1
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Doukov T, Herschlag D, Yabukarski F. Obtaining anomalous and ensemble information from protein crystals from 220 K up to physiological temperatures. Acta Crystallogr D Struct Biol 2023; 79:212-223. [PMID: 36876431 PMCID: PMC9986799 DOI: 10.1107/s205979832300089x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2022] [Accepted: 01/31/2023] [Indexed: 03/01/2023] Open
Abstract
X-ray crystallography has been invaluable in delivering structural information about proteins. Previously, an approach has been developed that allows high-quality X-ray diffraction data to be obtained from protein crystals at and above room temperature. Here, this previous work is built on and extended by showing that high-quality anomalous signal can be obtained from single protein crystals using diffraction data collected at 220 K up to physiological temperatures. The anomalous signal can be used to directly determine the structure of a protein, i.e. to phase the data, as is routinely performed under cryoconditions. This ability is demonstrated by obtaining diffraction data from model lysozyme, thaumatin and proteinase K crystals, the anomalous signal from which allowed their structures to be solved experimentally at 7.1 keV X-ray energy and at room temperature with relatively low data redundancy. It is also demonstrated that the anomalous signal from diffraction data obtained at 310 K (37°C) can be used to solve the structure of proteinase K and to identify ordered ions. The method provides useful anomalous signal at temperatures down to 220 K, resulting in an extended crystal lifetime and increased data redundancy. Finally, we show that useful anomalous signal can be obtained at room temperature using X-rays of 12 keV energy as typically used for routine data collection, allowing this type of experiment to be carried out at widely accessible synchrotron beamline energies and enabling the simultaneous extraction of high-resolution data and anomalous signal. With the recent emphasis on obtaining conformational ensemble information for proteins, the high resolution of the data allows such ensembles to be built, while the anomalous signal allows the structure to be experimentally solved, ions to be identified, and water molecules and ions to be differentiated. Because bound metal-, phosphorus- and sulfur-containing ions all have anomalous signal, obtaining anomalous signal across temperatures and up to physiological temperatures will provide a more complete description of protein conformational ensembles, function and energetics.
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Affiliation(s)
- Tzanko Doukov
- SMB, Stanford Synchrotron Radiation Lightsource, SLAC National Accelerator Laboratory, 2575 Sand Hill Road, Menlo Park, CA 94025, USA
| | - Daniel Herschlag
- Deparment of Biochemistry, Stanford University, Stanford, CA 94305, USA
- Department of Chemical Engineering, Stanford University, Stanford, CA 94305, USA
- Stanford ChEM-H, Stanford University, Stanford, CA 94305, USA
| | - Filip Yabukarski
- Deparment of Biochemistry, Stanford University, Stanford, CA 94305, USA
- Bristol-Myers Squibb, San Diego, CA 92121, USA
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2
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Gucwa M, Lenkiewicz J, Zheng H, Cymborowski M, Cooper DR, Murzyn K, Minor W. CMM-An enhanced platform for interactive validation of metal binding sites. Protein Sci 2023; 32:e4525. [PMID: 36464767 PMCID: PMC9794025 DOI: 10.1002/pro.4525] [Citation(s) in RCA: 18] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2022] [Revised: 11/21/2022] [Accepted: 11/22/2022] [Indexed: 12/12/2022]
Abstract
Metal ions bound to macromolecules play an integral role in many cellular processes. They can directly participate in catalytic mechanisms or be essential for the structural integrity of proteins and nucleic acids. However, their unique nature in macromolecules can make them difficult to model and refine, and a substantial portion of metal ions in the PDB are misidentified or poorly refined. CheckMyMetal (CMM) is a validation tool that has gained widespread acceptance as an essential tool for researchers working on metal-macromolecule complexes. CMM can be used during structure determination or to validate metal binding sites in structural models within the PDB. The functionalities of CMM have recently been greatly enhanced and provide researchers with additional information that can guide modeling decisions. The new version of CMM shows metals in the context of electron density maps and allows for on-the-fly refinement of metal binding sites. The improvements should increase the reproducibility of biomedical research. The web server is available at https://cmm.minorlab.org.
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Affiliation(s)
- Michal Gucwa
- Department of Molecular Physiology and Biological PhysicsUniversity of VirginiaCharlottesvilleVirginiaUSA,Department of Computational Biophysics and BioinformaticsJagiellonian UniversityKrakowPoland
| | - Joanna Lenkiewicz
- Department of Molecular Physiology and Biological PhysicsUniversity of VirginiaCharlottesvilleVirginiaUSA
| | - Heping Zheng
- Department of Molecular Physiology and Biological PhysicsUniversity of VirginiaCharlottesvilleVirginiaUSA,Present address:
Hunan University College of BiologyBioinformatics CenterHunanPeople's Republic of China
| | - Marcin Cymborowski
- Department of Molecular Physiology and Biological PhysicsUniversity of VirginiaCharlottesvilleVirginiaUSA
| | - David R. Cooper
- Department of Molecular Physiology and Biological PhysicsUniversity of VirginiaCharlottesvilleVirginiaUSA
| | - Krzysztof Murzyn
- Department of Computational Biophysics and BioinformaticsJagiellonian UniversityKrakowPoland
| | - Wladek Minor
- Department of Molecular Physiology and Biological PhysicsUniversity of VirginiaCharlottesvilleVirginiaUSA
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3
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Raush E, Abagyan R, Totrov M. Graph-Convolutional Neural Net Model of the Statistical Torsion Profiles for Small Organic Molecules. J Chem Inf Model 2022; 62:5896-5906. [PMID: 36456533 DOI: 10.1021/acs.jcim.2c00790] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/03/2022]
Abstract
We present a graph-convolutional neural network (GCNN)-based method for learning and prediction of statistical torsional profiles (STP) in small organic molecules based on the experimental X-ray structure data. A specialized GCNN torsion profile model is trained using the structures in the Crystallography Open Database (COD). The GCNN-STP model captures torsional preferences over a wide range of torsion rotor chemotypes and correctly predicts a variety of effects from the vicinal atoms and moieties. GCNN-STP statistical profiles also show good agreement with quantum chemically (DFT) calculated torsion energy profiles. Furthermore, we demonstrate the application of the GCNN-STP statistical profiles for conformer generation. A web server that allows interactive profile prediction and viewing is made freely available at https://www.molsoft.com/tortool.html.
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Affiliation(s)
- Eugene Raush
- Molsoft L.L.C., 11199 Sorrento Valley Road, S209, San Diego, California92121, United States
| | - Ruben Abagyan
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, California92093, United States
| | - Maxim Totrov
- Molsoft L.L.C., 11199 Sorrento Valley Road, S209, San Diego, California92121, United States
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4
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Song L, Merceron R, Hulpia F, Lucía A, Gracia B, Jian Y, Risseeuw MDP, Verstraelen T, Cos P, Aínsa JA, Boshoff HI, Munier-Lehmann H, Savvides SN, Van Calenbergh S. Structure-aided optimization of non-nucleoside M. tuberculosis thymidylate kinase inhibitors. Eur J Med Chem 2021; 225:113784. [PMID: 34450493 PMCID: PMC10500704 DOI: 10.1016/j.ejmech.2021.113784] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2021] [Revised: 08/14/2021] [Accepted: 08/14/2021] [Indexed: 10/20/2022]
Abstract
Mycobacterium tuberculosis thymidylate kinase (MtTMPK) has emerged as an attractive target for rational drug design. We recently investigated new families of non-nucleoside MtTMPK inhibitors in an effort to diversify MtTMPK inhibitor chemical space. We here report a new series of MtTMPK inhibitors by combining the Topliss scheme with rational drug design approaches, fueled by two co-crystal structures of MtTMPK in complex with developed inhibitors. These efforts furnished the most potent MtTMPK inhibitors in our assay, with two analogues displaying low micromolar MIC values against H37Rv Mtb. Prepared inhibitors address new sub-sites in the MtTMPK nucleotide binding pocket, thereby offering new insights into its druggability. We studied the role of efflux pumps as well as the impact of cell wall permeabilizers for selected compounds to potentially provide an explanation for the lack of correlation between potent enzyme inhibition and whole-cell activity.
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Affiliation(s)
- Lijun Song
- Laboratory for Medicinal Chemistry (FFW), Ghent University, Tergestensis 460, B-9000, Gent, Belgium; 3M, Zwijndrecht, Belgium
| | - Romain Merceron
- VIB Center for Inflammation Research, Zwijnaarde, Ghent, 9052, Belgium; Department of Biochemistry and Microbiology, Ghent University, Technologiepark 927, 9052, Zwijnaarde, Ghent, Belgium; Eurofins Group, Poitiers, France
| | - Fabian Hulpia
- Laboratory for Medicinal Chemistry (FFW), Ghent University, Tergestensis 460, B-9000, Gent, Belgium; Janssen Pharmaceutica, Beerse, Belgium
| | - Ainhoa Lucía
- Grupo de Genética de Micobacterias, Departamento de Microbiología, Facultad de Medicina, and BIFI, Universidad de Zaragoza, Zaragoza, Spain; CIBER Enfermedades Respiratorias (CIBERES), Instituto de Salud Carlos III, 28029, Madrid, Spain
| | - Begoña Gracia
- Grupo de Genética de Micobacterias, Departamento de Microbiología, Facultad de Medicina, and BIFI, Universidad de Zaragoza, Zaragoza, Spain; CIBER Enfermedades Respiratorias (CIBERES), Instituto de Salud Carlos III, 28029, Madrid, Spain
| | - Yanlin Jian
- Laboratory for Medicinal Chemistry (FFW), Ghent University, Tergestensis 460, B-9000, Gent, Belgium
| | - Martijn D P Risseeuw
- Laboratory for Medicinal Chemistry (FFW), Ghent University, Tergestensis 460, B-9000, Gent, Belgium
| | - Toon Verstraelen
- Center for Melecular Modeling, Ghent University, Zwijnaarde, Ghent, 9052, Belgium
| | - Paul Cos
- Laboratory for Microbiology, Parasitology and Hygiene (LMPH), Department of Pharmaceutical Sciences, University of Antwerp, Campus Drie Eiken, Universiteitsplein 1, B-2610, Antwerpen, Belgium
| | - José A Aínsa
- Grupo de Genética de Micobacterias, Departamento de Microbiología, Facultad de Medicina, and BIFI, Universidad de Zaragoza, Zaragoza, Spain; CIBER Enfermedades Respiratorias (CIBERES), Instituto de Salud Carlos III, 28029, Madrid, Spain
| | - Helena I Boshoff
- Tuberculosis Research Section, Laboratory of Clinical Immunology and Microbiology, National Institute of Allergy and Infectious Disease, National Institutes of Health, 9000 Rockville Pike, Bethesda, MD, 20892, United States
| | - Hélène Munier-Lehmann
- CNRS UMR3523, Department of Structural Biology and Chemistry, Institut Pasteur, 75724, Paris Cedex 15, France
| | - Savvas N Savvides
- VIB Center for Inflammation Research, Zwijnaarde, Ghent, 9052, Belgium; Department of Biochemistry and Microbiology, Ghent University, Technologiepark 927, 9052, Zwijnaarde, Ghent, Belgium
| | - Serge Van Calenbergh
- Laboratory for Medicinal Chemistry (FFW), Ghent University, Tergestensis 460, B-9000, Gent, Belgium.
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5
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Bradford SYC, El Khoury L, Ge Y, Osato M, Mobley DL, Fischer M. Temperature artifacts in protein structures bias ligand-binding predictions. Chem Sci 2021; 12:11275-11293. [PMID: 34667539 PMCID: PMC8447925 DOI: 10.1039/d1sc02751d] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2021] [Accepted: 07/09/2021] [Indexed: 12/14/2022] Open
Abstract
X-ray crystallography is the gold standard to resolve conformational ensembles that are significant for protein function, ligand discovery, and computational methods development. However, relevant conformational states may be missed at common cryogenic (cryo) data-collection temperatures but can be populated at room temperature. To assess the impact of temperature on making structural and computational discoveries, we systematically investigated protein conformational changes in response to temperature and ligand binding in a structural and computational workhorse, the T4 lysozyme L99A cavity. Despite decades of work on this protein, shifting to RT reveals new global and local structural changes. These include uncovering an apo helix conformation that is hidden at cryo but relevant for ligand binding, and altered side chain and ligand conformations. To evaluate the impact of temperature-induced protein and ligand changes on the utility of structural information in computation, we evaluated how temperature can mislead computational methods that employ cryo structures for validation. We find that when comparing simulated structures just to experimental cryo structures, hidden successes and failures often go unnoticed. When using structural information in ligand binding predictions, both coarse docking and rigorous binding free energy calculations are influenced by temperature effects. The trend that cryo artifacts limit the utility of structures for computation holds across five distinct protein classes. Our results suggest caution when consulting cryogenic structural data alone, as temperature artifacts can conceal errors and prevent successful computational predictions, which can mislead the development and application of computational methods in discovering bioactive molecules.
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Affiliation(s)
- Shanshan Y C Bradford
- Department of Chemical Biology & Therapeutics, St. Jude Children's Research Hospital Memphis TN 38105 USA
| | - Léa El Khoury
- Department of Pharmaceutical Sciences, University of California Irvine CA 92697 USA
| | - Yunhui Ge
- Department of Pharmaceutical Sciences, University of California Irvine CA 92697 USA
| | - Meghan Osato
- Department of Pharmaceutical Sciences, University of California Irvine CA 92697 USA
| | - David L Mobley
- Department of Pharmaceutical Sciences, University of California Irvine CA 92697 USA
- Department of Chemistry, University of California Irvine CA 92697 USA
| | - Marcus Fischer
- Department of Chemical Biology & Therapeutics, St. Jude Children's Research Hospital Memphis TN 38105 USA
- Department of Structural Biology, St. Jude Children's Research Hospital Memphis TN 38105 USA
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6
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Andersson I, Carlsson GH, Hasse D. Structural Analysis of Strigolactone-Related Gene Products. Methods Mol Biol 2021; 2309:245-257. [PMID: 34028692 DOI: 10.1007/978-1-0716-1429-7_19] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
Structural knowledge of biological macromolecules is essential for understanding their function and for modifying that function by engineering. Protein crystallography is a powerful method for elucidating molecular structures of proteins, but it is essential that the investigator has a basic knowledge of good practices and of the major pitfalls in the technique. Here we describe issues specific for the case of structural studies of strigolactone (SL) receptor structure and function, and in particular the difficulties associated with capturing complexes of SL receptors with the SL hormone ligand in the crystal.
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Affiliation(s)
- Inger Andersson
- Laboratory of Molecular Biophysics, Department of Cell and Molecular Biology, Uppsala University, Uppsala, Sweden. .,Arctic University of Norway, Tromsø, Norway. .,Institute of Biotechnology of the Czech Academy of Sciences, BIOCEV, Vestec, Czech Republic.
| | - Gunilla H Carlsson
- Laboratory of Molecular Biophysics, Department of Cell and Molecular Biology, Uppsala University, Uppsala, Sweden
| | - Dirk Hasse
- Laboratory of Molecular Biophysics, Department of Cell and Molecular Biology, Uppsala University, Uppsala, Sweden
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7
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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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8
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' All That Glitters Is Not Gold': High-Resolution Crystal Structures of Ligand-Protein Complexes Need Not Always Represent Confident Binding Poses. Int J Mol Sci 2021; 22:ijms22136830. [PMID: 34202053 PMCID: PMC8268033 DOI: 10.3390/ijms22136830] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2021] [Revised: 05/24/2021] [Accepted: 05/24/2021] [Indexed: 01/09/2023] Open
Abstract
Our understanding of the structure–function relationships of biomolecules and thereby applying it to drug discovery programs are substantially dependent on the availability of the structural information of ligand–protein complexes. However, the correct interpretation of the electron density of a small molecule bound to a crystal structure of a macromolecule is not trivial. Our analysis involving quality assessment of ~0.28 million small molecule–protein binding site pairs derived from crystal structures corresponding to ~66,000 PDB entries indicates that the majority (65%) of the pairs might need little (54%) or no (11%) attention. Out of the remaining 35% of pairs that need attention, 11% of the pairs (including structures with high/moderate resolution) pose serious concerns. Unfortunately, most users of crystal structures lack the training to evaluate the quality of a crystal structure against its experimental data and, in general, rely on the resolution as a ‘gold standard’ quality metric. Our work aims to sensitize the non-crystallographers that resolution, which is a global quality metric, need not be an accurate indicator of local structural quality. In this article, we demonstrate the use of several freely available tools that quantify local structural quality and are easy to use from a non-crystallographer’s perspective. We further propose a few solutions for consideration by the scientific community to promote quality research in structural biology and applied areas.
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9
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Jaskolski M, Dauter Z, Shabalin IG, Gilski M, Brzezinski D, Kowiel M, Rupp B, Wlodawer A. Crystallographic models of SARS-CoV-2 3CL pro: in-depth assessment of structure quality and validation. IUCRJ 2021; 8:238-256. [PMID: 33708401 PMCID: PMC7924243 DOI: 10.1107/s2052252521001159] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/11/2021] [Accepted: 02/01/2021] [Indexed: 05/26/2023]
Abstract
The appearance at the end of 2019 of the new SARS-CoV-2 coronavirus led to an unprecedented response by the structural biology community, resulting in the rapid determination of many hundreds of structures of proteins encoded by the virus. As part of an effort to analyze and, if necessary, remediate these structures as deposited in the Protein Data Bank (PDB), this work presents a detailed analysis of 81 crystal structures of the main protease 3CLpro, an important target for the design of drugs against COVID-19. The structures of the unliganded enzyme and its complexes with a number of inhibitors were determined by multiple research groups using different experimental approaches and conditions; the resulting structures span 13 different polymorphs representing seven space groups. The structures of the enzyme itself, all determined by molecular replacement, are highly similar, with the exception of one polymorph with a different inter-domain orientation. However, a number of complexes with bound inhibitors were found to pose significant problems. Some of these could be traced to faulty definitions of geometrical restraints for ligands and to the general problem of a lack of such information in the PDB depositions. Several problems with ligand definition in the PDB itself were also noted. In several cases extensive corrections to the models were necessary to adhere to the evidence of the electron-density maps. Taken together, this analysis of a large number of structures of a single, medically important protein, all determined within less than a year using modern experimental tools, should be useful in future studies of other systems of high interest to the biomedical community.
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Affiliation(s)
- 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
| | - Zbigniew Dauter
- Center for Structural Biology, National Cancer Institute, Frederick, MD 21702, USA
| | - Ivan G. Shabalin
- Department of Molecular Physiology and Biological Physics, University of Virginia, Charlottesville, VA 22908, 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
- 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
| | - Bernhard Rupp
- k.-k Hofkristallamt, San Diego, CA 92084, USA
- Institute of Genetic Epidemiology, Medical University Innsbruck, A-6020 Innsbruck, Austria
| | - Alexander Wlodawer
- Center for Structural Biology, National Cancer Institute, Frederick, MD 21702, USA
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10
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Grabowski M, Cooper DR, Brzezinski D, Macnar JM, Shabalin IG, Cymborowski M, Otwinowski Z, Minor W. Synchrotron Radiation as a Tool for Macromolecular X-Ray Crystallography: a XXI Century Perspective. NUCLEAR INSTRUMENTS & METHODS IN PHYSICS RESEARCH. SECTION B, BEAM INTERACTIONS WITH MATERIALS AND ATOMS 2021; 489:30-40. [PMID: 33603257 PMCID: PMC7886262 DOI: 10.1016/j.nimb.2020.12.016] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Intense X-rays available at powerful synchrotron beamlines provide macromolecular crystallographers with an incomparable tool for investigating biological phenomena on an atomic scale. The resulting insights into the mechanism's underlying biological processes have played an essential role and shaped biomedical sciences during the last 30 years, considered the "golden age" of structural biology. In this review, we analyze selected aspects of the impact of synchrotron radiation on structural biology. Synchrotron beamlines have been used to determine over 70% of all macromolecular structures deposited into the Protein Data Bank (PDB). These structures were deposited by over 13,000 different research groups. Interestingly, despite the impressive advances in synchrotron technologies, the median resolution of macromolecular structures determined using synchrotrons has remained constant throughout the last 30 years, at about 2 Å. Similarly, the median times from the data collection to the deposition and release have not changed significantly. We describe challenges to reproducibility related to recording all relevant data and metadata during the synchrotron experiments, including diffraction images. Finally, we discuss some of the recent opinions suggesting a diminishing importance of X-ray crystallography due to impressive advances in Cryo-EM and theoretical modeling. We believe that synchrotrons of the future will increasingly evolve towards a life science center model, where X-ray crystallography, Cryo-EM, and other experimental and computational resources and knowledge are encompassed within a versatile research facility. The recent response of crystallographers to the COVID-19 pandemic suggests that X-ray crystallography conducted at synchrotron beamlines will continue to play an essential role in structural biology and drug discovery for years to come.
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Affiliation(s)
- Marek Grabowski
- Department of Molecular Physiology and Biological Physics, University of Virginia, Charlottesville, VA22903, USA
| | - David R. Cooper
- Department of Molecular Physiology and Biological Physics, University of Virginia, Charlottesville, VA22903, USA
| | - Dariusz Brzezinski
- Department of Molecular Physiology and Biological Physics, University of Virginia, Charlottesville, VA22903, 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
| | - Joanna M. Macnar
- 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
| | - Ivan G. Shabalin
- Department of Molecular Physiology and Biological Physics, University of Virginia, Charlottesville, VA22903, USA
| | - Marcin Cymborowski
- Department of Molecular Physiology and Biological Physics, University of Virginia, Charlottesville, VA22903, USA
| | - Zbyszek Otwinowski
- Department of Biophysics, The University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
| | - Wladek Minor
- Department of Molecular Physiology and Biological Physics, University of Virginia, Charlottesville, VA22903, USA
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11
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Martynowycz MW, Gonen T. Ligand Incorporation into Protein Microcrystals for MicroED by On-Grid Soaking. Structure 2020; 29:88-95.e2. [PMID: 33007196 DOI: 10.1016/j.str.2020.09.003] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2020] [Revised: 08/29/2020] [Accepted: 09/15/2020] [Indexed: 11/17/2022]
Abstract
A high throughout method for soaking ligands into protein microcrystals on TEM grids is presented. Every crystal on the grid is soaked simultaneously using only standard cryoelectron microscopy vitrification equipment. The method is demonstrated using proteinase K microcrystals soaked with the 5-amino-2,4,6-triodoisophthalic acid (I3C) magic triangle. A soaked microcrystal is milled to a thickness of approximately 200 nm using a focused ion beam, and MicroED data are collected. A high-resolution structure of the protein with four ligands at high occupancy is determined. Both the number of ligands bound and their occupancy is higher using on-grid soaking of microcrystals compared with much larger crystals treated similarly and investigated by X-ray crystallography. These results indicate that on-grid soaking ligands into microcrystals results in efficient uptake of ligands into protein microcrystals.
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Affiliation(s)
- Michael W Martynowycz
- Department of Biological Chemistry, University of California Los Angeles, 615 Charles E Young Drive South, Los Angeles, CA 90095, USA; Department of Physiology, University of California Los Angeles, 615 Charles E Young Drive South, Los Angeles, CA 90095, USA; Howard Hughes Medical Institute, University of California Los Angeles, Los Angeles CA90095, USA
| | - Tamir Gonen
- Department of Biological Chemistry, University of California Los Angeles, 615 Charles E Young Drive South, Los Angeles, CA 90095, USA; Department of Physiology, University of California Los Angeles, 615 Charles E Young Drive South, Los Angeles, CA 90095, USA; Howard Hughes Medical Institute, University of California Los Angeles, Los Angeles CA90095, USA.
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12
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Fusani L, Palmer DS, Somers DO, Wall ID. Exploring Ligand Stability in Protein Crystal Structures Using Binding Pose Metadynamics. J Chem Inf Model 2020; 60:1528-1539. [PMID: 31910338 PMCID: PMC7145342 DOI: 10.1021/acs.jcim.9b00843] [Citation(s) in RCA: 51] [Impact Index Per Article: 12.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
![]()
Identification of
correct protein–ligand binding poses is
important in structure-based drug design and crucial for the evaluation
of protein–ligand binding affinity. Protein–ligand coordinates are commonly obtained from
crystallography experiments that provide a static model of an ensemble
of conformations. Binding pose metadynamics (BPMD) is an enhanced
sampling method that allows for an efficient assessment of ligand
stability in solution. Ligand poses that are unstable under the bias
of the metadynamics simulation are expected to be infrequently occupied
in the energy landscape, thus making minimal contributions to the
binding affinity. Here, the robustness of the method is studied using
crystal structures with ligands known to be incorrectly modeled, as
well as 63 structurally diverse crystal structures with ligand fit
to electron density from the Twilight database. Results show that
BPMD can successfully differentiate compounds whose binding pose is
not supported by the electron density from those with well-defined
electron density.
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Affiliation(s)
- Lucia Fusani
- Molecular Design UK, GSK Medicines Research Centre, Gunnels Wood Road, Stevenage, Hertfordshire SG1 2NY, U.K.,Department of Pure and Applied Chemistry, University of Strathclyde, 295 Cathedral Street, Glasgow G11XL, U.K
| | - David S Palmer
- Department of Pure and Applied Chemistry, University of Strathclyde, 295 Cathedral Street, Glasgow G11XL, U.K
| | - Don O Somers
- Protein, Cellular and Structural Sciences, GSK Medicines Research Centre, Gunnels Wood Road, Stevenage, Hertfordshire SG1 2NY, U.K
| | - Ian D Wall
- Molecular Design UK, GSK Medicines Research Centre, Gunnels Wood Road, Stevenage, Hertfordshire SG1 2NY, U.K
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13
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Kowiel M, Brzezinski D, Porebski PJ, Shabalin IG, Jaskolski M, Minor W. Automatic recognition of ligands in electron density by machine learning. Bioinformatics 2019; 35:452-461. [PMID: 30016407 DOI: 10.1093/bioinformatics/bty626] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2018] [Accepted: 07/12/2018] [Indexed: 11/13/2022] Open
Abstract
Motivation The correct identification of ligands in crystal structures of protein complexes is the cornerstone of structure-guided drug design. However, cognitive bias can sometimes mislead investigators into modeling fictitious compounds without solid support from the electron density maps. Ligand identification can be aided by automatic methods, but existing approaches are based on time-consuming iterative fitting. Results Here we report a new machine learning algorithm called CheckMyBlob that identifies ligands from experimental electron density maps. In benchmark tests on portfolios of up to 219 931 ligand binding sites containing the 200 most popular ligands found in the Protein Data Bank, CheckMyBlob markedly outperforms the existing automatic methods for ligand identification, in some cases doubling the recognition rates, while requiring significantly less time. Our work shows that machine learning can improve the automation of structure modeling and significantly accelerate the drug screening process of macromolecule-ligand complexes. Availability and implementation Code and data are available on GitHub at https://github.com/dabrze/CheckMyBlob. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Marcin Kowiel
- Center for Biocrystallographic Research, Institute of Bioorganic Chemistry, Polish Academy of Sciences, Poznan, Poland.,Department of Molecular Physiology and Biological Physics, University of Virginia, Charlottesville, VA, USA
| | - Dariusz Brzezinski
- Department of Molecular Physiology and Biological Physics, University of Virginia, Charlottesville, VA, USA.,Institute of Computing Science, Poznan University of Technology, Poznan, Poland
| | - Przemyslaw J Porebski
- Department of Molecular Physiology and Biological Physics, University of Virginia, Charlottesville, VA, USA.,Center for Structural Genomics of Infectious Diseases (CSGID), University of Virginia, Charlottesville, VA, USA
| | - Ivan G Shabalin
- Department of Molecular Physiology and Biological Physics, University of Virginia, Charlottesville, VA, USA.,Center for Structural Genomics of Infectious Diseases (CSGID), University of Virginia, Charlottesville, VA, USA
| | - Mariusz Jaskolski
- Center for Biocrystallographic Research, Institute of Bioorganic Chemistry, Polish Academy of Sciences, Poznan, Poland.,Department of Crystallography, Faculty of Chemistry, A. Mickiewicz University, Poznan, Poland
| | - Wladek Minor
- Department of Molecular Physiology and Biological Physics, University of Virginia, Charlottesville, VA, USA.,Center for Structural Genomics of Infectious Diseases (CSGID), University of Virginia, Charlottesville, VA, USA
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14
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Masmaliyeva RC, Murshudov GN. Analysis and validation of macromolecular B values. Acta Crystallogr D Struct Biol 2019; 75:505-518. [PMID: 31063153 PMCID: PMC6503761 DOI: 10.1107/s2059798319004807] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2019] [Accepted: 04/09/2019] [Indexed: 11/10/2022] Open
Abstract
This paper describes a global analysis of macromolecular B values. It is shown that the distribution of B values generally follows the shifted inverse-gamma distribution (SIGD). The parameters of the SIGD are estimated using the Fisher scoring technique with the expected Fisher information matrix. It is demonstrated that a contour plot based on the parameters of the SIGD can play a role in the validation of macromolecular structures. The dependence of the peak-height distribution on resolution and atomic B values is also analysed. It is demonstrated that the B-value distribution can have a dramatically different effect on peak heights at different resolutions. Consequently, a comparative analysis of the B values of neighbouring atoms must account for resolution. A combination of the SIGD, peak-height distribution and outlier detection was used to identify a number of entries from the PDB that require attention. It is also shown that the presence of a multimodal B-value distribution often indicates that some loops or parts of the molecule have either been mismodelled or have dramatically different mobility, depending on their environment within the crystal. These distributions can also indicate the level of sharpening/blurring used before atomic structure refinement. It is recommended that procedures such as sharpening/blurring should be avoided during refinement, although they can play important roles in map visualization and model building.
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Affiliation(s)
- Rafiga C. Masmaliyeva
- Institute of Molecular Biology and Biotechnology ANAS, Matbuat Avenue 2a, Baku 1073, Azerbaijan
| | - Garib N. Murshudov
- MRC Laboratory of Molecular Biology, Francis Crick Avenue, Cambridge CB2 0QH, England
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15
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Moreau DW, Atakisi H, Thorne RE. Ice formation and solvent nanoconfinement in protein crystals. IUCRJ 2019; 6:346-356. [PMID: 31098016 PMCID: PMC6503922 DOI: 10.1107/s2052252519001878] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/19/2018] [Accepted: 01/31/2019] [Indexed: 05/06/2023]
Abstract
Ice formation within protein crystals is a major obstacle to the cryocrystallographic study of protein structure, and has limited studies of how the structural ensemble of a protein evolves with temperature in the biophysically interesting range from ∼260 K to the protein-solvent glass transition near 200 K. Using protein crystals with solvent cavities as large as ∼70 Å, time-resolved X-ray diffraction was used to study the response of protein and internal solvent during rapid cooling. Solvent nanoconfinement suppresses freezing temperatures and ice-nucleation rates so that ice-free, low-mosaicity diffraction data can be reliably collected down to 200 K without the use of cryoprotectants. Hexagonal ice (Ih) forms in external solvent, but internal crystal solvent forms stacking-disordered ice (Isd) with a near-random stacking of cubic and hexagonal planes. Analysis of powder diffraction from internal ice and single-crystal diffraction from the host protein structure shows that the maximum crystallizable solvent fraction decreases with decreasing crystal solvent-cavity size, and that an ∼6 Å thick layer of solvent adjacent to the protein surface cannot crystallize. These results establish protein crystals as excellent model systems for the study of nanoconfined solvent. By combining fast cooling, intense X-ray beams and fast X-ray detectors, complete structural data sets for high-value targets, including membrane proteins and large complexes, may be collected at ∼220-240 K that have much lower mosaicities and comparable B factors, and that may allow more confident identification of ligand binding than in current cryocrystallographic practice.
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Affiliation(s)
- David W. Moreau
- Physics Department, Cornell University, Ithaca, NY 14853, USA
| | - Hakan Atakisi
- Physics Department, Cornell University, Ithaca, NY 14853, USA
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16
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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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17
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Borbulevych O, Martin RI, Westerhoff LM. High-throughput quantum-mechanics/molecular-mechanics (ONIOM) macromolecular crystallographic refinement with PHENIX/DivCon: the impact of mixed Hamiltonian methods on ligand and protein structure. Acta Crystallogr D Struct Biol 2018; 74:1063-1077. [PMID: 30387765 PMCID: PMC6213575 DOI: 10.1107/s2059798318012913] [Citation(s) in RCA: 10] [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: 03/19/2018] [Accepted: 09/12/2018] [Indexed: 12/28/2022] Open
Abstract
Conventional macromolecular crystallographic refinement relies on often dubious stereochemical restraints, the preparation of which often requires human validation for unusual species, and on rudimentary energy functionals that are devoid of nonbonding effects owing to electrostatics, polarization, charge transfer or even hydrogen bonding. While this approach has served the crystallographic community for decades, as structure-based drug design/discovery (SBDD) has grown in prominence it has become clear that these conventional methods are less rigorous than they need to be in order to produce properly predictive protein-ligand models, and that the human intervention that is required to successfully treat ligands and other unusual chemistries found in SBDD often precludes high-throughput, automated refinement. Recently, plugins to the Python-based Hierarchical ENvironment for Integrated Xtallography (PHENIX) crystallographic platform have been developed to augment conventional methods with the in situ use of quantum mechanics (QM) applied to ligand(s) along with the surrounding active site(s) at each step of refinement [Borbulevych et al. (2014), Acta Cryst D70, 1233-1247]. This method (Region-QM) significantly increases the accuracy of the X-ray refinement process, and this approach is now used, coupled with experimental density, to accurately determine protonation states, binding modes, ring-flip states, water positions and so on. In the present work, this approach is expanded to include a more rigorous treatment of the entire structure, including the ligand(s), the associated active site(s) and the entire protein, using a fully automated, mixed quantum-mechanics/molecular-mechanics (QM/MM) Hamiltonian recently implemented in the DivCon package. This approach was validated through the automatic treatment of a population of 80 protein-ligand structures chosen from the Astex Diverse Set. Across the entire population, this method results in an average 3.5-fold reduction in ligand strain and a 4.5-fold improvement in MolProbity clashscore, as well as improvements in Ramachandran and rotamer outlier analyses. Overall, these results demonstrate that the use of a structure-wide QM/MM Hamiltonian exhibits improvements in the local structural chemistry of the ligand similar to Region-QM refinement but with significant improvements in the overall structure beyond the active site.
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Affiliation(s)
- Oleg Borbulevych
- QuantumBio Inc., 2790 West College Avenue, State College, PA 16801, USA
| | - Roger I. Martin
- QuantumBio Inc., 2790 West College Avenue, State College, PA 16801, USA
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18
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Srivastava A, Nagai T, Srivastava A, Miyashita O, Tama F. Role of Computational Methods in Going beyond X-ray Crystallography to Explore Protein Structure and Dynamics. Int J Mol Sci 2018; 19:E3401. [PMID: 30380757 PMCID: PMC6274748 DOI: 10.3390/ijms19113401] [Citation(s) in RCA: 44] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2018] [Revised: 10/20/2018] [Accepted: 10/27/2018] [Indexed: 12/13/2022] Open
Abstract
Protein structural biology came a long way since the determination of the first three-dimensional structure of myoglobin about six decades ago. Across this period, X-ray crystallography was the most important experimental method for gaining atomic-resolution insight into protein structures. However, as the role of dynamics gained importance in the function of proteins, the limitations of X-ray crystallography in not being able to capture dynamics came to the forefront. Computational methods proved to be immensely successful in understanding protein dynamics in solution, and they continue to improve in terms of both the scale and the types of systems that can be studied. In this review, we briefly discuss the limitations of X-ray crystallography in studying protein dynamics, and then provide an overview of different computational methods that are instrumental in understanding the dynamics of proteins and biomacromolecular complexes.
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Affiliation(s)
- Ashutosh Srivastava
- Institute of Transformative Bio-Molecules (WPI), Nagoya University, Nagoya, Aichi 464-8601, Japan.
| | - Tetsuro Nagai
- Department of Physics, Graduate School of Science, Nagoya University, Nagoya, Aichi 464-8602, Japan.
| | - Arpita Srivastava
- Department of Physics, Graduate School of Science, Nagoya University, Nagoya, Aichi 464-8602, Japan.
| | - Osamu Miyashita
- RIKEN-Center for Computational Science, Kobe, Hyogo 650-0047, Japan.
| | - Florence Tama
- Institute of Transformative Bio-Molecules (WPI), Nagoya University, Nagoya, Aichi 464-8601, Japan.
- Department of Physics, Graduate School of Science, Nagoya University, Nagoya, Aichi 464-8602, Japan.
- RIKEN-Center for Computational Science, Kobe, Hyogo 650-0047, Japan.
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19
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Crystallographic snapshots of ligand binding to hexameric purine nucleoside phosphorylase and kinetic studies give insight into the mechanism of catalysis. Sci Rep 2018; 8:15427. [PMID: 30337572 PMCID: PMC6193948 DOI: 10.1038/s41598-018-33723-1] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2018] [Accepted: 09/26/2018] [Indexed: 01/16/2023] Open
Abstract
Purine nucleoside phosphorylase (PNP) catalyses the cleavage of the glycosidic bond of purine nucleosides using phosphate instead of water as a second substrate. PNP from Escherichia coli is a homohexamer, build as a trimer of dimers, and each subunit can be in two conformations, open or closed. This conformational change is induced by the presence of phosphate substrate, and very likely a required step for the catalysis. Closing one active site strongly affects the others, by a yet unclear mechanism and order of events. Kinetic and ligand binding studies show strong negative cooperativity between subunits. Here, for the first time, we managed to monitor the sequence of nucleoside binding to individual subunits in the crystal structures of the wild-type enzyme, showing that first the closed sites, not the open ones, are occupied by the nucleoside. However, two mutations within the active site, Asp204Ala/Arg217Ala, are enough not only to significantly reduce the effectiveness of the enzyme, but also reverse the sequence of the nucleoside binding. In the mutant the open sites, neighbours in a dimer of those in the closed conformation, are occupied as first. This demonstrates how important for the effective catalysis of Escherichia coli PNP is proper subunit cooperation.
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20
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Raczynska JE, Shabalin IG, Minor W, Wlodawer A, Jaskolski M. A close look onto structural models and primary ligands of metallo-β-lactamases. Drug Resist Updat 2018; 40:1-12. [PMID: 30466711 PMCID: PMC6260963 DOI: 10.1016/j.drup.2018.08.001] [Citation(s) in RCA: 33] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2018] [Revised: 08/14/2018] [Accepted: 08/16/2018] [Indexed: 10/28/2022]
Abstract
β-Lactamases are hydrolytic enzymes capable of opening the β-lactam ring of antibiotics such as penicillin, thus endowing the bacteria that produce them with antibiotic resistance. Of particular medical concern are metallo-β-lactamases (MBLs), with an active site built around coordinated Zn cations. MBLs are pan-reactive enzymes that can break down almost all classes of β-lactams, including such last-resort antibiotics as carbapenems. They are not only broad-spectrum-reactive but are often plasmid-borne (e.g., the New Delhi enzyme, NDM), and can spread horizontally even among unrelated bacteria. Acquired MBLs are encoded by mobile genetic elements, which often include other resistance genes, making the microbiological situation particularly alarming. There is an urgent need to develop MBL inhibitors in order to rescue our antibiotic armory. A number of such efforts have been undertaken, most notably using the 3D structures of various MBLs as drug-design targets. Structure-guided drug discovery depends on the quality of the structures that are collected in the Protein Data Bank (PDB) and on the consistency of the information in dedicated β-lactamase databases. We conducted a careful review of the crystal structures of class B β-lactamases, concluding that the quality of these structures varies widely, especially in the regions where small molecules interact with the macromolecules. In a number of examples the interpretation of the bound ligands (e.g., inhibitors, substrate/product analogs) is doubtful or even incorrect, and it appears that in some cases the modeling of ligands was not supported by electron density. For ten MBL structures, alternative interpretations of the original diffraction data could be proposed and the new models have been deposited in the PDB. In four cases, these models, prepared jointly with the authors of the original depositions, superseded the previous deposits. This review emphasizes the importance of critical assessment of structural models describing key drug design targets at the level of the raw experimental data. Since the structures reviewed here are the basis for ongoing design of new MBL inhibitors, it is important to identify and correct the problems with ambiguous crystallographic interpretations, thus enhancing reproducibility in this highly medically relevant area.
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Affiliation(s)
- Joanna E Raczynska
- Center for Biocrystallographic Research, Institute of Bioorganic Chemistry, Polish Academy of Sciences, Poznan, Poland
| | - Ivan G Shabalin
- Department of Molecular Physiology and Biological Physics, University of Virginia, Charlottesville, VA 22908, USA; Center for Structural Genomics of Infectious Diseases, Charlottesville, VA 22908, USA
| | - Wladek Minor
- Department of Molecular Physiology and Biological Physics, University of Virginia, Charlottesville, VA 22908, USA; Center for Structural Genomics of Infectious Diseases, Charlottesville, VA 22908, USA
| | - Alexander Wlodawer
- Protein Structure Section, Macromolecular Crystallography Laboratory, National Cancer Institute, Frederick, MD 21702, USA
| | - Mariusz Jaskolski
- Center for Biocrystallographic Research, Institute of Bioorganic Chemistry, Polish Academy of Sciences, Poznan, Poland; Department of Crystallography, Faculty of Chemistry, A. Mickiewicz University, Poznan, Poland.
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21
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Fischer NM, Polêto MD, Steuer J, van der Spoel D. Influence of Na+ and Mg2+ ions on RNA structures studied with molecular dynamics simulations. Nucleic Acids Res 2018; 46:4872-4882. [PMID: 29718375 PMCID: PMC6007214 DOI: 10.1093/nar/gky221] [Citation(s) in RCA: 62] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2017] [Revised: 02/16/2018] [Accepted: 04/23/2018] [Indexed: 01/11/2023] Open
Abstract
The structure of ribonucleic acid (RNA) polymers is strongly dependent on the presence of, in particular Mg2+ cations to stabilize structural features. Only in high-resolution X-ray crystallography structures can ions be identified reliably. Here, we perform molecular dynamics simulations of 24 RNA structures with varying ion concentrations. Twelve of the structures were helical and the others complex folded. The aim of the study is to predict ion positions but also to evaluate the impact of different types of ions (Na+ or Mg2+) and the ionic strength on structural stability and variations of RNA. As a general conclusion Mg2+ is found to conserve the experimental structure better than Na+ and, where experimental ion positions are available, they can be reproduced with reasonable accuracy. If a large surplus of ions is present the added electrostatic screening makes prediction of binding-sites less reproducible. Distinct differences in ion-binding between helical and complex folded structures are found. The strength of binding (ΔG‡ for breaking RNA atom-ion interactions) is found to differ between roughly 10 and 26 kJ/mol for the different RNA atoms. Differences in stability between helical and complex folded structures and of the influence of metal ions on either are discussed.
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Affiliation(s)
- Nina M Fischer
- Uppsala Centre for Computational Chemistry, Science for Life Laboratory, Department of Cell and Molecular Biology, Uppsala University, Husargatan 3, Box 596, SE-75124 Uppsala, Sweden
| | - Marcelo D Polêto
- Uppsala Centre for Computational Chemistry, Science for Life Laboratory, Department of Cell and Molecular Biology, Uppsala University, Husargatan 3, Box 596, SE-75124 Uppsala, Sweden
- Center of Biotechnology, Universidade Federal do Rio Grande do Sul, Bento Gonçalves 9500, BR-91500-970 Porto Alegre, Brazil
| | - Jakob Steuer
- Uppsala Centre for Computational Chemistry, Science for Life Laboratory, Department of Cell and Molecular Biology, Uppsala University, Husargatan 3, Box 596, SE-75124 Uppsala, Sweden
- Department of Chemistry, University of Konstanz, Universitätstraße 10, D-78457 Konstanz, Germany
| | - David van der Spoel
- Uppsala Centre for Computational Chemistry, Science for Life Laboratory, Department of Cell and Molecular Biology, Uppsala University, Husargatan 3, Box 596, SE-75124 Uppsala, Sweden
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22
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Kleywegt GJ, Velankar S, Patwardhan A. Structural biology data archiving - where we are and what lies ahead. FEBS Lett 2018; 592:2153-2167. [PMID: 29749603 PMCID: PMC6019198 DOI: 10.1002/1873-3468.13086] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2018] [Revised: 04/25/2018] [Accepted: 04/30/2018] [Indexed: 12/31/2022]
Abstract
For almost 50 years, structural biology has endeavoured to conserve and share its experimental data and their interpretations (usually, atomistic models) through global public archives such as the Protein Data Bank, Electron Microscopy Data Bank and Biological Magnetic Resonance Data Bank (BMRB). These archives are treasure troves of freely accessible data that document our quest for molecular or atomic understanding of biological function and processes in health and disease. They have prepared the field to tackle new archiving challenges as more and more (combinations of) techniques are being utilized to elucidate structure at ever increasing length scales. Furthermore, the field has made substantial efforts to develop validation methods that help users to assess the reliability of structures and to identify the most appropriate data for their needs. In this Review, we present an overview of public data archives in structural biology and discuss the importance of validation for users and producers of structural data. Finally, we sketch our efforts to integrate structural data with bioimaging data and with other sources of biological data. This will make relevant structural information available and more easily discoverable for a wide range of scientists.
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Affiliation(s)
- Gerard J. Kleywegt
- European Molecular Biology Laboratory (EMBL)European Bioinformatics Institute (EMBL‐EBI)CambridgeUK
| | - Sameer Velankar
- European Molecular Biology Laboratory (EMBL)European Bioinformatics Institute (EMBL‐EBI)CambridgeUK
| | - Ardan Patwardhan
- European Molecular Biology Laboratory (EMBL)European Bioinformatics Institute (EMBL‐EBI)CambridgeUK
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23
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Rupp B. Against Method: Table 1-Cui Bono? Structure 2018; 26:919-923. [PMID: 29861344 DOI: 10.1016/j.str.2018.04.013] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2017] [Revised: 03/20/2018] [Accepted: 04/18/2018] [Indexed: 11/16/2022]
Abstract
The almost universally required "Table 1," summarizing data-collection and data-processing statistics, has in its present form outlived its usefulness in almost all publications of biomolecular crystal structure reports. Information contained in "Table 1" is insufficient to evaluate or repeat the experiment; is redundant with information extractable from deposited diffraction data; and includes data items whose meaning is under increased scrutiny in the crystallographic community. Direct and consistent extraction and analysis of data quality metrics from preferably unmerged intensity data with graphical presentation of reciprocal space features, including impact on map and model features, should replace "Table 1."
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Affiliation(s)
- Bernhard Rupp
- k.-k.Hofkristallamt, San Diego, CA 92084, USA; Division of Genetic Epidemiology, Medical University Innsbruck, Schöpfstraße 41, Innsbruck, Tyrol 6020, Austria.
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24
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Song L, Merceron R, Gracia B, Quintana AL, Risseeuw MDP, Hulpia F, Cos P, Aínsa JA, Munier-Lehmann H, Savvides SN, Van Calenbergh S. Structure Guided Lead Generation toward Nonchiral M. tuberculosis Thymidylate Kinase Inhibitors. J Med Chem 2018; 61:2753-2775. [DOI: 10.1021/acs.jmedchem.7b01570] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Affiliation(s)
- Lijun Song
- Laboratory for Medicinal Chemistry (FFW), Ghent University, Ottergemsesteenweg 460, B-9000 Gent, Belgium
| | - Romain Merceron
- VIB Center for Inflammation Research, Zwijnaarde, Ghent 9052, Belgium
- Department of Biochemistry and Microbiology, Ghent University, Ghent 9000, Belgium
| | - Begoña Gracia
- Grupo de Genética de Micobacterias, Departamento de Microbiología, Facultad de Medicina, and BIFI, Universidad de Zaragoza, Zaragoza, Spain
- CIBER Enfermedades Respiratorias (CIBERES), Instituto de Salud Carlos III, 28029 Madrid, Spain
| | - Ainhoa Lucía Quintana
- Grupo de Genética de Micobacterias, Departamento de Microbiología, Facultad de Medicina, and BIFI, Universidad de Zaragoza, Zaragoza, Spain
- CIBER Enfermedades Respiratorias (CIBERES), Instituto de Salud Carlos III, 28029 Madrid, Spain
| | - Martijn D. P. Risseeuw
- Laboratory for Medicinal Chemistry (FFW), Ghent University, Ottergemsesteenweg 460, B-9000 Gent, Belgium
| | - Fabian Hulpia
- Laboratory for Medicinal Chemistry (FFW), Ghent University, Ottergemsesteenweg 460, B-9000 Gent, Belgium
| | - Paul Cos
- Laboratory for Microbiology, Parasitology and Hygiene (LMPH), Department of Pharmaceutical Sciences, University of Antwerp, Campus Drie Eiken, Universiteitsplein 1, B-2610 Antwerpen, Belgium
| | - José A. Aínsa
- Grupo de Genética de Micobacterias, Departamento de Microbiología, Facultad de Medicina, and BIFI, Universidad de Zaragoza, Zaragoza, Spain
- CIBER Enfermedades Respiratorias (CIBERES), Instituto de Salud Carlos III, 28029 Madrid, Spain
| | - Hélène Munier-Lehmann
- Unit of Chemistry and Biocatalysis, Department of Structural Biology and Chemistry, Institut Pasteur, CNRS UMR3523, 28 Rue du Dr. Roux, Cedex 15 75724 Paris, France
| | - Savvas N. Savvides
- VIB Center for Inflammation Research, Zwijnaarde, Ghent 9052, Belgium
- Department of Biochemistry and Microbiology, Ghent University, Ghent 9000, Belgium
| | - Serge Van Calenbergh
- Laboratory for Medicinal Chemistry (FFW), Ghent University, Ottergemsesteenweg 460, B-9000 Gent, Belgium
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25
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Smart OS, Horský V, Gore S, Svobodová Vařeková R, Bendová V, Kleywegt GJ, Velankar S. Validation of ligands in macromolecular structures determined by X-ray crystallography. Acta Crystallogr D Struct Biol 2018; 74:228-236. [PMID: 29533230 PMCID: PMC5947763 DOI: 10.1107/s2059798318002541] [Citation(s) in RCA: 40] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2017] [Accepted: 02/12/2018] [Indexed: 01/19/2023] Open
Abstract
Crystallographic studies of ligands bound to biological macromolecules (proteins and nucleic acids) play a crucial role in structure-guided drug discovery and design, and also provide atomic level insights into the physical chemistry of complex formation between macromolecules and ligands. The quality with which small-molecule ligands have been modelled in Protein Data Bank (PDB) entries has been, and continues to be, a matter of concern for many investigators. Correctly interpreting whether electron density found in a binding site is compatible with the soaked or co-crystallized ligand or represents water or buffer molecules is often far from trivial. The Worldwide PDB validation report (VR) provides a mechanism to highlight any major issues concerning the quality of the data and the model at the time of deposition and annotation, so the depositors can fix issues, resulting in improved data quality. The ligand-validation methods used in the generation of the current VRs are described in detail, including an examination of the metrics to assess both geometry and electron-density fit. It is found that the LLDF score currently used to identify ligand electron-density fit outliers can give misleading results and that better ligand-validation metrics are required.
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Affiliation(s)
- Oliver S. Smart
- Protein Data Bank in Europe, European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, England
| | - Vladimír Horský
- National Centre for Biomolecular Research, Faculty of Science, Masaryk University, Kamenice 5, 625 00 Brno, Czech Republic
- CEITEC – Central European Institute of Technology, Masaryk University, Kamenice 5, 625 00 Brno, Czech Republic
| | - Swanand Gore
- Protein Data Bank in Europe, European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, England
| | - Radka Svobodová Vařeková
- National Centre for Biomolecular Research, Faculty of Science, Masaryk University, Kamenice 5, 625 00 Brno, Czech Republic
- CEITEC – Central European Institute of Technology, Masaryk University, Kamenice 5, 625 00 Brno, Czech Republic
| | - Veronika Bendová
- National Centre for Biomolecular Research, Faculty of Science, Masaryk University, Kamenice 5, 625 00 Brno, Czech Republic
- CEITEC – Central European Institute of Technology, Masaryk University, Kamenice 5, 625 00 Brno, Czech Republic
- Institute of Mathematics and Statistics, Masaryk University, Kotlářská 2, 611 37 Brno, Czech Republic
| | - Gerard J. Kleywegt
- Protein Data Bank in Europe, European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, England
| | - Sameer Velankar
- Protein Data Bank in Europe, European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, England
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26
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Wlodawer A, Dauter Z, Porebski PJ, Minor W, Stanfield R, Jaskolski M, Pozharski E, Weichenberger CX, Rupp B. Detect, correct, retract: How to manage incorrect structural models. FEBS J 2018; 285:444-466. [PMID: 29113027 PMCID: PMC5799025 DOI: 10.1111/febs.14320] [Citation(s) in RCA: 40] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2017] [Accepted: 11/01/2017] [Indexed: 12/13/2022]
Abstract
The massive technical and computational progress of biomolecular crystallography has generated some adverse side effects. Most crystal structure models, produced by crystallographers or well-trained structural biologists, constitute useful sources of information, but occasional extreme outliers remind us that the process of structure determination is not fail-safe. The occurrence of severe errors or gross misinterpretations raises fundamental questions: Why do such aberrations emerge in the first place? How did they evade the sophisticated validation procedures which often produce clear and dire warnings, and why were severe errors not noticed by the depositors themselves, their supervisors, referees and editors? Once detected, what can be done to either correct, improve or eliminate such models? How do incorrect models affect the underlying claims or biomedical hypotheses they were intended, but failed, to support? What is the long-range effect of the propagation of such errors? And finally, what mechanisms can be envisioned to restore the validity of the scientific record and, if necessary, retract publications that are clearly invalidated by the lack of experimental evidence? We suggest that cognitive bias and flawed epistemology are likely at the root of the problem. By using examples from the published literature and from public repositories such as the Protein Data Bank, we provide case summaries to guide correction or improvement of structural models. When strong claims are unsustainable because of a deficient crystallographic model, removal of such a model and even retraction of the affected publication are necessary to restore the integrity of the scientific record.
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Affiliation(s)
- Alexander Wlodawer
- Protein Structure Section, Macromolecular Crystallography Laboratory, National Cancer Institute, Frederick, MD, 21702, USA
| | - Zbigniew Dauter
- Synchrotron Radiation Research Section, Macromolecular Crystallography Laboratory, National Cancer Institute, Argonne National Laboratory, Argonne, IL 60439, USA
| | - Przemyslaw J. Porebski
- Department of Molecular Physiology and Biological Physics, University of Virginia, 1340 Jefferson Park Avenue, Charlottesville, VA, 22908, USA
| | - Wladek Minor
- Department of Molecular Physiology and Biological Physics, University of Virginia, 1340 Jefferson Park Avenue, Charlottesville, VA, 22908, USA
| | - Robyn Stanfield
- Department of Structural and Computational Biology, BCC206, The Scripps Research Institute, 10550 N. Torrey Pines Road, La Jolla, CA, 92037, USA
| | - Mariusz Jaskolski
- Department of Crystallography, Faculty of Chemistry, A. Mickiewicz University, Umultowska 89b, Poznan, 61-614, Poland
- Center for Biocrystallographic Research, Institute of Bioorganic Chemistry, Polish Academy of Sciences, Noskowskiego 12/14, Poznan, 61-704, Poland
| | - Edwin Pozharski
- Department of Biochemistry and Molecular Biology, University of Maryland School of Medicine, Baltimore, MD, USA
| | | | - Bernhard Rupp
- CVMO, k.-k.Hofkristallamt, 991 Audrey Place, Vista, CA, 92084, USA
- Department of Genetic Epidemiology, Medical University Innsbruck, Schöpfstr. 41, Innsbruck, 6020, Austria
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27
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Helliwell JR, McMahon B, Guss JM, Kroon-Batenburg LMJ. The science is in the data. IUCRJ 2017; 4:714-722. [PMID: 29123672 PMCID: PMC5668855 DOI: 10.1107/s2052252517013690] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/18/2017] [Accepted: 09/24/2017] [Indexed: 05/22/2023]
Abstract
Understanding published research results should be through one's own eyes and include the opportunity to work with raw diffraction data to check the various decisions made in the analyses by the original authors. Today, preserving raw diffraction data is technically and organizationally viable at a growing number of data archives, both centralized and distributed, which are empowered to register data sets and obtain a preservation descriptor, typically a 'digital object identifier'. This introduces an important role of preserving raw data, namely understanding where we fail in or could improve our analyses. Individual science area case studies in crystallography are provided.
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Affiliation(s)
- John R. Helliwell
- School of Chemistry, University of Manchester, Manchester M13 9PL, England
| | - Brian McMahon
- International Union of Crystallography, 5 Abbey Square, Chester CH1 2HU, England
| | - J. Mitchell Guss
- School of Life and Environmental Sciences, The University of Sydney, Sydney, NSW 2006, Australia
| | - Loes M. J. Kroon-Batenburg
- Crystal and Structural Chemistry, Bijvoet Center for Biomolecular Research, Utrecht University, Padualaan 8, CH 3584 Utrecht, The Netherlands
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28
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Peach ML, Cachau RE, Nicklaus MC. Conformational energy range of ligands in protein crystal structures: The difficult quest for accurate understanding. J Mol Recognit 2017; 30:10.1002/jmr.2618. [PMID: 28233410 PMCID: PMC5553890 DOI: 10.1002/jmr.2618] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2016] [Revised: 01/31/2017] [Accepted: 01/31/2017] [Indexed: 12/25/2022]
Abstract
In this review, we address a fundamental question: What is the range of conformational energies seen in ligands in protein-ligand crystal structures? This value is important biophysically, for better understanding the protein-ligand binding process; and practically, for providing a parameter to be used in many computational drug design methods such as docking and pharmacophore searches. We synthesize a selection of previously reported conflicting results from computational studies of this issue and conclude that high ligand conformational energies really are present in some crystal structures. The main source of disagreement between different analyses appears to be due to divergent treatments of electrostatics and solvation. At the same time, however, for many ligands, a high conformational energy is in error, due to either crystal structure inaccuracies or incorrect determination of the reference state. Aside from simple chemistry mistakes, we argue that crystal structure error may mainly be because of the heuristic weighting of ligand stereochemical restraints relative to the fit of the structure to the electron density. This problem cannot be fixed with improvements to electron density fitting or with simple ligand geometry checks, though better metrics are needed for evaluating ligand and binding site chemistry in addition to geometry during structure refinement. The ultimate solution for accurately determining ligand conformational energies lies in ultrahigh-resolution crystal structures that can be refined without restraints.
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Affiliation(s)
- Megan L Peach
- Basic Science Program, Chemical Biology Laboratory, Leidos Biomedical Research Inc., Frederick National Laboratory for Cancer Research, Frederick, MD, USA
| | - Raul E Cachau
- Data Science and Information Technology Program, Advanced Biomedical Computing Center, Leidos Biomedical Research Inc., Frederick National Laboratory for Cancer Research, Frederick, MD, USA
| | - Marc C Nicklaus
- Chemical Biology Laboratory, Center for Cancer Research, National Cancer Institute, Frederick, MD, USA
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29
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Morelhão SL, Remédios CMR, Calligaris GA, Nisbet G. X-ray dynamical diffraction in amino acid crystals: a step towards improving structural resolution of biological molecules via physical phase measurements. J Appl Crystallogr 2017; 50:689-700. [PMID: 28656034 PMCID: PMC5458588 DOI: 10.1107/s1600576717004757] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2016] [Accepted: 03/27/2017] [Indexed: 12/29/2022] Open
Abstract
X-ray phase measurements have been applied to study hydrogen bonds and radiation damage in amino acid crystals. In this work, experimental and data analysis procedures were developed and applied for studying amino acid crystals by means of X-ray phase measurements. The results clearly demonstrated the sensitivity of invariant triplet phases to electronic charge distribution in d-alanine crystals, providing useful information for molecular dynamics studies of intermolecular forces. The feasibility of using phase measurements to investigate radiation damage mechanisms is also discussed on experimental and theoretical grounds.
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Affiliation(s)
- Sérgio L Morelhão
- Instituto de Física, Universidade de São Paulo, São Paulo, SP, Brazil
| | | | | | - Gareth Nisbet
- Diamond Light Source, Harwell Science and Innovation Campus, OX11 0DE, UK
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30
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Christensen EM, Patel SM, Korasick DA, Campbell AC, Krause KL, Becker DF, Tanner JJ. Resolving the cofactor-binding site in the proline biosynthetic enzyme human pyrroline-5-carboxylate reductase 1. J Biol Chem 2017; 292:7233-7243. [PMID: 28258219 PMCID: PMC5409489 DOI: 10.1074/jbc.m117.780288] [Citation(s) in RCA: 40] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2017] [Revised: 02/27/2017] [Indexed: 01/22/2023] Open
Abstract
Pyrroline-5-carboxylate reductase (PYCR) is the final enzyme in proline biosynthesis, catalyzing the NAD(P)H-dependent reduction of Δ1-pyrroline-5-carboxylate (P5C) to proline. Mutations in the PYCR1 gene alter mitochondrial function and cause the connective tissue disorder cutis laxa. Furthermore, PYCR1 is overexpressed in multiple cancers, and the PYCR1 knock-out suppresses tumorigenic growth, suggesting that PYCR1 is a potential cancer target. However, inhibitor development has been stymied by limited mechanistic details for the enzyme, particularly in light of a previous crystallographic study that placed the cofactor-binding site in the C-terminal domain rather than the anticipated Rossmann fold of the N-terminal domain. To fill this gap, we report crystallographic, sedimentation-velocity, and kinetics data for human PYCR1. Structures of binary complexes of PYCR1 with NADPH or proline determined at 1.9 Å resolution provide insight into cofactor and substrate recognition. We see NADPH bound to the Rossmann fold, over 25 Å from the previously proposed site. The 1.85 Å resolution structure of a ternary complex containing NADPH and a P5C/proline analog provides a model of the Michaelis complex formed during hydride transfer. Sedimentation velocity shows that PYCR1 forms a concentration-dependent decamer in solution, consistent with the pentamer-of-dimers assembly seen crystallographically. Kinetic and mutational analysis confirmed several features seen in the crystal structure, including the importance of a hydrogen bond between Thr-238 and the substrate as well as limited cofactor discrimination.
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Affiliation(s)
| | - Sagar M Patel
- the Department of Biochemistry and Redox Biology Center, University of Nebraska, Lincoln, Nebraska 68588
| | | | | | - Kurt L Krause
- the Department of Biochemistry, University of Otago, Dunedin 9054, New Zealand, and
| | - Donald F Becker
- the Department of Biochemistry and Redox Biology Center, University of Nebraska, Lincoln, Nebraska 68588
| | - John J Tanner
- From the Departments of Chemistry and
- Biochemistry University of Missouri, Columbia, Missouri 65211
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31
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A multi-crystal method for extracting obscured crystallographic states from conventionally uninterpretable electron density. Nat Commun 2017; 8:15123. [PMID: 28436492 PMCID: PMC5413968 DOI: 10.1038/ncomms15123] [Citation(s) in RCA: 157] [Impact Index Per Article: 22.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2016] [Accepted: 03/03/2017] [Indexed: 12/02/2022] Open
Abstract
In macromolecular crystallography, the rigorous detection of changed states (for example, ligand binding) is difficult unless signal is strong. Ambiguous (‘weak' or ‘noisy') density is experimentally common, since molecular states are generally only fractionally present in the crystal. Existing methodologies focus on generating maximally accurate maps whereby minor states become discernible; in practice, such map interpretation is disappointingly subjective, time-consuming and methodologically unsound. Here we report the PanDDA method, which automatically reveals clear electron density for the changed state—even from inaccurate maps—by subtracting a proportion of the confounding ‘ground state'; changed states are objectively identified from statistical analysis of density distributions. The method is completely general, implying new best practice for all changed-state studies, including the routine collection of multiple ground-state crystals. More generally, these results demonstrate: the incompleteness of atomic models; that single data sets contain insufficient information to model them fully; and that accuracy requires further map-deconvolution approaches. Building a ligand into a weak region of an electron density map of a protein is a subjective process. Here, the authors present a new method to obtain a clear electron density for a bound ligand based on multi-crystal experiments and 3D background correction.
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32
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Krojer T, Talon R, Pearce N, Collins P, Douangamath A, Brandao-Neto J, Dias A, Marsden B, von Delft F. The XChemExplorer graphical workflow tool for routine or large-scale protein-ligand structure determination. Acta Crystallogr D Struct Biol 2017; 73:267-278. [PMID: 28291762 PMCID: PMC5349439 DOI: 10.1107/s2059798316020234] [Citation(s) in RCA: 64] [Impact Index Per Article: 9.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2016] [Accepted: 12/20/2016] [Indexed: 11/11/2022] Open
Abstract
XChemExplorer (XCE) is a data-management and workflow tool to support large-scale simultaneous analysis of protein-ligand complexes during structure-based ligand discovery (SBLD). The user interfaces of established crystallographic software packages such as CCP4 [Winn et al. (2011), Acta Cryst. D67, 235-242] or PHENIX [Adams et al. (2010), Acta Cryst. D66, 213-221] have entrenched the paradigm that a `project' is concerned with solving one structure. This does not hold for SBLD, where many almost identical structures need to be solved and analysed quickly in one batch of work. Functionality to track progress and annotate structures is essential. XCE provides an intuitive graphical user interface which guides the user from data processing, initial map calculation, ligand identification and refinement up until data dissemination. It provides multiple entry points depending on the need of each project, enables batch processing of multiple data sets and records metadata, progress and annotations in an SQLite database. XCE is freely available and works on any Linux and Mac OS X system, and the only dependency is to have the latest version of CCP4 installed. The design and usage of this tool are described here, and its usefulness is demonstrated in the context of fragment-screening campaigns at the Diamond Light Source. It is routinely used to analyse projects comprising 1000 data sets or more, and therefore scales well to even very large ligand-design projects.
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Affiliation(s)
- Tobias Krojer
- Structural Genomics Consortium, University of Oxford, Roosevelt Drive, Oxford OX3 7DQ, England
| | - Romain Talon
- Structural Genomics Consortium, University of Oxford, Roosevelt Drive, Oxford OX3 7DQ, England
| | - Nicholas Pearce
- Structural Genomics Consortium, University of Oxford, Roosevelt Drive, Oxford OX3 7DQ, England
| | - Patrick Collins
- Diamond Light Source Ltd, Harwell Science and Innovation Campus, Didcot OX11 0QX, England
| | - Alice Douangamath
- Diamond Light Source Ltd, Harwell Science and Innovation Campus, Didcot OX11 0QX, England
| | - Jose Brandao-Neto
- Diamond Light Source Ltd, Harwell Science and Innovation Campus, Didcot OX11 0QX, England
| | - Alexandre Dias
- Diamond Light Source Ltd, Harwell Science and Innovation Campus, Didcot OX11 0QX, England
| | - Brian Marsden
- Structural Genomics Consortium, University of Oxford, Roosevelt Drive, Oxford OX3 7DQ, England
- Kennedy Institute of Rheumatology, University of Oxford, Roosevelt Drive, Oxford OX3 7FY, England
| | - Frank von Delft
- Structural Genomics Consortium, University of Oxford, Roosevelt Drive, Oxford OX3 7DQ, England
- Diamond Light Source Ltd, Harwell Science and Innovation Campus, Didcot OX11 0QX, England
- Department of Biochemistry, University of Johannesburg, Auckland Park 2006, South Africa
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33
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Beshnova DA, Pereira J, Lamzin VS. Estimation of the protein-ligand interaction energy for model building and validation. Acta Crystallogr D Struct Biol 2017; 73:195-202. [PMID: 28291754 PMCID: PMC5349431 DOI: 10.1107/s2059798317003400] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2016] [Accepted: 03/01/2017] [Indexed: 12/03/2022] Open
Abstract
Macromolecular X-ray crystallography is one of the main experimental techniques to visualize protein-ligand interactions. The high complexity of the ligand universe, however, has delayed the development of efficient methods for the automated identification, fitting and validation of ligands in their electron-density clusters. The identification and fitting are primarily based on the density itself and do not take into account the protein environment, which is a step that is only taken during the validation of the proposed binding mode. Here, a new approach, based on the estimation of the major energetic terms of protein-ligand interaction, is introduced for the automated identification of crystallographic ligands in the indicated binding site with ARP/wARP. The applicability of the method to the validation of protein-ligand models from the Protein Data Bank is demonstrated by the detection of models that are `questionable' and the pinpointing of unfavourable interatomic contacts.
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Affiliation(s)
- Daria A. Beshnova
- European Molecular Biology Laboratory, c/o DESY, Notkestrasse 85, 22607 Hamburg, Germany
| | - Joana Pereira
- European Molecular Biology Laboratory, c/o DESY, Notkestrasse 85, 22607 Hamburg, Germany
| | - Victor S. Lamzin
- European Molecular Biology Laboratory, c/o DESY, Notkestrasse 85, 22607 Hamburg, Germany
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34
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Weichenberger CX, Pozharski E, Rupp B. Twilight reloaded: the peptide experience. Acta Crystallogr D Struct Biol 2017; 73:211-222. [PMID: 28291756 PMCID: PMC5349433 DOI: 10.1107/s205979831601620x] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2016] [Accepted: 10/12/2016] [Indexed: 01/20/2024] Open
Abstract
The de facto commoditization of biomolecular crystallography as a result of almost disruptive instrumentation automation and continuing improvement of software allows any sensibly trained structural biologist to conduct crystallographic studies of biomolecules with reasonably valid outcomes: that is, models based on properly interpreted electron density. Robust validation has led to major mistakes in the protein part of structure models becoming rare, but some depositions of protein-peptide complex structure models, which generally carry significant interest to the scientific community, still contain erroneous models of the bound peptide ligand. Here, the protein small-molecule ligand validation tool Twilight is updated to include peptide ligands. (i) The primary technical reasons and potential human factors leading to problems in ligand structure models are presented; (ii) a new method used to score peptide-ligand models is presented; (iii) a few instructive and specific examples, including an electron-density-based analysis of peptide-ligand structures that do not contain any ligands, are discussed in detail; (iv) means to avoid such mistakes and the implications for database integrity are discussed and (v) some suggestions as to how journal editors could help to expunge errors from the Protein Data Bank are provided.
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Affiliation(s)
| | - Edwin Pozharski
- Department of Biochemistry and Molecular Biology, University of Maryland School of Medicine, Baltimore, Maryland, USA
| | - Bernhard Rupp
- k.k. Hofkristallamt, 991 Audrey Place, Vista, CA 92084, USA
- Department of Genetic Epidemiology, Medical University Innsbruck, Schöpfstrasse 41, A-6020 Innsbruck, Austria
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35
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Abstract
Crystal structures of protein-ligand complexes are often used to infer biology and inform structure-based drug discovery. Hence, it is important to build accurate, reliable models of ligands that give confidence in the interpretation of the respective protein-ligand complex. This paper discusses key stages in the ligand-fitting process, including ligand binding-site identification, ligand description and conformer generation, ligand fitting, refinement and subsequent validation. The CCP4 suite contains a number of software tools that facilitate this task: AceDRG for the creation of ligand descriptions and conformers, Lidia and JLigand for two-dimensional and three-dimensional ligand editing and visual analysis, Coot for density interpretation, ligand fitting, analysis and validation, and REFMAC5 for macromolecular refinement. In addition to recent advancements in automatic carbohydrate building in Coot (LO/Carb) and ligand-validation tools (FLEV), the release of the CCP4i2 GUI provides an integrated solution that streamlines the ligand-fitting workflow, seamlessly passing results from one program to the next. The ligand-fitting process is illustrated using instructive practical examples, including problematic cases such as post-translational modifications, highlighting the need for careful analysis and rigorous validation.
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Affiliation(s)
- Robert A. Nicholls
- Structural Studies, MRC Laboratory of Molecular Biology, Francis Crick Avenue, Cambridge CB2 0QH, England
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36
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Long F, Nicholls RA, Emsley P, Gražulis S, Merkys A, Vaitkus A, Murshudov GN. AceDRG: a stereochemical description generator for ligands. Acta Crystallogr D Struct Biol 2017; 73:112-122. [PMID: 28177307 PMCID: PMC5297914 DOI: 10.1107/s2059798317000067] [Citation(s) in RCA: 221] [Impact Index Per Article: 31.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2016] [Accepted: 01/03/2017] [Indexed: 11/11/2022] Open
Abstract
The program AceDRG is designed for the derivation of stereochemical information about small molecules. It uses local chemical and topological environment-based atom typing to derive and organize bond lengths and angles from a small-molecule database: the Crystallography Open Database (COD). Information about the hybridization states of atoms, whether they belong to small rings (up to seven-membered rings), ring aromaticity and nearest-neighbour information is encoded in the atom types. All atoms from the COD have been classified according to the generated atom types. All bonds and angles have also been classified according to the atom types and, in a certain sense, bond types. Derived data are tabulated in a machine-readable form that is freely available from CCP4. AceDRG can also generate stereochemical information, provided that the basic bonding pattern of a ligand is known. The basic bonding pattern is perceived from one of the computational chemistry file formats, including SMILES, mmCIF, SDF MOL and SYBYL MOL2 files. Using the bonding chemistry, atom types, and bond and angle tables generated from the COD, AceDRG derives the `ideal' bond lengths, angles, plane groups, aromatic rings and chirality information, and writes them to an mmCIF file that can be used by the refinement program REFMAC5 and the model-building program Coot. Other refinement and model-building programs such as PHENIX and BUSTER can also use these files. AceDRG also generates one or more coordinate sets corresponding to the most favourable conformation(s) of a given ligand. AceDRG employs RDKit for chemistry perception and for initial conformation generation, as well as for the interpretation of SMILES strings, SDF MOL and SYBYL MOL2 files.
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Affiliation(s)
- Fei Long
- Structural Studies, MRC Laboratory of Molecular Biology, Francis Crick Avenue, Cambridge CB2 0QH, England
| | - Robert A. Nicholls
- Structural Studies, MRC Laboratory of Molecular Biology, Francis Crick Avenue, Cambridge CB2 0QH, England
| | - Paul Emsley
- Structural Studies, MRC Laboratory of Molecular Biology, Francis Crick Avenue, Cambridge CB2 0QH, England
| | - Saulius Gražulis
- Institute of Biotechnology, Saulėtekio al. 7, LT-10257 Vilnius, Lithuania
| | - Andrius Merkys
- Institute of Biotechnology, Saulėtekio al. 7, LT-10257 Vilnius, Lithuania
| | - Antanas Vaitkus
- Institute of Biotechnology, Saulėtekio al. 7, LT-10257 Vilnius, Lithuania
| | - Garib N. Murshudov
- Structural Studies, MRC Laboratory of Molecular Biology, Francis Crick Avenue, Cambridge CB2 0QH, England
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37
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Long F, Nicholls RA, Emsley P, Gražulis S, Merkys A, Vaitkus A, Murshudov GN. Validation and extraction of molecular-geometry information from small-molecule databases. Acta Crystallogr D Struct Biol 2017; 73:103-111. [PMID: 28177306 PMCID: PMC5297913 DOI: 10.1107/s2059798317000079] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2016] [Accepted: 01/03/2017] [Indexed: 11/18/2022] Open
Abstract
A freely available small-molecule structure database, the Crystallography Open Database (COD), is used for the extraction of molecular-geometry information on small-molecule compounds. The results are used for the generation of new ligand descriptions, which are subsequently used by macromolecular model-building and structure-refinement software. To increase the reliability of the derived data, and therefore the new ligand descriptions, the entries from this database were subjected to very strict validation. The selection criteria made sure that the crystal structures used to derive atom types, bond and angle classes are of sufficiently high quality. Any suspicious entries at a crystal or molecular level were removed from further consideration. The selection criteria included (i) the resolution of the data used for refinement (entries solved at 0.84 Å resolution or higher) and (ii) the structure-solution method (structures must be from a single-crystal experiment and all atoms of generated molecules must have full occupancies), as well as basic sanity checks such as (iii) consistency between the valences and the number of connections between atoms, (iv) acceptable bond-length deviations from the expected values and (v) detection of atomic collisions. The derived atom types and bond classes were then validated using high-order moment-based statistical techniques. The results of the statistical analyses were fed back to fine-tune the atom typing. The developed procedure was repeated four times, resulting in fine-grained atom typing, bond and angle classes. The procedure will be repeated in the future as and when new entries are deposited in the COD. The whole procedure can also be applied to any source of small-molecule structures, including the Cambridge Structural Database and the ZINC database.
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Affiliation(s)
- Fei Long
- Structural Studies, MRC Laboratory of Molecular Biology, Francis Crick Avenue, Cambridge CB2 0QH, England
| | - Robert A. Nicholls
- Structural Studies, MRC Laboratory of Molecular Biology, Francis Crick Avenue, Cambridge CB2 0QH, England
| | - Paul Emsley
- Structural Studies, MRC Laboratory of Molecular Biology, Francis Crick Avenue, Cambridge CB2 0QH, England
| | - Saulius Gražulis
- Institute of Biotechnology, Saulėtekio al. 7, LT-10257 Vilnius, Lithuania
| | - Andrius Merkys
- Institute of Biotechnology, Saulėtekio al. 7, LT-10257 Vilnius, Lithuania
| | - Antanas Vaitkus
- Institute of Biotechnology, Saulėtekio al. 7, LT-10257 Vilnius, Lithuania
| | - Garib N. Murshudov
- Structural Studies, MRC Laboratory of Molecular Biology, Francis Crick Avenue, Cambridge CB2 0QH, England
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38
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Leonarski F, D'Ascenzo L, Auffinger P. Mg2+ ions: do they bind to nucleobase nitrogens? Nucleic Acids Res 2017; 45:987-1004. [PMID: 27923930 PMCID: PMC5314772 DOI: 10.1093/nar/gkw1175] [Citation(s) in RCA: 64] [Impact Index Per Article: 9.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2016] [Revised: 11/10/2016] [Accepted: 11/14/2016] [Indexed: 01/28/2023] Open
Abstract
Given the many roles proposed for Mg2+ in nucleic acids, it is essential to accurately determine their binding modes. Here, we surveyed the PDB to classify Mg2+ inner-sphere binding patterns to nucleobase imine N1/N3/N7 atoms. Among those, purine N7 atoms are considered to be the best nucleobase binding sites for divalent metals. Further, Mg2+ coordination to N7 has been implied in several ribozyme catalytic mechanisms. We report that Mg2+ assigned near imine nitrogens derive mostly from poor interpretations of electron density patterns and are most often misidentified Na+, K+, NH4+ ions, water molecules or spurious density peaks. Consequently, apart from few documented exceptions, Mg2+ ions do not bind to N7 atoms. Without much of a surprise, Mn2+, Zn2+ and Cd2+, which have a higher affinity for nitrogens, may contact N7 atoms when present in crystallization buffers. In this respect, we describe for the first time a potential Zn2+ ribosomal binding site involving two purine N7 atoms. Further, we provide a set of guidelines to help in the assignment of Mg2+ in crystallographic, cryo-EM, NMR and model building practices and discuss implications of our findings related to ion substitution experiments.
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Affiliation(s)
- Filip Leonarski
- Université de Strasbourg, CNRS, Architecture et Réactivité de l'ARN, UPR9002, F-67000 Strasbourg, France
- Faculty of Chemistry, University of Warsaw, Pasteura 1, 02-093 Warsaw, Poland
| | - Luigi D'Ascenzo
- Université de Strasbourg, CNRS, Architecture et Réactivité de l'ARN, UPR9002, F-67000 Strasbourg, France
| | - Pascal Auffinger
- Université de Strasbourg, CNRS, Architecture et Réactivité de l'ARN, UPR9002, F-67000 Strasbourg, France
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39
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Pozharski E, Deller MC, Rupp B. Validation of Protein-Ligand Crystal Structure Models: Small Molecule and Peptide Ligands. Methods Mol Biol 2017; 1607:611-625. [PMID: 28573591 DOI: 10.1007/978-1-4939-7000-1_25] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
Models of target proteins in complex with small molecule ligands or peptide ligands are of significant interest to the biomedical research community. Structure-guided lead discovery and structure-based drug design make extensive use of such models. The bound ligands comprise only a small fraction of the total X-ray scattering mass, and therefore particular care must be taken to properly validate the atomic model of the ligand as experimental data can often be scarce. The ligand model must be validated against both the primary experimental data and the local environment, specifically: (1) the primary evidence in the form of the electron density, (2) examined for reasonable stereochemistry, and (3) the chemical plausibility of the binding interactions must be inspected. Tools that assist the researcher in the validation process are presented.
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Affiliation(s)
- Edwin Pozharski
- Department of Biochemistry and Molecular Biology, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Marc C Deller
- Stanford ChEM-H, Macromolecular Structure Knowledge Center, Stanford University, Shriram Center, 443 Via Ortega, Room 097, MC5082, Stanford, CA, 94305-4125, USA
| | - Bernhard Rupp
- k.-k. Hofkristallamt, 991 Audrey Place, Vista, CA, 92084, USA.
- Department of Genetic Epidemiology, Medical University Innsbruck, Schöpfstr. 41, Innsbruck, 6020, Austria.
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40
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Adams PD, Aertgeerts K, Bauer C, Bell JA, Berman HM, Bhat TN, Blaney JM, Bolton E, Bricogne G, Brown D, Burley SK, Case DA, Clark KL, Darden T, Emsley P, Feher VA, Feng Z, Groom CR, Harris SF, Hendle J, Holder T, Joachimiak A, Kleywegt GJ, Krojer T, Marcotrigiano J, Mark AE, Markley JL, Miller M, Minor W, Montelione GT, Murshudov G, Nakagawa A, Nakamura H, Nicholls A, Nicklaus M, Nolte RT, Padyana AK, Peishoff CE, Pieniazek S, Read RJ, Shao C, Sheriff S, Smart O, Soisson S, Spurlino J, Stouch T, Svobodova R, Tempel W, Terwilliger TC, Tronrud D, Velankar S, Ward SC, Warren GL, Westbrook JD, Williams P, Yang H, Young J. Outcome of the First wwPDB/CCDC/D3R Ligand Validation Workshop. Structure 2016; 24:502-508. [PMID: 27050687 DOI: 10.1016/j.str.2016.02.017] [Citation(s) in RCA: 53] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2015] [Revised: 02/24/2016] [Accepted: 02/25/2016] [Indexed: 10/22/2022]
Abstract
Crystallographic studies of ligands bound to biological macromolecules (proteins and nucleic acids) represent an important source of information concerning drug-target interactions, providing atomic level insights into the physical chemistry of complex formation between macromolecules and ligands. Of the more than 115,000 entries extant in the Protein Data Bank (PDB) archive, ∼75% include at least one non-polymeric ligand. Ligand geometrical and stereochemical quality, the suitability of ligand models for in silico drug discovery and design, and the goodness-of-fit of ligand models to electron-density maps vary widely across the archive. We describe the proceedings and conclusions from the first Worldwide PDB/Cambridge Crystallographic Data Center/Drug Design Data Resource (wwPDB/CCDC/D3R) Ligand Validation Workshop held at the Research Collaboratory for Structural Bioinformatics at Rutgers University on July 30-31, 2015. Experts in protein crystallography from academe and industry came together with non-profit and for-profit software providers for crystallography and with experts in computational chemistry and data archiving to discuss and make recommendations on best practices, as framed by a series of questions central to structural studies of macromolecule-ligand complexes. What data concerning bound ligands should be archived in the PDB? How should the ligands be best represented? How should structural models of macromolecule-ligand complexes be validated? What supplementary information should accompany publications of structural studies of biological macromolecules? Consensus recommendations on best practices developed in response to each of these questions are provided, together with some details regarding implementation. Important issues addressed but not resolved at the workshop are also enumerated.
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Affiliation(s)
- Paul D Adams
- Molecular Biophysics & Integrated Bioimaging Division, Lawrence Berkeley Laboratory, Department of Bioengineering, UC Berkeley, Berkeley, CA 94720-8235, USA
| | | | - Cary Bauer
- Bruker AXS, Inc., Madison, WI 53711, USA
| | | | - Helen M Berman
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Center for Integrative Proteomics Research, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA; Department of Chemistry and Chemical Biology, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Talapady N Bhat
- Biosystems and Biomaterials Division, NIST, Gaithersburg, MD 20899, USA
| | | | - Evan Bolton
- National Center for Biotechnology Information, U.S. National Library of Medicine, Bethesda, MD 20894, USA
| | | | - David Brown
- School of Biosciences, University of Kent, Canterbury CT2 7NH, UK; Charles River Ltd., Structural Biology and Biophysics, Cambridge CB10 1XL, UK
| | - Stephen K Burley
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Center for Integrative Proteomics Research, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA; Department of Chemistry and Chemical Biology, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA; Skaggs School of Pharmacy and Pharmaceutical Sciences and San Diego Supercomputer Center, University of California, San Diego, La Jolla, CA 92093, USA.
| | - David A Case
- Department of Chemistry and Chemical Biology, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Kirk L Clark
- Novartis Institutes for BioMedical Research, Cambridge, MA 02139, USA
| | - Tom Darden
- OpenEye Scientific, Cambridge, MA 02142, USA
| | - Paul Emsley
- MRC Laboratory of Molecular Biology, Cambridge CB2 0QH, UK
| | - Victoria A Feher
- Drug Design Data Resource and Department of Chemistry and Biochemistry, University of California, San Diego, La Jolla, CA 92093, USA.
| | - Zukang Feng
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Center for Integrative Proteomics Research, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA; Department of Chemistry and Chemical Biology, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Colin R Groom
- Cambridge Crystallographic Data Centre, Cambridge CB2 1EZ, UK.
| | | | - Jorg Hendle
- Structural Biology, Lilly Biotechnology Center, San Diego, CA 92121, USA
| | | | - Andrzej Joachimiak
- Structural Biology Center, Biosciences, Argonne National Laboratory, Argonne, IL 60439, USA
| | - Gerard J Kleywegt
- Protein Data Bank in Europe, European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Tobias Krojer
- Structural Genomics Consortium, University of Oxford, Oxford OX3 7DQ, UK
| | - Joseph Marcotrigiano
- Department of Chemistry and Chemical Biology, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA; Center for Advanced Biotechnology and Medicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Alan E Mark
- School of Chemistry & Molecular Biosciences, University of Queensland, St Lucia, QLD 4072, Australia
| | - John L Markley
- BioMagResBank, Department of Biochemistry, University of Wisconsin-Madison, Madison, WI 53706-1544, USA
| | - Matthew Miller
- Center for Advanced Biotechnology and Medicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Wladek Minor
- Department of Molecular Physiology and Biological Physics, University of Virginia, Charlottesville, VA 22908, USA
| | - Gaetano T Montelione
- Center for Advanced Biotechnology and Medicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA; Department of Molecular Biology and Biochemistry, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | | | - Atsushi Nakagawa
- Protein Data Bank Japan, Institute for Protein Research, Osaka University, Osaka 565-0871, Japan
| | - Haruki Nakamura
- Protein Data Bank Japan, Institute for Protein Research, Osaka University, Osaka 565-0871, Japan
| | | | - Marc Nicklaus
- Computer-Aided Drug Design Group, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Frederick, MD 21702, USA
| | | | | | | | - Susan Pieniazek
- Bristol-Myers Squibb Research and Development, Pennington, NJ 08534, USA
| | - Randy J Read
- Department of Haematology, Cambridge Institute for Medical Research, University of Cambridge, Cambridge CB2 0XY, UK
| | - Chenghua Shao
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Center for Integrative Proteomics Research, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Steven Sheriff
- Bristol-Myers Squibb Research and Development, Princeton, NJ 08543, USA
| | - Oliver Smart
- Protein Data Bank in Europe, European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | | | - John Spurlino
- Janssen Pharmaceuticals, Inc., Spring House, PA 19002, USA
| | - Terry Stouch
- Science For Solutions, LLC, West Windsor, NJ 08550, USA
| | - Radka Svobodova
- CEITEC-Central European Institute of Technology and National Centre for Biomolecular Research, Masaryk University Brno, 625 00 Brno, Czech Republic
| | - Wolfram Tempel
- Structural Genomics Consortium, University of Toronto, Toronto, ON M5G 1L7, Canada
| | | | - Dale Tronrud
- Department of Biochemistry and Biophysics, Oregon State University, Corvallis, OR 97331, USA
| | - Sameer Velankar
- Protein Data Bank in Europe, European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Suzanna C Ward
- Cambridge Crystallographic Data Centre, Cambridge CB2 1EZ, UK
| | | | - John D Westbrook
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Center for Integrative Proteomics Research, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA; Department of Chemistry and Chemical Biology, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | | | - Huanwang Yang
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Center for Integrative Proteomics Research, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA; Department of Chemistry and Chemical Biology, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Jasmine Young
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Center for Integrative Proteomics Research, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA; Department of Chemistry and Chemical Biology, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
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41
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The Intersection of Structural and Chemical Biology - An Essential Synergy. Cell Chem Biol 2016; 23:173-182. [PMID: 26933743 DOI: 10.1016/j.chembiol.2015.12.005] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2015] [Revised: 12/04/2015] [Accepted: 12/04/2015] [Indexed: 12/22/2022]
Abstract
The continual improvement in our ability to generate high resolution structural models of biological molecules has stimulated and supported innovative chemical biology projects that target increasingly challenging ligand interaction sites. In this review we outline some of the recent developments in chemical biology and rational ligand design and show selected examples that illustrate the synergy between these research areas.
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42
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Janowski PA, Moriarty NW, Kelley BP, Case DA, York DM, Adams PD, Warren GL. Improved ligand geometries in crystallographic refinement using AFITT in PHENIX. Acta Crystallogr D Struct Biol 2016; 72:1062-72. [PMID: 27599738 PMCID: PMC5013598 DOI: 10.1107/s2059798316012225] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2015] [Accepted: 07/27/2016] [Indexed: 11/24/2022] Open
Abstract
Modern crystal structure refinement programs rely on geometry restraints to overcome the challenge of a low data-to-parameter ratio. While the classical Engh and Huber restraints work well for standard amino-acid residues, the chemical complexity of small-molecule ligands presents a particular challenge. Most current approaches either limit ligand restraints to those that can be readily described in the Crystallographic Information File (CIF) format, thus sacrificing chemical flexibility and energetic accuracy, or they employ protocols that substantially lengthen the refinement time, potentially hindering rapid automated refinement workflows. PHENIX-AFITT refinement uses a full molecular-mechanics force field for user-selected small-molecule ligands during refinement, eliminating the potentially difficult problem of finding or generating high-quality geometry restraints. It is fully integrated with a standard refinement protocol and requires practically no additional steps from the user, making it ideal for high-throughput workflows. PHENIX-AFITT refinements also handle multiple ligands in a single model, alternate conformations and covalently bound ligands. Here, the results of combining AFITT and the PHENIX software suite on a data set of 189 protein-ligand PDB structures are presented. Refinements using PHENIX-AFITT significantly reduce ligand conformational energy and lead to improved geometries without detriment to the fit to the experimental data. For the data presented, PHENIX-AFITT refinements result in more chemically accurate models for small-molecule ligands.
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Affiliation(s)
- Pawel A. Janowski
- BioMaPs Institute, Center for Integrative Proteomics Research, Rutgers University, Piscataway, NJ 08854, USA
- Department of Chemistry and Chemical Biology, Rutgers University, Piscataway, NJ 08854, USA
| | - Nigel W. Moriarty
- Molecular Biophysics and Integrated Bioimaging, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - Brian P. Kelley
- Novartis Institutes for BioMedical Research Inc., Cambridge, MA 02139, USA
| | - David A. Case
- BioMaPs Institute, Center for Integrative Proteomics Research, Rutgers University, Piscataway, NJ 08854, USA
- Department of Chemistry and Chemical Biology, Rutgers University, Piscataway, NJ 08854, USA
| | - Darrin M. York
- BioMaPs Institute, Center for Integrative Proteomics Research, Rutgers University, Piscataway, NJ 08854, USA
- Department of Chemistry and Chemical Biology, Rutgers University, Piscataway, NJ 08854, USA
| | - Paul D. Adams
- Molecular Biophysics and Integrated Bioimaging, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
- Department of Bioengineering, University of California at Berkeley, Berkeley, CA 94720, USA
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43
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Stanfield R, Pozharski E, Rupp B. Comment on Three X-ray Crystal Structure Papers. THE JOURNAL OF IMMUNOLOGY 2016; 196:521-4. [PMID: 26747564 DOI: 10.4049/jimmunol.1501343] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
Affiliation(s)
| | - Edwin Pozharski
- School of Medicine, University of Maryland, Baltimore, MD 21201;
| | - Bernhard Rupp
- Medical University of Innsbruck, A 6020 Innsbruck, Austria; and Department of Forensic Crystallography, k.-k. Hofkristallamt, Vista, CA 92084
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44
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Touw WG, Joosten RP, Vriend G. New Biological Insights from Better Structure Models. J Mol Biol 2016; 428:1375-1393. [PMID: 26869101 DOI: 10.1016/j.jmb.2016.02.002] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2015] [Revised: 01/04/2016] [Accepted: 02/01/2016] [Indexed: 02/01/2023]
Abstract
Structure validation is a key component of all steps in the structure determination process, from structure building, refinement, deposition, and evaluation all the way to post-deposition optimisation of structures in the Protein Data Bank (PDB) by re-refinement and re-building. Today, many aspects of protein structures are understood better than 10years ago, and combined with improved software and more computing power, the automated PDB_REDO procedure can significantly improve about 85% of all X-ray structures ever deposited in the PDB. We review structure validation, structure improvement, and a series of validation resources and facilities that give access to improved PDB files and to reports on the quality of the original and the improved structures. Post-deposition optimisation generally leads to improved protein structures and a series of examples will illustrate how that, in turn, leads to improved or even novel biological insights.
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Affiliation(s)
- Wouter G Touw
- Centre for Molecular and Biomolecular Informatics, Radboud University Medical Center, Geert Grooteplein-Zuid 26-28, 6525 GA Nijmegen, The Netherlands
| | - Robbie P Joosten
- Department of Biochemistry, Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX Amsterdam, The Netherlands
| | - Gert Vriend
- Centre for Molecular and Biomolecular Informatics, Radboud University Medical Center, Geert Grooteplein-Zuid 26-28, 6525 GA Nijmegen, The Netherlands.
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45
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Porebski PJ, Cymborowski M, Pasenkiewicz-Gierula M, Minor W. Fitmunk: improving protein structures by accurate, automatic modeling of side-chain conformations. Acta Crystallogr D Struct Biol 2016; 72:266-80. [PMID: 26894674 PMCID: PMC4756610 DOI: 10.1107/s2059798315024730] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2015] [Accepted: 12/23/2015] [Indexed: 11/21/2022] Open
Abstract
Improvements in crystallographic hardware and software have allowed automated structure-solution pipelines to approach a near-`one-click' experience for the initial determination of macromolecular structures. However, in many cases the resulting initial model requires a laborious, iterative process of refinement and validation. A new method has been developed for the automatic modeling of side-chain conformations that takes advantage of rotamer-prediction methods in a crystallographic context. The algorithm, which is based on deterministic dead-end elimination (DEE) theory, uses new dense conformer libraries and a hybrid energy function derived from experimental data and prior information about rotamer frequencies to find the optimal conformation of each side chain. In contrast to existing methods, which incorporate the electron-density term into protein-modeling frameworks, the proposed algorithm is designed to take advantage of the highly discriminatory nature of electron-density maps. This method has been implemented in the program Fitmunk, which uses extensive conformational sampling. This improves the accuracy of the modeling and makes it a versatile tool for crystallographic model building, refinement and validation. Fitmunk was extensively tested on over 115 new structures, as well as a subset of 1100 structures from the PDB. It is demonstrated that the ability of Fitmunk to model more than 95% of side chains accurately is beneficial for improving the quality of crystallographic protein models, especially at medium and low resolutions. Fitmunk can be used for model validation of existing structures and as a tool to assess whether side chains are modeled optimally or could be better fitted into electron density. Fitmunk is available as a web service at http://kniahini.med.virginia.edu/fitmunk/server/ or at http://fitmunk.bitbucket.org/.
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Affiliation(s)
- Przemyslaw Jerzy Porebski
- Department of Molecular Physiology and Biological Physics, University of Virginia, Jordan Hall, 1340 Jefferson Park Avenue, Charlottesville, VA 22908, USA
- Faculty of Biochemistry, Biophysics and Biotechnology, Jagiellonian University, ul. Gronostajowa 7, 30-387 Kraków, Poland
| | - Marcin Cymborowski
- Department of Molecular Physiology and Biological Physics, University of Virginia, Jordan Hall, 1340 Jefferson Park Avenue, Charlottesville, VA 22908, USA
| | - Marta Pasenkiewicz-Gierula
- Faculty of Biochemistry, Biophysics and Biotechnology, Jagiellonian University, ul. Gronostajowa 7, 30-387 Kraków, Poland
| | - Wladek Minor
- Department of Molecular Physiology and Biological Physics, University of Virginia, Jordan Hall, 1340 Jefferson Park Avenue, Charlottesville, VA 22908, USA
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46
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Abstract
The use of macromolecular structures is widespread for a variety of applications, from teaching protein structure principles all the way to ligand optimization in drug development. Applying data mining techniques on these experimentally determined structures requires a highly uniform, standardized structural data source. The Protein Data Bank (PDB) has evolved over the years toward becoming the standard resource for macromolecular structures. However, the process selecting the data most suitable for specific applications is still very much based on personal preferences and understanding of the experimental techniques used to obtain these models. In this chapter, we will first explain the challenges with data standardization, annotation, and uniformity in the PDB entries determined by X-ray crystallography. We then discuss the specific effect that crystallographic data quality and model optimization methods have on structural models and how validation tools can be used to make informed choices. We also discuss specific advantages of using the PDB_REDO databank as a resource for structural data. Finally, we will provide guidelines on how to select the most suitable protein structure models for detailed analysis and how to select a set of structure models suitable for data mining.
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Affiliation(s)
- Bart van Beusekom
- Department of Biochemistry, Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX, Amsterdam, The Netherlands
| | - Anastassis Perrakis
- Department of Biochemistry, Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX, Amsterdam, The Netherlands
| | - Robbie P Joosten
- Department of Biochemistry, Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX, Amsterdam, The Netherlands.
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47
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Lin J, van den Bedem H, Brunger AT, Wilson MA. Atomic resolution experimental phase information reveals extensive disorder and bound 2-methyl-2,4-pentanediol in Ca(2+)-calmodulin. Acta Crystallogr D Struct Biol 2016; 72:83-92. [PMID: 26894537 PMCID: PMC4756614 DOI: 10.1107/s2059798315021609] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2015] [Accepted: 11/14/2015] [Indexed: 11/10/2022] Open
Abstract
Calmodulin (CaM) is the primary calcium signaling protein in eukaryotes and has been extensively studied using various biophysical techniques. Prior crystal structures have noted the presence of ambiguous electron density in both hydrophobic binding pockets of Ca(2+)-CaM, but no assignment of these features has been made. In addition, Ca(2+)-CaM samples many conformational substates in the crystal and accurately modeling the full range of this functionally important disorder is challenging. In order to characterize these features in a minimally biased manner, a 1.0 Å resolution single-wavelength anomalous diffraction data set was measured for selenomethionine-substituted Ca(2+)-CaM. Density-modified electron-density maps enabled the accurate assignment of Ca(2+)-CaM main-chain and side-chain disorder. These experimental maps also substantiate complex disorder models that were automatically built using low-contour features of model-phased electron density. Furthermore, experimental electron-density maps reveal that 2-methyl-2,4-pentanediol (MPD) is present in the C-terminal domain, mediates a lattice contact between N-terminal domains and may occupy the N-terminal binding pocket. The majority of the crystal structures of target-free Ca(2+)-CaM have been derived from crystals grown using MPD as a precipitant, and thus MPD is likely to be bound in functionally critical regions of Ca(2+)-CaM in most of these structures. The adventitious binding of MPD helps to explain differences between the Ca(2+)-CaM crystal and solution structures and is likely to favor more open conformations of the EF-hands in the crystal.
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Affiliation(s)
- Jiusheng Lin
- Department of Biochemistry and Redox Biology Center, University of Nebraska, Beadle Center, Lincoln, NE 68588, USA
| | - Henry van den Bedem
- Biosciences Division, SLAC National Accelerator Laboratory, Stanford University, Menlo Park, CA 94025, USA
| | - Axel T. Brunger
- Departments of Molecular and Cellular Physiology, Neurology and Neurological Sciences, Structural Biology, and Photon Science, Stanford University and Howard Hughes Medical Institute, Stanford, CA 94305, USA
| | - Mark A. Wilson
- Department of Biochemistry and Redox Biology Center, University of Nebraska, Beadle Center, Lincoln, NE 68588, USA
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Dauter Z, Wlodawer A. Progress in protein crystallography. Protein Pept Lett 2016; 23:201-10. [PMID: 26732246 PMCID: PMC6287266 DOI: 10.2174/0929866523666160106153524] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2015] [Revised: 10/26/2015] [Accepted: 01/03/2016] [Indexed: 11/22/2022]
Abstract
Macromolecular crystallography evolved enormously from the pioneering days, when structures were solved by "wizards" performing all complicated procedures almost by hand. In the current situation crystal structures of large systems can be often solved very effectively by various powerful automatic programs in days or hours, or even minutes. Such progress is to a large extent coupled to the advances in many other fields, such as genetic engineering, computer technology, availability of synchrotron beam lines and many other techniques, creating the highly interdisciplinary science of macromolecular crystallography. Due to this unprecedented success crystallography is often treated as one of the analytical methods and practiced by researchers interested in structures of macromolecules, but not highly competent in the procedures involved in the process of structure determination. One should therefore take into account that the contemporary, highly automatic systems can produce results almost without human intervention, but the resulting structures must be carefully checked and validated before their release into the public domain.
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Affiliation(s)
- Zbigniew Dauter
- Macromolecular Crystallography Laboratory, National Cancer Institute, Frederick, MD and Argonne, IL, USA.
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49
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Deller MC, Rupp B. Models of protein-ligand crystal structures: trust, but verify. J Comput Aided Mol Des 2015; 29:817-36. [PMID: 25665575 PMCID: PMC4531100 DOI: 10.1007/s10822-015-9833-8] [Citation(s) in RCA: 59] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2014] [Accepted: 01/29/2015] [Indexed: 11/26/2022]
Abstract
X-ray crystallography provides the most accurate models of protein-ligand structures. These models serve as the foundation of many computational methods including structure prediction, molecular modelling, and structure-based drug design. The success of these computational methods ultimately depends on the quality of the underlying protein-ligand models. X-ray crystallography offers the unparalleled advantage of a clear mathematical formalism relating the experimental data to the protein-ligand model. In the case of X-ray crystallography, the primary experimental evidence is the electron density of the molecules forming the crystal. The first step in the generation of an accurate and precise crystallographic model is the interpretation of the electron density of the crystal, typically carried out by construction of an atomic model. The atomic model must then be validated for fit to the experimental electron density and also for agreement with prior expectations of stereochemistry. Stringent validation of protein-ligand models has become possible as a result of the mandatory deposition of primary diffraction data, and many computational tools are now available to aid in the validation process. Validation of protein-ligand complexes has revealed some instances of overenthusiastic interpretation of ligand density. Fundamental concepts and metrics of protein-ligand quality validation are discussed and we highlight software tools to assist in this process. It is essential that end users select high quality protein-ligand models for their computational and biological studies, and we provide an overview of how this can be achieved.
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Affiliation(s)
- Marc C Deller
- The Joint Center for Structural Genomics, San Diego, CA, USA
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, 10550 North Torrey Pines Road, La Jolla, CA, 92037, USA
| | - Bernhard Rupp
- , k.-k. Hofkristallamt 991 Audrey Place, Vista, CA, 92084, USA.
- Department of Genetic Epidemiology, Medical University of Innsbruck, Schöpfstr. 41, 6020, Innsbruck, Austria.
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50
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Zheng H, Handing KB, Zimmerman MD, Shabalin IG, Almo SC, Minor W. X-ray crystallography over the past decade for novel drug discovery - where are we heading next? Expert Opin Drug Discov 2015; 10:975-89. [PMID: 26177814 DOI: 10.1517/17460441.2015.1061991] [Citation(s) in RCA: 50] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
INTRODUCTION Macromolecular X-ray crystallography has been the primary methodology for determining the three-dimensional structures of proteins, nucleic acids and viruses. Structural information has paved the way for structure-guided drug discovery and laid the foundations for structural bioinformatics. However, X-ray crystallography still has a few fundamental limitations, some of which may be overcome and complemented using emerging methods and technologies in other areas of structural biology. AREAS COVERED This review describes how structural knowledge gained from X-ray crystallography has been used to advance other biophysical methods for structure determination (and vice versa). This article also covers current practices for integrating data generated by other biochemical and biophysical methods with those obtained from X-ray crystallography. Finally, the authors articulate their vision about how a combination of structural and biochemical/biophysical methods may improve our understanding of biological processes and interactions. EXPERT OPINION X-ray crystallography has been, and will continue to serve as, the central source of experimental structural biology data used in the discovery of new drugs. However, other structural biology techniques are useful not only to overcome the major limitation of X-ray crystallography, but also to provide complementary structural data that is useful in drug discovery. The use of recent advancements in biochemical, spectroscopy and bioinformatics methods may revolutionize drug discovery, albeit only when these data are combined and analyzed with effective data management systems. Accurate and complete data management is crucial for developing experimental procedures that are robust and reproducible.
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Affiliation(s)
- Heping Zheng
- University of Virginia, Department of Molecular Physiology and Biological Physics , 1340 Jefferson Park Avenue, Charlottesville, VA 22908 , USA +1 434 243 6865 ; +1 434 243 2981 ;
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