1
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Graille M. InsPection of electron density maps supports wrongly modeled hexakisphosphate (InsP6) bound to African swine fever mRNA-decapping enzyme g5Rp. J Virol 2024; 98:e0159723. [PMID: 38656175 PMCID: PMC11092362 DOI: 10.1128/jvi.01597-23] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/26/2024] Open
Affiliation(s)
- Marc Graille
- Laboratoire de Biologie Structurale de la Cellule (BIOC), CNRS, École polytechnique, Institut Polytechnique de Paris, Palaiseau, France
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2
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Bijak V, Szczygiel M, Lenkiewicz J, Gucwa M, Cooper DR, Murzyn K, Minor W. The current role and evolution of X-ray crystallography in drug discovery and development. Expert Opin Drug Discov 2023; 18:1221-1230. [PMID: 37592849 PMCID: PMC10620067 DOI: 10.1080/17460441.2023.2246881] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Accepted: 08/08/2023] [Indexed: 08/19/2023]
Abstract
INTRODUCTION Macromolecular X-ray crystallography and cryo-EM are currently the primary techniques used to determine the three-dimensional structures of proteins, nucleic acids, and viruses. Structural information has been critical to drug discovery and structural bioinformatics. The integration of artificial intelligence (AI) into X-ray crystallography has shown great promise in automating and accelerating the analysis of complex structural data, further improving the efficiency and accuracy of structure determination. AREAS COVERED This review explores the relationship between X-ray crystallography and other modern structural determination methods. It examines the integration of data acquired from diverse biochemical and biophysical techniques with those derived from structural biology. Additionally, the paper offers insights into the influence of AI on X-ray crystallography, emphasizing how integrating AI with experimental approaches can revolutionize our comprehension of biological processes and interactions. EXPERT OPINION Investing in science is crucially emphasized due to its significant role in drug discovery and advancements in healthcare. X-ray crystallography remains an essential source of structural biology data for drug discovery. Recent advances in biochemical, spectroscopic, and bioinformatic methods, along with the integration of AI techniques, hold the potential to revolutionize drug discovery when effectively combined with robust data management practices.
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Affiliation(s)
- Vanessa Bijak
- Department of Molecular Physiology and Biological Physics, University of Virginia, Charlottesville 22908
| | - Michal Szczygiel
- Department of Molecular Physiology and Biological Physics, University of Virginia, Charlottesville 22908
- Department of Computational Biophysics and Bioinformatics, Jagiellonian University, Krakow, Poland
| | - Joanna Lenkiewicz
- Department of Molecular Physiology and Biological Physics, University of Virginia, Charlottesville 22908
| | - Michal Gucwa
- Department of Molecular Physiology and Biological Physics, University of Virginia, Charlottesville 22908
- Doctoral School of Exact and Natural Sciences, Jagiellonian University, Krakow, Poland
| | - David R. Cooper
- Department of Molecular Physiology and Biological Physics, University of Virginia, Charlottesville 22908
| | - Krzysztof Murzyn
- Department of Computational Biophysics and Bioinformatics, Jagiellonian University, Krakow, Poland
| | - Wladek Minor
- Department of Molecular Physiology and Biological Physics, University of Virginia, Charlottesville 22908
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3
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Kumar P, Vyas P, Faisal SM, Chang YF, Akif M. Crystal structure of a variable region segment of Leptospira host-interacting outer surface protein, LigA, reveals the orientation of Ig-like domains. Int J Biol Macromol 2023:125445. [PMID: 37336372 DOI: 10.1016/j.ijbiomac.2023.125445] [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: 02/07/2023] [Revised: 06/13/2023] [Accepted: 06/14/2023] [Indexed: 06/21/2023]
Abstract
Leptospiral immunoglobulin-like (Lig) protein family is a surface-exposed protein from the pathogenic Leptospira. The Lig protein family has been identified as an essential virulence factor of L. interrogan. One of the family members, LigA, contains 13 homologous tandem repeats of bacterial Ig-like (Big) domains in its extracellular portion. It is crucial in binding with the host's Extracellular matrices (ECM) and complement factors. However, its vital role in the invasion and evasion of pathogenic Leptospira, structural details, and domain organization of the extracellular portion of this protein are not explored thoroughly. Here, we described the first high-resolution crystal structure of a variable region segment (LigA8-9) of LigA at 1.87 Å resolution. The structure showed some remarkably distinctive aspects compared with the most closely related Immunoglobulin superfamily (IgSF) members. The structure illustrated the relative orientation of two domains and highlighted the role of the linker region in the domain orientation. We also observed an apparent electron density of Ca2+ ions coordinated with a proper interacting geometry within the protein. Molecular dynamic simulations demonstrated the involvement of a linker salt bridge in providing rigidity between the two domains. Our study proposes an overall arrangement of Ig-like domains in the LigA protein. The structural understanding of the extracellular portion of LigA and its interaction with the ECM provides insight into developing new therapeutics directed toward leptospirosis.
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Affiliation(s)
- Pankaj Kumar
- Laboratory of Structural Biology, Department of Biochemistry, School of Life Sciences, University of Hyderabad, Gachibowli, Hyderabad, India
| | - Pallavi Vyas
- Laboratory of Vaccine Immunology, National Institute of Animal Biotechnology, Gachibowli, Hyderabad, Telangana, India
| | - Syed M Faisal
- Laboratory of Vaccine Immunology, National Institute of Animal Biotechnology, Gachibowli, Hyderabad, Telangana, India
| | - Yung-Fu Chang
- Department of Population Medicine and Diagnostic Sciences, College of Veterinary Medicine, Cornell University, Ithaca, NY, USA
| | - Mohd Akif
- Laboratory of Structural Biology, Department of Biochemistry, School of Life Sciences, University of Hyderabad, Gachibowli, Hyderabad, India.
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4
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Bijak V, Gucwa M, Lenkiewicz J, Murzyn K, Cooper DR, Minor W. Continuous Validation Across Macromolecular Structure Determination Process. NIHON KESSHO GAKKAI SHI 2023; 65:10-16. [PMID: 37416056 PMCID: PMC10321142 DOI: 10.5940/jcrsj.65.10] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/08/2023]
Abstract
The overall quality of the experimentally determined structures contained in the PDB is exceptionally high, mainly due to the continuous improvement of model building and structural validation programs. Improving reproducibility on a large scale requires expanding the concept of validation in structural biology and all other disciplines to include a broader framework that encompasses the entire project. A successful approach to science requires diligent attention to detail and a focus on the future. An earnest commitment to data availability and reuse is essential for scientific progress, be that by human minds or artificial intelligence.
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Affiliation(s)
- Vanessa Bijak
- Department of Molecular Physiology and Biological Physics, University of Virginia
| | - Michal Gucwa
- Department of Molecular Physiology and Biological Physics, University of Virginia
- Department of Computational Biophysics and Bioinformatics, Jagiellonian University
| | - Joanna Lenkiewicz
- Department of Molecular Physiology and Biological Physics, University of Virginia
| | - Krzysztof Murzyn
- Department of Computational Biophysics and Bioinformatics, Jagiellonian University
| | - David R Cooper
- Department of Molecular Physiology and Biological Physics, University of Virginia
| | - Wladek Minor
- Department of Molecular Physiology and Biological Physics, University of Virginia
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5
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Macnar JM, Brzezinski D, Chruszcz M, Gront D. Analysis of protein structures containing
HEPES
and
MES
molecules. Protein Sci 2022. [PMCID: PMC9601878 DOI: 10.1002/pro.4415] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
X‐ray crystallography is the main experimental method behind ligand–macromolecule complexes found in the Protein Data Bank (PDB). Applying bioinformatics methods to such structural data can fuel drug discovery, albeit under the condition that the information is correct. Regrettably, a small number of structures in the PDB are of suboptimal quality due to incorrectly identified and modeled ligands in protein–ligand complexes. In this paper, we combine a theoretical‐graph approach, nuclear density estimates, bioinformatics methods, and prior chemical knowledge to analyze two non‐physiological ligands, HEPES and MES, that are frequent components of crystallization and purifications buffers. Our analysis includes quantum mechanics calculations and Cambridge Structure Database (CSD) queries to define the ideal conformation of these ligands, geometry analysis of PDB deposits regarding several quality factors, and a search for homologous structures to identify other small molecules that could bind in place of the parasitic ligand. Our results highlight the need for careful refinement of macromolecule–ligand complexes and better validation tools that integrate results from all relevant resources. PDB Code(s): 3K4L, 3PYI, 5T6L, 6BB0, 1PJX, 3O4P, 6WCF, 3DKE, 3E10, 6G38, 4E8R, 4Z91, 3E9F, 1MOS, 1MOQ, 2ESB, 1VHR, 4P66 and 6NNI;
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Affiliation(s)
- Joanna Magdalena Macnar
- Department of Molecular Physiology and Biological Physics University of Virginia Charlottesville Virginia USA
- College of Inter‐Faculty Individual Studies in Mathematics and Natural Sciences University of Warsaw Warsaw Poland
- Faculty of Chemistry, Biological and Chemical Research Center University of Warsaw Warsaw Poland
| | - Dariusz Brzezinski
- Department of Molecular Physiology and Biological Physics University of Virginia Charlottesville Virginia USA
- Institute of Computing Science Poznan University of Technology Poznan Poland
- Center for Biocrystallographic Research, Institute of Bioorganic Chemistry Polish Academy of Sciences Poznan Poland
| | - Maksymilian Chruszcz
- Department of Chemistry and Biochemistry University of South Carolina Columbia South Carolina USA
| | - Dominik Gront
- Department of Molecular Physiology and Biological Physics University of Virginia Charlottesville Virginia USA
- Faculty of Chemistry, Biological and Chemical Research Center University of Warsaw Warsaw Poland
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6
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Czarna A, Plewka J, Kresik L, Matsuda A, Karim A, Robinson C, O'Byrne S, Cunningham F, Georgiou I, Wilk P, Pachota M, Popowicz G, Wyatt PG, Dubin G, Pyrć K. Refolding of lid subdomain of SARS-CoV-2 nsp14 upon nsp10 interaction releases exonuclease activity. Structure 2022; 30:1050-1054.e2. [PMID: 35609600 PMCID: PMC9125827 DOI: 10.1016/j.str.2022.04.014] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2022] [Revised: 04/11/2022] [Accepted: 04/28/2022] [Indexed: 12/13/2022]
Abstract
During RNA replication, coronaviruses require proofreading to maintain the integrity of their large genomes. Nsp14 associates with viral polymerase complex to excise the mismatched nucleotides. Aside from the exonuclease activity, nsp14 methyltransferase domain mediates cap methylation, facilitating translation initiation and protecting viral RNA from recognition by the innate immune sensors. The nsp14 exonuclease activity is modulated by a protein co-factor nsp10. While the nsp10/nsp14 complex structure is available, the mechanistic basis for nsp10-mediated modulation remains unclear in the absence of the nsp14 structure. Here, we provide a crystal structure of nsp14 in an apo-form. Comparative analysis of the apo- and nsp10-bound structures explain the modulatory role of the co-factor protein and reveal the allosteric nsp14 control mechanism essential for drug discovery. Further, the flexibility of the N-terminal lid of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) nsp14 structure presented in this study rationalizes the recently proposed idea of nsp14/nsp10/nsp16 ternary complex.
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Affiliation(s)
- Anna Czarna
- Virogenetics Laboratory of Virology, Malopolska Centre of Biotechnology, Jagiellonian University, Gronostajowa 7a, 30-387 Krakow, Poland
| | - Jacek Plewka
- Virogenetics Laboratory of Virology, Malopolska Centre of Biotechnology, Jagiellonian University, Gronostajowa 7a, 30-387 Krakow, Poland
| | - Leanid Kresik
- Virogenetics Laboratory of Virology, Malopolska Centre of Biotechnology, Jagiellonian University, Gronostajowa 7a, 30-387 Krakow, Poland
| | - Alex Matsuda
- Virogenetics Laboratory of Virology, Malopolska Centre of Biotechnology, Jagiellonian University, Gronostajowa 7a, 30-387 Krakow, Poland
| | - Abdulkarim Karim
- Department of Biology, College of Science, Salahaddin University-Erbil, Kirkuk Road, 44002 Erbil, Kurdistan Region, Iraq; Department of Community Health, College of Health Technology, Cihan University-Erbil, 100 Street, 44001 Erbil, Kurdistan Region, Iraq
| | - Colin Robinson
- Drug Discovery Unit, Wellcome Centre for Anti-Infectives Research, School of Life Sciences, University of Dundee, Dow Street, Dundee DDI 5EH, UK
| | - Sean O'Byrne
- Drug Discovery Unit, Wellcome Centre for Anti-Infectives Research, School of Life Sciences, University of Dundee, Dow Street, Dundee DDI 5EH, UK
| | - Fraser Cunningham
- Drug Discovery Unit, Wellcome Centre for Anti-Infectives Research, School of Life Sciences, University of Dundee, Dow Street, Dundee DDI 5EH, UK
| | - Irene Georgiou
- Drug Discovery Unit, Wellcome Centre for Anti-Infectives Research, School of Life Sciences, University of Dundee, Dow Street, Dundee DDI 5EH, UK
| | - Piotr Wilk
- Structural Biology Core Facility, Malopolska Centre of Biotechnology, Jagiellonian University, Gronostajowa 7a, 30-387 Krakow, Poland
| | - Magdalena Pachota
- Virogenetics Laboratory of Virology, Malopolska Centre of Biotechnology, Jagiellonian University, Gronostajowa 7a, 30-387 Krakow, Poland
| | - Grzegorz Popowicz
- Helmholtz Zentrum München, Ingolstädter Landstrasse 1, 85764 Neuherberg, Germany; Bavarian NMR Center, Department of Chemistry, Technical University of Munich, Lichtenbergstrasse 4, 85748 Garching, Germany
| | - Paul Graham Wyatt
- Drug Discovery Unit, Wellcome Centre for Anti-Infectives Research, School of Life Sciences, University of Dundee, Dow Street, Dundee DDI 5EH, UK.
| | - Grzegorz Dubin
- Protein Crystallography Research Group, Malopolska Centre of Biotechnology, Jagiellonian University, Gronostajowa 7a, 30-387 Krakow, Poland.
| | - Krzysztof Pyrć
- Virogenetics Laboratory of Virology, Malopolska Centre of Biotechnology, Jagiellonian University, Gronostajowa 7a, 30-387 Krakow, Poland.
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7
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Lu Q. Molecular structure recognition by blob detection. RSC Adv 2021; 11:35879-35886. [PMID: 35492772 PMCID: PMC9043223 DOI: 10.1039/d1ra05752a] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2021] [Accepted: 10/31/2021] [Indexed: 11/23/2022] Open
Abstract
Molecular structure recognition is fundamental in computational chemistry. The most common approach is to calculate the root mean square deviation (RMSD) between two sets of molecular coordinates. However, this method does not perform well for large molecules. In this work, a new method is proposed for structure comparison. Blob detection is used for recognizing structural features. Fragmentation of molecules is proposed as the pre-treatment. Mapping between blobs and atoms is developed as the post-treatment. A set of key parameters important for blob detections are determined. The dissimilarity is quantified by calculating the Euclidean metric of the blob vectors. The overall algorithm is found to be accurate to distinguish structural dissimilarity. The method has potential to be combined with other pattern recognition techniques for new chemistry discoveries. Molecular structure recognition is fundamental in computational chemistry.![]()
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Affiliation(s)
- Qing Lu
- Beijing National Laboratory for Molecular Sciences, Institute of Chemistry, Chinese Academy of Sciences Beijing 100190 China
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8
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Brzezinski D, Porebski PJ, Kowiel M, Macnar JM, Minor W. Recognizing and validating ligands with CheckMyBlob. Nucleic Acids Res 2021; 49:W86-W92. [PMID: 33905501 PMCID: PMC8262754 DOI: 10.1093/nar/gkab296] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2021] [Revised: 04/04/2021] [Accepted: 04/11/2021] [Indexed: 11/15/2022] Open
Abstract
Structure-guided drug design depends on the correct identification of ligands in crystal structures of protein complexes. However, the interpretation of the electron density maps is challenging and often burdened with confirmation bias. Ligand identification can be aided by automatic methods such as CheckMyBlob, a machine learning algorithm that learns to generalize ligand descriptions from sets of moieties deposited in the Protein Data Bank. Here, we present the CheckMyBlob web server, a platform that can identify ligands in unmodeled fragments of electron density maps or validate ligands in existing models. The server processes PDB/mmCIF and MTZ files and returns a ranking of 10 most likely ligands for each detected electron density blob along with interactive 3D visualizations. Additionally, for each prediction/validation, a plugin script is generated that enables users to conduct a detailed analysis of the server results in Coot. The CheckMyBlob web server is available at https://checkmyblob.bioreproducibility.org.
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Affiliation(s)
- Dariusz Brzezinski
- Department of Molecular Physiology and Biological Physics, University of Virginia, Charlottesville, VA 22908, USA.,Institute of Computing Science, Poznan University of Technology, Poznan, 60-965, Poland.,Center for Biocrystallographic Research, Institute of Bioorganic Chemistry, Polish Academy of Sciences, Poznan, 61-704, Poland
| | - Przemyslaw J Porebski
- Department of Molecular Physiology and Biological Physics, University of Virginia, Charlottesville, VA 22908, USA
| | - Marcin Kowiel
- Center for Biocrystallographic Research, Institute of Bioorganic Chemistry, Polish Academy of Sciences, Poznan, 61-704, Poland
| | - Joanna M Macnar
- Department of Molecular Physiology and Biological Physics, University of Virginia, Charlottesville, VA 22908, USA.,College of Inter-Faculty Individual Studies in Mathematics and Natural Sciences, University of Warsaw, Warsaw, 02-097, Poland.,Faculty of Chemistry, Biological and Chemical Research Center, University of Warsaw, Warsaw, 02-089, Poland
| | - Wladek Minor
- Department of Molecular Physiology and Biological Physics, University of Virginia, Charlottesville, VA 22908, USA
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9
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Czyzewski A, Krawiec F, Brzezinski D, Porebski PJ, Minor W. Detecting anomalies in X-ray diffraction images using convolutional neural networks. EXPERT SYSTEMS WITH APPLICATIONS 2021; 174:114740. [PMID: 34366575 PMCID: PMC8341115 DOI: 10.1016/j.eswa.2021.114740] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/09/2023]
Abstract
Our understanding of life is based upon the interpretation of macromolecular structures and their dynamics. Almost 90% of currently known macromolecular models originated from electron density maps constructed using X-ray diffraction images. Even though diffraction images are critical for structure determination, due to their vast amounts and noisy, non-intuitive nature, their quality is rarely inspected. In this paper, we use recent advances in machine learning to automatically detect seven types of anomalies in X-ray diffraction images. For this purpose, we utilize a novel X-ray beam center detection algorithm, propose three different image representations, and compare the predictive performance of general-purpose classifiers and deep convolutional neural networks (CNNs). In benchmark tests on a set of 6,311 X-ray diffraction images, the proposed CNN achieved between 87% and 99% accuracy depending on the type of anomaly. Experimental results show that the proposed anomaly detection system can be considered suitable for early detection of sub-optimal data collection conditions and malfunctions at X-ray experimental stations.
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Affiliation(s)
- Adam Czyzewski
- Institute of Computing Science, Poznan University of
Technology, ul. Piotrowo 2, 60-965 Poznan, Poland
| | - Faustyna Krawiec
- Institute of Computing Science, Poznan University of
Technology, ul. Piotrowo 2, 60-965 Poznan, Poland
| | - Dariusz Brzezinski
- Institute of Computing Science, Poznan University of
Technology, ul. Piotrowo 2, 60-965 Poznan, Poland
- Center for Biocrystallographic Research, Institute of
Bioorganic Chemistry, Polish Academy of Sciences, Poznan, 61-714, Poland
- Center for Artificial Intelligence and Machine Learning,
Poznan University of Technology, ul. Piotrowo 2, 60-965 Poznan, Poland
- Department of Molecular Physiology and Biological Physics,
University of Virginia, Charlottesville, VA 22901, USA
| | - Przemyslaw Jerzy Porebski
- Department of Molecular Physiology and Biological Physics,
University of Virginia, Charlottesville, VA 22901, USA
| | - Wladek Minor
- Department of Molecular Physiology and Biological Physics,
University of Virginia, Charlottesville, VA 22901, USA
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10
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Grabowski M, Macnar JM, Cymborowski M, Cooper DR, Shabalin IG, Gilski M, Brzezinski D, Kowiel M, Dauter Z, Rupp B, Wlodawer A, Jaskolski M, Minor W. Rapid response to emerging biomedical challenges and threats. IUCRJ 2021; 8:395-407. [PMID: 33953926 PMCID: PMC8086160 DOI: 10.1107/s2052252521003018] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/11/2021] [Accepted: 03/22/2021] [Indexed: 05/13/2023]
Abstract
As part of the global mobilization to combat the present pandemic, almost 100 000 COVID-19-related papers have been published and nearly a thousand models of macromolecules encoded by SARS-CoV-2 have been deposited in the Protein Data Bank within less than a year. The avalanche of new structural data has given rise to multiple resources dedicated to assessing the correctness and quality of structural data and models. Here, an approach to evaluate the massive amounts of such data using the resource https://covid19.bioreproducibility.org is described, which offers a template that could be used in large-scale initiatives undertaken in response to future biomedical crises. Broader use of the described methodology could considerably curtail information noise and significantly improve the reproducibility of biomedical research.
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Affiliation(s)
- Marek Grabowski
- Department of Molecular Physiology and Biological Physics, University of Virginia, Charlottesville, Virginia, USA
| | - Joanna M. Macnar
- Department of Molecular Physiology and Biological Physics, University of Virginia, Charlottesville, Virginia, USA
- College of Inter-Faculty Individual Studies in Mathematics and Natural Sciences, University of Warsaw, Warsaw, Poland
- Faculty of Chemistry, Biological and Chemical Research Center, University of Warsaw, Warsaw, Poland
| | - Marcin Cymborowski
- Department of Molecular Physiology and Biological Physics, University of Virginia, Charlottesville, Virginia, USA
| | - David R. Cooper
- Department of Molecular Physiology and Biological Physics, University of Virginia, Charlottesville, Virginia, USA
| | - Ivan G. Shabalin
- Department of Molecular Physiology and Biological Physics, University of Virginia, Charlottesville, Virginia, USA
| | - Miroslaw Gilski
- Department of Crystallography, Faculty of Chemistry, A. Mickiewicz University, Poznan, Poland
- Center for Biocrystallographic Research, Institute of Bioorganic Chemistry, Polish Academy of Sciences, Poznan, Poland
| | - Dariusz Brzezinski
- Department of Molecular Physiology and Biological Physics, University of Virginia, Charlottesville, Virginia, USA
- Center for Biocrystallographic Research, Institute of Bioorganic Chemistry, Polish Academy of Sciences, Poznan, Poland
- Institute of Computing Science, Poznan University of Technology, Poznan, Poland
| | - Marcin Kowiel
- Center for Biocrystallographic Research, Institute of Bioorganic Chemistry, Polish Academy of Sciences, Poznan, Poland
| | - Zbigniew Dauter
- Center for Structural Biology, National Cancer Institute, Frederick, Maryland, USA
| | - Bernhard Rupp
- k.-k Hofkristallamt, San Diego, California, USA
- Institute of Genetic Epidemiology, Medical University Innsbruck, Innsbruck, Austria
| | - Alexander Wlodawer
- Center for Structural Biology, National Cancer Institute, Frederick, Maryland, USA
| | - Mariusz Jaskolski
- Department of Crystallography, Faculty of Chemistry, A. Mickiewicz University, Poznan, Poland
- Center for Biocrystallographic Research, Institute of Bioorganic Chemistry, Polish Academy of Sciences, Poznan, Poland
| | - Wladek Minor
- Department of Molecular Physiology and Biological Physics, University of Virginia, Charlottesville, Virginia, USA
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11
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Brzezinski D, Kowiel M, Cooper DR, Cymborowski M, Grabowski M, Wlodawer A, Dauter Z, Shabalin IG, Gilski M, Rupp B, Jaskolski M, Minor W. Covid-19.bioreproducibility.org: A web resource for SARS-CoV-2-related structural models. Protein Sci 2021; 30:115-124. [PMID: 32981130 PMCID: PMC7537053 DOI: 10.1002/pro.3959] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2020] [Revised: 09/23/2020] [Accepted: 09/25/2020] [Indexed: 12/15/2022]
Abstract
The COVID-19 pandemic has triggered numerous scientific activities aimed at understanding the SARS-CoV-2 virus and ultimately developing treatments. Structural biologists have already determined hundreds of experimental X-ray, cryo-EM, and NMR structures of proteins and nucleic acids related to this coronavirus, and this number is still growing. To help biomedical researchers, who may not necessarily be experts in structural biology, navigate through the flood of structural models, we have created an online resource, covid19.bioreproducibility.org, that aggregates expert-verified information about SARS-CoV-2-related macromolecular models. In this article, we describe this web resource along with the suite of tools and methodologies used for assessing the structures presented therein.
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Affiliation(s)
- Dariusz Brzezinski
- Department of Molecular Physiology and Biological PhysicsUniversity of VirginiaCharlottesvilleVirginiaUSA
- Center for Biocrystallographic Research, Institute of Bioorganic ChemistryPolish Academy of SciencesPoznanPoland
- Institute of Computing SciencePoznan University of TechnologyPoznanPoland
| | - Marcin Kowiel
- Center for Biocrystallographic Research, Institute of Bioorganic ChemistryPolish Academy of SciencesPoznanPoland
| | - David R. Cooper
- Department of Molecular Physiology and Biological PhysicsUniversity of VirginiaCharlottesvilleVirginiaUSA
| | - Marcin Cymborowski
- Department of Molecular Physiology and Biological PhysicsUniversity of VirginiaCharlottesvilleVirginiaUSA
| | - Marek Grabowski
- Department of Molecular Physiology and Biological PhysicsUniversity of VirginiaCharlottesvilleVirginiaUSA
| | - Alexander Wlodawer
- Macromolecular Crystallography Laboratory, National Cancer InstituteFrederickMarylandUSA
| | - Zbigniew Dauter
- Macromolecular Crystallography Laboratory, National Cancer InstituteFrederickMarylandUSA
| | - Ivan G. Shabalin
- Department of Molecular Physiology and Biological PhysicsUniversity of VirginiaCharlottesvilleVirginiaUSA
| | - Miroslaw Gilski
- Center for Biocrystallographic Research, Institute of Bioorganic ChemistryPolish Academy of SciencesPoznanPoland
- Department of Crystallography, Faculty of ChemistryAdam Mickiewicz UniversityPoznanPoland
| | - Bernhard Rupp
- k.‐k. HofkristallamtSan DiegoCaliforniaUSA
- Institute of Genetic EpidemiologyMedical University InnsbruckSchöpfstr. 41InnsbruckTyrol6020Austria
| | - Mariusz Jaskolski
- Center for Biocrystallographic Research, Institute of Bioorganic ChemistryPolish Academy of SciencesPoznanPoland
- Department of Crystallography, Faculty of ChemistryAdam Mickiewicz UniversityPoznanPoland
| | - Wladek Minor
- Department of Molecular Physiology and Biological PhysicsUniversity of VirginiaCharlottesvilleVirginiaUSA
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12
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Auffinger P, Ennifar E, D'Ascenzo L. Deflating the RNA Mg 2+ bubble. Stereochemistry to the rescue! RNA (NEW YORK, N.Y.) 2020; 27:rna.076067.120. [PMID: 33268500 PMCID: PMC7901845 DOI: 10.1261/rna.076067.120] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/25/2020] [Accepted: 11/20/2020] [Indexed: 05/03/2023]
Abstract
Proper evaluation of the ionic structure of biomolecular systems through X ray and cryo-EM techniques remains challenging but is essential for advancing our understanding of the underlying structure/activity/solvent relationships. However, numerous studies overestimate the number of Mg2+ in deposited structures due to assignment errors finding their origin in improper consideration of stereochemical rules. Herein, to tackle such issues, we re-evaluate the PDBid 6QNR and 6SJ6 models of the ribosome ionic structure. We establish that stereochemical principles need to be carefully pondered when evaluating ion binding features, even when K+ anomalous signals are available as it is the case for the 6QNR PDB entry. For ribosomes, assignment errors can result in misleading conceptions of their solvent structure. For instance, present stereochemical analysis result in a significant decrease of the number of assigned Mg2+ in 6QNR, suggesting that K+ and not Mg2+ is the prevalent ion in the ribosome 1st solvation shell. We stress that the use of proper stereochemical guidelines in combination or not with other identification techniques, such as those pertaining to the detection of transition metals, of some anions and of K+ anomalous signals, is critical for deflating the current Mg2+ bubble witnessed in many ribosome and other RNA structures. We also stress that for the identification of lighter ions such as Mg2+, Na+, …, for which no anomalous signals can be detected, stereochemistry coupled with high resolution structures (<2.4 Å) remain the best currently available option.
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13
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Brzezinski D, Dauter Z, Minor W, Jaskolski M. On the evolution of the quality of macromolecular models in the PDB. FEBS J 2020; 287:2685-2698. [PMID: 32311227 PMCID: PMC7340579 DOI: 10.1111/febs.15314] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2019] [Revised: 03/02/2020] [Accepted: 03/26/2020] [Indexed: 01/06/2023]
Abstract
Crystallographic models of biological macromolecules have been ranked using the quality criteria associated with them in the Protein Data Bank (PDB). The outcomes of this quality analysis have been correlated with time and with the journals that published papers based on those models. The results show that the overall quality of PDB structures has substantially improved over the last ten years, but this period of progress was preceded by several years of stagnation or even depression. Moreover, the study shows that the historically observed negative correlation between journal impact and the quality of structural models presented therein seems to disappear as time progresses.
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Affiliation(s)
- Dariusz Brzezinski
- Center for Biocrystallographic ResearchInstitute of Bioorganic ChemistryPolish Academy of SciencesPoznanPoland
- Institute of Computing SciencePoznan University of TechnologyPoland
- Center for Artificial Intelligence and Machine LearningPoznan University of TechnologyPoland
- Department of Molecular Physiology and Biological PhysicsUniversity of VirginiaCharlottesvilleVAUSA
| | - Zbigniew Dauter
- Synchrotron Radiation Research SectionMacromolecular Crystallography LaboratoryNational Cancer InstituteArgonne National LaboratoryArgonneILUSA
| | - Wladek Minor
- Department of Molecular Physiology and Biological PhysicsUniversity of VirginiaCharlottesvilleVAUSA
| | - Mariusz Jaskolski
- Center for Biocrystallographic ResearchInstitute of Bioorganic ChemistryPolish Academy of SciencesPoznanPoland
- Department of CrystallographyFaculty of ChemistryA. Mickiewicz UniversityPoznanPoland
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14
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Farkas B, Csizmadia G, Katona E, Tusnády GE, Hegedűs T. MemBlob database and server for identifying transmembrane regions using cryo-EM maps. Bioinformatics 2020; 36:2595-2598. [PMID: 31290936 PMCID: PMC7178402 DOI: 10.1093/bioinformatics/btz539] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2018] [Revised: 05/31/2019] [Accepted: 07/09/2019] [Indexed: 11/14/2022] Open
Abstract
SUMMARY The identification of transmembrane helices in transmembrane proteins is crucial, not only to understand their mechanism of action but also to develop new therapies. While experimental data on the boundaries of membrane-embedded regions are sparse, this information is present in cryo-electron microscopy (cryo-EM) density maps and it has not been utilized yet for determining membrane regions. We developed a computational pipeline, where the inputs of a cryo-EM map, the corresponding atomistic structure, and the potential bilayer orientation determined by TMDET algorithm of a given protein result in an output defining the residues assigned to the bulk water phase, lipid interface and the lipid hydrophobic core. Based on this method, we built a database involving published cryo-EM protein structures and a server to be able to compute this data for newly obtained structures. AVAILABILITY AND IMPLEMENTATION http://memblob.hegelab.org. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Bianka Farkas
- Department of Biophysics and Radiation Biology, Semmelweis University, Budapest 1094, Hungary.,MTA-SE Molecular Biophysics Research Group, Hungarian Academy of Sciences, Budapest 1094, Hungary.,Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, Budapest 1083, Hungary
| | - Georgina Csizmadia
- Department of Biophysics and Radiation Biology, Semmelweis University, Budapest 1094, Hungary.,MTA-SE Molecular Biophysics Research Group, Hungarian Academy of Sciences, Budapest 1094, Hungary
| | - Eszter Katona
- Department of Biophysics and Radiation Biology, Semmelweis University, Budapest 1094, Hungary.,Faculty of Brain Sciences, University College London, London W1T 7NF, UK
| | - Gábor E Tusnády
- 'Momentum' Membrane Protein Bioinformatics Research Group, Institute of Enzymology, RCNS, Hungarian Academy of Sciences, 1117 Budapest, Hungary
| | - Tamás Hegedűs
- Department of Biophysics and Radiation Biology, Semmelweis University, Budapest 1094, Hungary.,MTA-SE Molecular Biophysics Research Group, Hungarian Academy of Sciences, Budapest 1094, Hungary
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15
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Grabowski M, Cymborowski M, Porebski PJ, Osinski T, Shabalin IG, Cooper DR, Minor W. The Integrated Resource for Reproducibility in Macromolecular Crystallography: Experiences of the first four years. STRUCTURAL DYNAMICS (MELVILLE, N.Y.) 2019; 6:064301. [PMID: 31768399 PMCID: PMC6874509 DOI: 10.1063/1.5128672] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/24/2019] [Accepted: 11/04/2019] [Indexed: 05/05/2023]
Abstract
It has been increasingly recognized that preservation and public accessibility of primary experimental data are cornerstones necessary for the reproducibility of empirical sciences. In the field of molecular crystallography, many journals now recommend that authors of manuscripts presenting a new crystal structure should deposit their primary experimental data (X-ray diffraction images) to one of the dedicated resources created in recent years. Here, we describe our experiences developing the Integrated Resource for Reproducibility in Molecular Crystallography (IRRMC) and describe several examples of a crucial role that diffraction data can play in improving previously determined protein structures. In its first four years, several hundred crystallographers have deposited data from over 5200 diffraction experiments performed at over 60 different synchrotron beamlines or home sources all over the world. In addition to improving the resource and curating submitted data, we have been building a pipeline for extraction or, in some cases, reconstruction of the metadata necessary for seamless automated processing. Preliminary analysis indicates that about 95% of the archived data can be automatically reprocessed. A high rate of reprocessing success shows the feasibility of using the automated metadata extraction and automated processing as a validation step for the deposition of raw diffraction images. The IRRMC is guided by the Findable, Accessible, Interoperable, and Reusable data management principles.
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Affiliation(s)
| | | | - Przemyslaw J. Porebski
- Department of Molecular Physiology and Biological Physics, University of Virginia, Charottesville, Virginia 22908, USA
| | - Tomasz Osinski
- Department of Molecular Physiology and Biological Physics, University of Virginia, Charottesville, Virginia 22908, USA
| | | | | | - Wladek Minor
- Authors to whom correspondence should be addressed: and
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16
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Porebski PJ, Bokota G, Venkataramany BS, Minor W. Molstack: A platform for interactive presentations of electron density and cryo-EM maps and their interpretations. Protein Sci 2019; 29:120-127. [PMID: 31605409 DOI: 10.1002/pro.3747] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2019] [Revised: 10/07/2019] [Accepted: 10/08/2019] [Indexed: 12/21/2022]
Abstract
In the Special Issue on Tools for Protein Science in 2018, we presented Molstack: a concept of a cloud-based platform for sharing electron density maps and their interpretations. Molstack is a web platform that allows the interactive visualization of density maps through the simultaneous presentation of multiple datasets and models in a way that allows for easy pairwise comparison. We anticipated that the users of this conceptually simple platform would find many different uses for their projects, and we were not mistaken. We have observed researchers use Molstack to present experimental evidence for their models in the form of electron density maps, omit maps, and anomalous difference density maps. Users also employed Molstack to present alternative interpretations of densities, including rerefinements and speculative interpretations. While we anticipated these types of projects to be the main use cases, we were pleased to see Molstack used to display superpositions of different models, as a tool for story-driven presentations, and for collaboration as well. Here, we present developments in the platform that were driven by user feedback, highlight several cases that used Molstack to enhance the publication, and discuss possible directions for the platform.
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Affiliation(s)
- Przemyslaw J Porebski
- Department of Molecular Physiology & Biological Physics, University of Virginia, Charlottesville, Virginia
| | - Grzegorz Bokota
- Department of Molecular Physiology & Biological Physics, University of Virginia, Charlottesville, Virginia.,Faculty of Mathematics, Informatics and Mechanics, University of Warsaw, Warsaw, Poland
| | - Barat S Venkataramany
- Department of Molecular Physiology & Biological Physics, University of Virginia, Charlottesville, Virginia
| | - Wladek Minor
- Department of Molecular Physiology & Biological Physics, University of Virginia, Charlottesville, Virginia
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17
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Leonarski F, D'Ascenzo L, Auffinger P. Nucleobase carbonyl groups are poor Mg 2+ inner-sphere binders but excellent monovalent ion binders-a critical PDB survey. RNA (NEW YORK, N.Y.) 2019; 25:173-192. [PMID: 30409785 PMCID: PMC6348993 DOI: 10.1261/rna.068437.118] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/23/2018] [Accepted: 10/16/2018] [Indexed: 05/04/2023]
Abstract
Precise knowledge of Mg2+ inner-sphere binding site properties is vital for understanding the structure and function of nucleic acid systems. Unfortunately, the PDB, which represents the main source of Mg2+ binding sites, contains a substantial number of assignment issues that blur our understanding of the functions of these ions. Here, following a previous study devoted to Mg2+ binding to nucleobase nitrogens, we surveyed nucleic acid X-ray structures from the PDB with resolutions ≤2.9 Å to classify the Mg2+ inner-sphere binding patterns to nucleotide carbonyl, ribose hydroxyl, cyclic ether, and phosphodiester oxygen atoms. From this classification, we derived a set of "prior-knowledge" nucleobase Mg2+ binding sites. We report that crystallographic examples of trustworthy nucleobase Mg2+ binding sites are fewer than expected since many of those are associated with misidentified Na+ or K+ We also emphasize that binding of Na+ and K+ to nucleic acids is much more frequent than anticipated. Overall, we provide evidence derived from X-ray structures that nucleobases are poor inner-sphere binders for Mg2+ but good binders for monovalent ions. Based on strict stereochemical criteria, we propose an extended set of guidelines designed to help in the assignment and validation of ions directly contacting nucleobase and ribose atoms. These guidelines should help in the interpretation of X-ray and cryo-EM solvent density maps. When borderline Mg2+ stereochemistry is observed, alternative placement of Na+, K+, or Ca2+ must be considered. We also critically examine the use of lanthanides (Yb3+, Tb3+) as Mg2+ substitutes in crystallography experiments.
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Affiliation(s)
- Filip Leonarski
- Swiss Light Source, Paul Scherrer Institut, Villigen PSI, 5232, Switzerland
- Architecture et Réactivité de l'ARN, Université de Strasbourg, Institut de Biologie Moléculaire et Cellulaire du CNRS, Strasbourg, 67084, France
| | - Luigi D'Ascenzo
- Architecture et Réactivité de l'ARN, Université de Strasbourg, Institut de Biologie Moléculaire et Cellulaire du CNRS, Strasbourg, 67084, France
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, California 92037, USA
| | - Pascal Auffinger
- Architecture et Réactivité de l'ARN, Université de Strasbourg, Institut de Biologie Moléculaire et Cellulaire du CNRS, Strasbourg, 67084, France
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18
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Jiang QX. Structural Variability in the RLR-MAVS Pathway and Sensitive Detection of Viral RNAs. Med Chem 2019; 15:443-458. [PMID: 30569868 PMCID: PMC6858087 DOI: 10.2174/1573406415666181219101613] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2018] [Revised: 10/23/2018] [Accepted: 12/12/2018] [Indexed: 12/25/2022]
Abstract
Cells need high-sensitivity detection of non-self molecules in order to fight against pathogens. These cellular sensors are thus of significant importance to medicinal purposes, especially for treating novel emerging pathogens. RIG-I-like receptors (RLRs) are intracellular sensors for viral RNAs (vRNAs). Their active forms activate mitochondrial antiviral signaling protein (MAVS) and trigger downstream immune responses against viral infection. Functional and structural studies of the RLR-MAVS signaling pathway have revealed significant supramolecular variability in the past few years, which revealed different aspects of the functional signaling pathway. Here I will discuss the molecular events of RLR-MAVS pathway from the angle of detecting single copy or a very low copy number of vRNAs in the presence of non-specific competition from cytosolic RNAs, and review key structural variability in the RLR / vRNA complexes, the MAVS helical polymers, and the adapter-mediated interactions between the active RLR / vRNA complex and the inactive MAVS in triggering the initiation of the MAVS filaments. These structural variations may not be exclusive to each other, but instead may reflect the adaptation of the signaling pathways to different conditions or reach different levels of sensitivity in its response to exogenous vRNAs.
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Affiliation(s)
- Qiu-Xing Jiang
- Department of Microbiology and Cell Science, University of Florida, Gainesville, FL 32611, United States
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19
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Shabalin IG, Porebski PJ, Minor W. Refining the macromolecular model - achieving the best agreement with the data from X-ray diffraction experiment. CRYSTALLOGR REV 2018; 24:236-262. [PMID: 30416256 DOI: 10.1080/0889311x.2018.1521805] [Citation(s) in RCA: 37] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
Abstract
Refinement of macromolecular X-ray crystal structures involves using complex software with hundreds of different settings. The complexity of underlying concepts and the sheer amount sof instructions may make it difficult for less experienced crystallographers to achieve optimal results in their refinements. This tutorial review offers guidelines for choosing the best settings for the reciprocal-space refinement of macromolecular models and provides practical tips for manual model correction. To help aspiring crystallographers navigate the process, some of the most practically important concepts of protein structure refinement are described. Among the topics covered are the use and purpose of R-free, geometrical restraints, restraints on atomic displacement parameters (ADPs), refinement weights, various parametrizations of ADPs (full anisotropic refinement and TLS), and omit maps. We also give practical tips for manual model correction in Coot, modelling of side-chains with poor or missing density, and ligand identification, fitting, and refinement.
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Affiliation(s)
- Ivan G Shabalin
- Department of Molecular Physiology and Biological Physics, University of Virginia, Charlottesville, VA 22908, United States.,Center for Structural Genomics of Infectious Diseases (CSGID), Charlottesville, VA, 22908, United States
| | - Przemyslaw J Porebski
- Department of Molecular Physiology and Biological Physics, University of Virginia, Charlottesville, VA 22908, United States.,Center for Structural Genomics of Infectious Diseases (CSGID), Charlottesville, VA, 22908, United States
| | - Wladek Minor
- Department of Molecular Physiology and Biological Physics, University of Virginia, Charlottesville, VA 22908, United States.,Center for Structural Genomics of Infectious Diseases (CSGID), Charlottesville, VA, 22908, United States
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