in NMR structure calculations. High resolution NMR models of domains can serve as building blocks for SAXS-based rigid body modeling. Flexible systems can be well described using ensemble approaches combining SAXS and NMR. Dynamics studies can be enhanced by combining SAXS and NMR.
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17
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Hybrid Applications of Solution Scattering to Aid Structural Biology. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2017; 1009:215-227. [PMID: 29218562 DOI: 10.1007/978-981-10-6038-0_13] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Biomolecular applications of solution X-ray and neutron scattering (SAXS and SANS, respectively) started in late 1960s - early 1970s but were relatively limited in their ability to provide a detailed structural picture and lagged behind what became the two primary methods of experimental structural biology - X-ray crystallography and NMR. However, improvements in both data analysis and instrumentation led to an explosive growth in the number of studies that used small-angle scattering (SAS) for investigation of macromolecular structure, often in combination with other biophysical techniques. Such hybrid applications are nowadays quickly becoming a norm whenever scattering data are used for two reasons. First, it is generally accepted that SAS data on their own cannot lead to a uniquely defined high-resolution structural model, creating a need for supplementing them with information from complementary techniques. Second, solution scattering data are frequently applied in situations when a method such NMR or X-ray crystallography cannot provide a satisfactory structural picture, which makes these additional restraints highly desirable. Maturation of the hybrid bio-SAS approaches brings to light new questions including completeness of the conformational space sampling, model validation, and data compatibility.
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18
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Rossi P, Xia Y, Khanra N, Veglia G, Kalodimos CG. 15N and 13C- SOFAST-HMQC editing enhances 3D-NOESY sensitivity in highly deuterated, selectively [ 1H, 13C]-labeled proteins. JOURNAL OF BIOMOLECULAR NMR 2016; 66:259-271. [PMID: 27878649 PMCID: PMC5218894 DOI: 10.1007/s10858-016-0074-5] [Citation(s) in RCA: 46] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/29/2016] [Accepted: 11/01/2016] [Indexed: 05/03/2023]
Abstract
The ongoing NMR method development effort strives for high quality multidimensional data with reduced collection time. Here, we apply 'SOFAST-HMQC' to frequency editing in 3D NOESY experiments and demonstrate the sensitivity benefits using highly deuterated and 15N, methyl labeled samples in H2O. The experiments benefit from a combination of selective T 1 relaxation (or L-optimized effect), from Ernst angle optimization and, in certain types of experiments, from using the mixing time for both NOE buildup and magnetization recovery. This effect enhances sensitivity by up to 2.4× at fast pulsing versus reference HMQC sequences of same overall length and water suppression characteristics. Representative experiments designed to address interesting protein NMR challenges are detailed. Editing capabilities are exploited with heteronuclear 15N,13C-edited, or with diagonal-free 13C aromatic/methyl-resolved 3D-SOFAST-HMQC-NOESY-HMQC. The latter experiment is used here to elucidate the methyl-aromatic NOE network in the hydrophobic core of the 19 kDa FliT-FliJ flagellar protein complex. Incorporation of fast pulsing to reference experiments such as 3D-NOESY-HMQC boosts digital resolution, simplifies the process of NOE assignment and helps to automate protein structure determination.
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Affiliation(s)
- Paolo Rossi
- Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota, Minneapolis, MN, 55455, USA.
| | - Youlin Xia
- Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota, Minneapolis, MN, 55455, USA
| | - Nandish Khanra
- Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota, Minneapolis, MN, 55455, USA
| | - Gianluigi Veglia
- Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota, Minneapolis, MN, 55455, USA.
| | - Charalampos G Kalodimos
- Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota, Minneapolis, MN, 55455, USA.
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19
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Bender BJ, Cisneros A, Duran AM, Finn JA, Fu D, Lokits AD, Mueller BK, Sangha AK, Sauer MF, Sevy AM, Sliwoski G, Sheehan JH, DiMaio F, Meiler J, Moretti R. Protocols for Molecular Modeling with Rosetta3 and RosettaScripts. Biochemistry 2016; 55:4748-63. [PMID: 27490953 PMCID: PMC5007558 DOI: 10.1021/acs.biochem.6b00444] [Citation(s) in RCA: 137] [Impact Index Per Article: 17.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
Abstract
![]()
Previously, we published an article
providing an overview of the
Rosetta suite of biomacromolecular modeling software and a series
of step-by-step tutorials [Kaufmann, K. W., et al. (2010) Biochemistry 49, 2987–2998]. The overwhelming positive
response to this publication we received motivates us to here share
the next iteration of these tutorials that feature de novo folding, comparative modeling, loop construction, protein docking,
small molecule docking, and protein design. This updated and expanded
set of tutorials is needed, as since 2010 Rosetta has been fully redesigned
into an object-oriented protein modeling program Rosetta3. Notable
improvements include a substantially improved energy function, an
XML-like language termed “RosettaScripts” for flexibly
specifying modeling task, new analysis tools, the addition of the
TopologyBroker to control conformational sampling, and support for
multiple templates in comparative modeling. Rosetta’s ability
to model systems with symmetric proteins, membrane proteins, noncanonical
amino acids, and RNA has also been greatly expanded and improved.
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Affiliation(s)
- Brian J Bender
- Department of Pharmacology, Vanderbilt University , Nashville, Tennessee 37232-6600, United States.,Center for Structural Biology, Vanderbilt University , Nashville, Tennessee 37240-7917, United States
| | - Alberto Cisneros
- Center for Structural Biology, Vanderbilt University , Nashville, Tennessee 37240-7917, United States.,Chemical and Physical Biology Program, Vanderbilt University , Nashville, Tennessee 37232-0301, United States
| | - Amanda M Duran
- Center for Structural Biology, Vanderbilt University , Nashville, Tennessee 37240-7917, United States.,Department of Chemistry, Vanderbilt University , Nashville, Tennessee 37235, United States
| | - Jessica A Finn
- Center for Structural Biology, Vanderbilt University , Nashville, Tennessee 37240-7917, United States.,Department of Pathology, Microbiology and Immunology, Vanderbilt University , Nashville, Tennessee 37232-2561, United States
| | - Darwin Fu
- Center for Structural Biology, Vanderbilt University , Nashville, Tennessee 37240-7917, United States.,Department of Chemistry, Vanderbilt University , Nashville, Tennessee 37235, United States
| | - Alyssa D Lokits
- Center for Structural Biology, Vanderbilt University , Nashville, Tennessee 37240-7917, United States.,Neuroscience Program, Vanderbilt University , Nashville, Tennessee 37235, United States
| | - Benjamin K Mueller
- Center for Structural Biology, Vanderbilt University , Nashville, Tennessee 37240-7917, United States.,Department of Chemistry, Vanderbilt University , Nashville, Tennessee 37235, United States
| | - Amandeep K Sangha
- Center for Structural Biology, Vanderbilt University , Nashville, Tennessee 37240-7917, United States.,Department of Chemistry, Vanderbilt University , Nashville, Tennessee 37235, United States
| | - Marion F Sauer
- Center for Structural Biology, Vanderbilt University , Nashville, Tennessee 37240-7917, United States.,Chemical and Physical Biology Program, Vanderbilt University , Nashville, Tennessee 37232-0301, United States
| | - Alexander M Sevy
- Center for Structural Biology, Vanderbilt University , Nashville, Tennessee 37240-7917, United States.,Chemical and Physical Biology Program, Vanderbilt University , Nashville, Tennessee 37232-0301, United States
| | - Gregory Sliwoski
- Center for Structural Biology, Vanderbilt University , Nashville, Tennessee 37240-7917, United States.,Department of Chemistry, Vanderbilt University , Nashville, Tennessee 37235, United States
| | - Jonathan H Sheehan
- Center for Structural Biology, Vanderbilt University , Nashville, Tennessee 37240-7917, United States
| | - Frank DiMaio
- Department of Biochemistry, University of Washington , Seattle, Washington 98195, United States
| | - Jens Meiler
- Department of Pharmacology, Vanderbilt University , Nashville, Tennessee 37232-6600, United States.,Center for Structural Biology, Vanderbilt University , Nashville, Tennessee 37240-7917, United States.,Chemical and Physical Biology Program, Vanderbilt University , Nashville, Tennessee 37232-0301, United States.,Department of Chemistry, Vanderbilt University , Nashville, Tennessee 37235, United States.,Department of Pathology, Microbiology and Immunology, Vanderbilt University , Nashville, Tennessee 37232-2561, United States.,Neuroscience Program, Vanderbilt University , Nashville, Tennessee 37235, United States
| | - Rocco Moretti
- Center for Structural Biology, Vanderbilt University , Nashville, Tennessee 37240-7917, United States.,Department of Chemistry, Vanderbilt University , Nashville, Tennessee 37235, United States
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20
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Recognition and targeting mechanisms by chaperones in flagellum assembly and operation. Proc Natl Acad Sci U S A 2016; 113:9798-803. [PMID: 27528687 DOI: 10.1073/pnas.1607845113] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
The flagellum is a complex bacterial nanomachine that requires the proper assembly of several different proteins for its function. Dedicated chaperones are central in preventing aggregation or undesired interactions of flagellar proteins, including their targeting to the export gate. FliT is a key flagellar chaperone that binds to several flagellar proteins in the cytoplasm, including its cognate filament-capping protein FliD. We have determined the solution structure of the FliT chaperone in the free state and in complex with FliD and the flagellar ATPase FliI. FliT adopts a four-helix bundle and uses a hydrophobic surface formed by the first three helices to recognize its substrate proteins. We show that the fourth helix constitutes the binding site for FlhA, a membrane protein at the export gate. In the absence of a substrate protein FliT adopts an autoinhibited structure wherein both the binding sites for substrates and FlhA are occluded. Substrate binding to FliT activates the complex for FlhA binding and thus targeting of the chaperone-substrate complex to the export gate. The activation and targeting mechanisms reported for FliT appear to be shared among the other flagellar chaperones.
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21
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Vestergaard B. Analysis of biostructural changes, dynamics, and interactions – Small-angle X-ray scattering to the rescue. Arch Biochem Biophys 2016; 602:69-79. [DOI: 10.1016/j.abb.2016.02.029] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2016] [Revised: 02/17/2016] [Accepted: 02/26/2016] [Indexed: 12/27/2022]
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22
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Kmiecik S, Gront D, Kolinski M, Wieteska L, Dawid AE, Kolinski A. Coarse-Grained Protein Models and Their Applications. Chem Rev 2016; 116:7898-936. [DOI: 10.1021/acs.chemrev.6b00163] [Citation(s) in RCA: 555] [Impact Index Per Article: 69.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Affiliation(s)
- Sebastian Kmiecik
- Faculty
of Chemistry, University of Warsaw, Pasteura 1, 02-093 Warsaw, Poland
| | - Dominik Gront
- Faculty
of Chemistry, University of Warsaw, Pasteura 1, 02-093 Warsaw, Poland
| | - Michal Kolinski
- Bioinformatics
Laboratory, Mossakowski Medical Research Center of the Polish Academy of Sciences, Pawinskiego 5, 02-106 Warsaw, Poland
| | - Lukasz Wieteska
- Faculty
of Chemistry, University of Warsaw, Pasteura 1, 02-093 Warsaw, Poland
- Department
of Medical Biochemistry, Medical University of Lodz, Mazowiecka 6/8, 92-215 Lodz, Poland
| | | | - Andrzej Kolinski
- Faculty
of Chemistry, University of Warsaw, Pasteura 1, 02-093 Warsaw, Poland
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23
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Venditti V, Egner TK, Clore GM. Hybrid Approaches to Structural Characterization of Conformational Ensembles of Complex Macromolecular Systems Combining NMR Residual Dipolar Couplings and Solution X-ray Scattering. Chem Rev 2016; 116:6305-22. [PMID: 26739383 PMCID: PMC5590664 DOI: 10.1021/acs.chemrev.5b00592] [Citation(s) in RCA: 35] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
Solving structures or structural ensembles of large macromolecular systems in solution poses a challenging problem. While NMR provides structural information at atomic resolution, increased spectral complexity, chemical shift overlap, and short transverse relaxation times (associated with slow tumbling) render application of the usual techniques that have been so successful for medium sized systems (<50 kDa) difficult. Solution X-ray scattering, on the other hand, is not limited by molecular weight but only provides low resolution structural information related to the overall shape and size of the system under investigation. Here we review how combining atomic resolution structures of smaller domains with sparse experimental data afforded by NMR residual dipolar couplings (which yield both orientational and shape information) and solution X-ray scattering data in rigid-body simulated annealing calculations provides a powerful approach for investigating the structural aspects of conformational dynamics in large multidomain proteins. The application of this hybrid methodology is illustrated for the 128 kDa dimer of bacterial Enzyme I which exists in a variety of open and closed states that are sampled at various points in the catalytic cycles, and for the capsid protein of the human immunodeficiency virus.
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Affiliation(s)
- Vincenzo Venditti
- Department of Chemistry, Iowa State University, Ames, Iowa 50011, United States
- Roy J. Carver Department of Biochemistry, Biophysics and Molecular Biology, Iowa State University, Ames, Iowa 50011, United States
| | - Timothy K. Egner
- Department of Chemistry, Iowa State University, Ames, Iowa 50011, United States
| | - G. Marius Clore
- Laboratory of Chemical Physics, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, Maryland 20892-0520, United States
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Habenstein B, Loquet A. Solid-state NMR: An emerging technique in structural biology of self-assemblies. Biophys Chem 2016; 210:14-26. [DOI: 10.1016/j.bpc.2015.07.003] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2015] [Accepted: 07/08/2015] [Indexed: 12/13/2022]
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25
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Protein-protein interactions: a supra-structural phenomenon demanding trans-disciplinary biophysical approaches. Curr Opin Struct Biol 2015; 35:76-86. [PMID: 26496626 DOI: 10.1016/j.sbi.2015.09.003] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2015] [Revised: 09/01/2015] [Accepted: 09/28/2015] [Indexed: 01/14/2023]
Abstract
Responsive formation of protein:protein interaction (PPI) upon diverse stimuli is a fundament of cellular function. As a consequence, PPIs are complex, adaptive entities, and exist in structurally heterogeneous interplays defined by the energetic states of the free and complexed protomers. The biophysical and structural investigations of PPIs consequently demand hybrid approaches, implementing orthogonal methods and strategies for global data analysis. Currently, impressive developments in hardware and software within several methodologies define a new era for the biostructural community. Data can be obtained at increasing resolution, at relevant time-scales and under increasingly relevant experimental conditions, intricate data are interpreted reliably, and the questions posed and answered grow in complexity. With this review, highlights from the study of PPIs using a multitude of biophysical methods, are reported. The aim is to depict how the elucidation of the interplay of structures requires the interplay of methods.
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26
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Sgourakis NG, May NA, Boyd LF, Ying J, Bax A, Margulies DH. A Novel MHC-I Surface Targeted for Binding by the MCMV m06 Immunoevasin Revealed by Solution NMR. J Biol Chem 2015; 290:28857-68. [PMID: 26463211 DOI: 10.1074/jbc.m115.689661] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2015] [Indexed: 12/21/2022] Open
Abstract
As part of its strategy to evade detection by the host immune system, murine cytomegalovirus (MCMV) encodes three proteins that modulate cell surface expression of major histocompatibility complex class I (MHC-I) molecules: the MHC-I homolog m152/gp40 as well as the m02-m16 family members m04/gp34 and m06/gp48. Previous studies of the m04 protein revealed a divergent Ig-like fold that is unique to immunoevasins of the m02-m16 family. Here, we engineer and characterize recombinant m06 and investigate its interactions with full-length and truncated forms of the MHC-I molecule H2-L(d) by several techniques. Furthermore, we employ solution NMR to map the interaction footprint of the m06 protein on MHC-I, taking advantage of a truncated H2-L(d), "mini-H2-L(d)," consisting of only the α1α2 platform domain. Mini-H2-L(d) refolded in vitro with a high affinity peptide yields a molecule that shows outstanding NMR spectral features, permitting complete backbone assignments. These NMR-based studies reveal that m06 binds tightly to a discrete site located under the peptide-binding platform that partially overlaps with the β2-microglobulin interface on the MHC-I heavy chain, consistent with in vitro binding experiments showing significantly reduced complex formation between m06 and β2-microglobulin-associated MHC-I. Moreover, we carry out NMR relaxation experiments to characterize the picosecond-nanosecond dynamics of the free mini-H2-L(d) MHC-I molecule, revealing that the site of interaction is highly ordered. This study provides insight into the mechanism of the interaction of m06 with MHC-I, suggesting a structural manipulation of the target MHC-I molecule at an early stage of the peptide-loading pathway.
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Affiliation(s)
| | - Nathan A May
- the Molecular Biology Section, Laboratory of Immunology, NIAID, National Institutes of Health, Bethesda, Maryland 20892
| | - Lisa F Boyd
- the Molecular Biology Section, Laboratory of Immunology, NIAID, National Institutes of Health, Bethesda, Maryland 20892
| | - Jinfa Ying
- From the Laboratory of Chemical Physics, NIDDK, and
| | - Ad Bax
- From the Laboratory of Chemical Physics, NIDDK, and
| | - David H Margulies
- the Molecular Biology Section, Laboratory of Immunology, NIAID, National Institutes of Health, Bethesda, Maryland 20892
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27
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Everett JK, Tejero R, Murthy SBK, Acton TB, Aramini JM, Baran MC, Benach J, Cort JR, Eletsky A, Forouhar F, Guan R, Kuzin AP, Lee HW, Liu G, Mani R, Mao B, Mills JL, Montelione AF, Pederson K, Powers R, Ramelot T, Rossi P, Seetharaman J, Snyder D, Swapna GVT, Vorobiev SM, Wu Y, Xiao R, Yang Y, Arrowsmith CH, Hunt JF, Kennedy MA, Prestegard JH, Szyperski T, Tong L, Montelione GT. A community resource of experimental data for NMR / X-ray crystal structure pairs. Protein Sci 2015; 25:30-45. [PMID: 26293815 DOI: 10.1002/pro.2774] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2015] [Accepted: 08/17/2015] [Indexed: 12/11/2022]
Abstract
We have developed an online NMR / X-ray Structure Pair Data Repository. The NIGMS Protein Structure Initiative (PSI) has provided many valuable reagents, 3D structures, and technologies for structural biology. The Northeast Structural Genomics Consortium was one of several PSI centers. NESG used both X-ray crystallography and NMR spectroscopy for protein structure determination. A key goal of the PSI was to provide experimental structures for at least one representative of each of hundreds of targeted protein domain families. In some cases, structures for identical (or nearly identical) constructs were determined by both NMR and X-ray crystallography. NMR spectroscopy and X-ray diffraction data for 41 of these "NMR / X-ray" structure pairs determined using conventional triple-resonance NMR methods with extensive sidechain resonance assignments have been organized in an online NMR / X-ray Structure Pair Data Repository. In addition, several NMR data sets for perdeuterated, methyl-protonated protein samples are included in this repository. As an example of the utility of this repository, these data were used to revisit questions about the precision and accuracy of protein NMR structures first outlined by Levy and coworkers several years ago (Andrec et al., Proteins 2007;69:449-465). These results demonstrate that the agreement between NMR and X-ray crystal structures is improved using modern methods of protein NMR spectroscopy. The NMR / X-ray Structure Pair Data Repository will provide a valuable resource for new computational NMR methods development.
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Affiliation(s)
- John K Everett
- Center for Advanced Biotechnology and Medicine, Department of Molecular Biology and Biochemistry, and Northeast Structural Genomics Consortium, Rutgers, the State University of New Jersey, Piscataway, New Jersey, 08854, USA
| | - Roberto Tejero
- Departamento De Química Física, Universidad De Valencia, Valencia, Spain
| | - Sarath B K Murthy
- Center for Advanced Biotechnology and Medicine, Department of Molecular Biology and Biochemistry, and Northeast Structural Genomics Consortium, Rutgers, the State University of New Jersey, Piscataway, New Jersey, 08854, USA
| | - Thomas B Acton
- Center for Advanced Biotechnology and Medicine, Department of Molecular Biology and Biochemistry, and Northeast Structural Genomics Consortium, Rutgers, the State University of New Jersey, Piscataway, New Jersey, 08854, USA
| | - James M Aramini
- Center for Advanced Biotechnology and Medicine, Department of Molecular Biology and Biochemistry, and Northeast Structural Genomics Consortium, Rutgers, the State University of New Jersey, Piscataway, New Jersey, 08854, USA
| | - Michael C Baran
- Center for Advanced Biotechnology and Medicine, Department of Molecular Biology and Biochemistry, and Northeast Structural Genomics Consortium, Rutgers, the State University of New Jersey, Piscataway, New Jersey, 08854, USA
| | - Jordi Benach
- Department of Biological Sciences and Northeast Structural Genomics Consortium, Columbia University, New York, NY, 10027, USA
| | - John R Cort
- Fundamental and Computational Sciences Directorate, Pacific Northwest National Laboratory, Richland, Washington, 99354, USA
| | - Alexander Eletsky
- Department of Chemistry, The State University of New York at Buffalo, and Northeast Structural Genomics Consortium, Buffalo, New York, 14260, USA
| | - Farhad Forouhar
- Department of Biological Sciences and Northeast Structural Genomics Consortium, Columbia University, New York, NY, 10027, USA
| | - Rongjin Guan
- Center for Advanced Biotechnology and Medicine, Department of Molecular Biology and Biochemistry, and Northeast Structural Genomics Consortium, Rutgers, the State University of New Jersey, Piscataway, New Jersey, 08854, USA
| | - Alexandre P Kuzin
- Department of Biological Sciences and Northeast Structural Genomics Consortium, Columbia University, New York, NY, 10027, USA
| | - Hsiau-Wei Lee
- Complex Carbohydrate Research Center and Northeast Structural Genomics Consortium, University of Georgia, Athens, Georgia, 30602, USA
| | - Gaohua Liu
- Center for Advanced Biotechnology and Medicine, Department of Molecular Biology and Biochemistry, and Northeast Structural Genomics Consortium, Rutgers, the State University of New Jersey, Piscataway, New Jersey, 08854, USA
| | - Rajeswari Mani
- Center for Advanced Biotechnology and Medicine, Department of Molecular Biology and Biochemistry, and Northeast Structural Genomics Consortium, Rutgers, the State University of New Jersey, Piscataway, New Jersey, 08854, USA
| | - Binchen Mao
- Center for Advanced Biotechnology and Medicine, Department of Molecular Biology and Biochemistry, and Northeast Structural Genomics Consortium, Rutgers, the State University of New Jersey, Piscataway, New Jersey, 08854, USA
| | - Jeffrey L Mills
- Department of Chemistry, The State University of New York at Buffalo, and Northeast Structural Genomics Consortium, Buffalo, New York, 14260, USA
| | - Alexander F Montelione
- Center for Advanced Biotechnology and Medicine, Department of Molecular Biology and Biochemistry, and Northeast Structural Genomics Consortium, Rutgers, the State University of New Jersey, Piscataway, New Jersey, 08854, USA
| | - Kari Pederson
- Complex Carbohydrate Research Center and Northeast Structural Genomics Consortium, University of Georgia, Athens, Georgia, 30602, USA
| | - Robert Powers
- Department of Chemistry, University of Nebraska-Lincoln, Lincoln, Nebraska, 68588, USA
| | - Theresa Ramelot
- Department of Chemistry and Biochemistry, Northeast Structural Genomics Consortium, Miami University, Oxford, Ohio, 45056, USA
| | - Paolo Rossi
- Center for Advanced Biotechnology and Medicine, Department of Molecular Biology and Biochemistry, and Northeast Structural Genomics Consortium, Rutgers, the State University of New Jersey, Piscataway, New Jersey, 08854, USA
| | - Jayaraman Seetharaman
- Department of Biological Sciences and Northeast Structural Genomics Consortium, Columbia University, New York, NY, 10027, USA
| | - David Snyder
- Department of Chemistry, College of Science and Health, William Paterson University of NJ, Wayne, New Jersey, 07470, USA
| | - G V T Swapna
- Center for Advanced Biotechnology and Medicine, Department of Molecular Biology and Biochemistry, and Northeast Structural Genomics Consortium, Rutgers, the State University of New Jersey, Piscataway, New Jersey, 08854, USA
| | - Sergey M Vorobiev
- Department of Biological Sciences and Northeast Structural Genomics Consortium, Columbia University, New York, NY, 10027, USA
| | - Yibing Wu
- Department of Chemistry, The State University of New York at Buffalo, and Northeast Structural Genomics Consortium, Buffalo, New York, 14260, USA
| | - Rong Xiao
- Center for Advanced Biotechnology and Medicine, Department of Molecular Biology and Biochemistry, and Northeast Structural Genomics Consortium, Rutgers, the State University of New Jersey, Piscataway, New Jersey, 08854, USA
| | - Yunhuang Yang
- Department of Chemistry and Biochemistry, Northeast Structural Genomics Consortium, Miami University, Oxford, Ohio, 45056, USA
| | - Cheryl H Arrowsmith
- Cancer Genomics & Proteomics, Department of Medical Biophysics, Ontario Cancer Institute, and Northeast Structural Genomics Consortium, University of Toronto, Toronto, Ontario, M5G 1L7, Canada
| | - John F Hunt
- Department of Biological Sciences and Northeast Structural Genomics Consortium, Columbia University, New York, NY, 10027, USA
| | - Michael A Kennedy
- Department of Chemistry and Biochemistry, Northeast Structural Genomics Consortium, Miami University, Oxford, Ohio, 45056, USA
| | - James H Prestegard
- Complex Carbohydrate Research Center and Northeast Structural Genomics Consortium, University of Georgia, Athens, Georgia, 30602, USA
| | - Thomas Szyperski
- Department of Chemistry, The State University of New York at Buffalo, and Northeast Structural Genomics Consortium, Buffalo, New York, 14260, USA
| | - Liang Tong
- Department of Biological Sciences and Northeast Structural Genomics Consortium, Columbia University, New York, NY, 10027, USA
| | - Gaetano T Montelione
- Center for Advanced Biotechnology and Medicine, Department of Molecular Biology and Biochemistry, and Northeast Structural Genomics Consortium, Rutgers, the State University of New Jersey, Piscataway, New Jersey, 08854, USA.,Department of Biochemistry, Robert Wood Johnson Medical School, Rutgers, the State University of New Jersey, Piscataway, New Jersey, 08854, USA
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