1
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Abstract
Glycoscience assembles all the scientific disciplines involved in studying various molecules and macromolecules containing carbohydrates and complex glycans. Such an ensemble involves one of the most extensive sets of molecules in quantity and occurrence since they occur in all microorganisms and higher organisms. Once the compositions and sequences of these molecules are established, the determination of their three-dimensional structural and dynamical features is a step toward understanding the molecular basis underlying their properties and functions. The range of the relevant computational methods capable of addressing such issues is anchored by the specificity of stereoelectronic effects from quantum chemistry to mesoscale modeling throughout molecular dynamics and mechanics and coarse-grained and docking calculations. The Review leads the reader through the detailed presentations of the applications of computational modeling. The illustrations cover carbohydrate-carbohydrate interactions, glycolipids, and N- and O-linked glycans, emphasizing their role in SARS-CoV-2. The presentation continues with the structure of polysaccharides in solution and solid-state and lipopolysaccharides in membranes. The full range of protein-carbohydrate interactions is presented, as exemplified by carbohydrate-active enzymes, transporters, lectins, antibodies, and glycosaminoglycan binding proteins. A final section features a list of 150 tools and databases to help address the many issues of structural glycobioinformatics.
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Affiliation(s)
- Serge Perez
- Centre de Recherche sur les Macromolecules Vegetales, University of Grenoble-Alpes, Centre National de la Recherche Scientifique, Grenoble F-38041, France
| | - Olga Makshakova
- FRC Kazan Scientific Center of Russian Academy of Sciences, Kazan Institute of Biochemistry and Biophysics, Kazan 420111, Russia
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2
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GAG-DB, the New Interface of the Three-Dimensional Landscape of Glycosaminoglycans. Biomolecules 2020; 10:biom10121660. [PMID: 33322545 PMCID: PMC7763844 DOI: 10.3390/biom10121660] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2020] [Revised: 12/03/2020] [Accepted: 12/09/2020] [Indexed: 12/18/2022] Open
Abstract
Glycosaminoglycans (GAGs) are complex linear polysaccharides. GAG-DB is a curated database that classifies the three-dimensional features of the six mammalian GAGs (chondroitin sulfate, dermatan sulfate, heparin, heparan sulfate, hyaluronan, and keratan sulfate) and their oligosaccharides complexed with proteins. The entries are structures of GAG and GAG-protein complexes determined by X-ray single-crystal diffraction methods, X-ray fiber diffractometry, solution NMR spectroscopy, and scattering data often associated with molecular modeling. We designed the database architecture and the navigation tools to query the database with the Protein Data Bank (PDB), UniProtKB, and GlyTouCan (universal glycan repository) identifiers. Special attention was devoted to the description of the bound glycan ligands using simple graphical representation and numerical format for cross-referencing to other databases in glycoscience and functional data. GAG-DB provides detailed information on GAGs, their bound protein ligands, and features their interactions using several open access applications. Binding covers interactions between monosaccharides and protein monosaccharide units and the evaluation of quaternary structure. GAG-DB is freely available.
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3
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Sehnal D, Svobodová R, Berka K, Rose AS, Burley SK, Velankar S, Koča J. High-performance macromolecular data delivery and visualization for the web. Acta Crystallogr D Struct Biol 2020; 76:1167-1173. [PMID: 33263322 PMCID: PMC7709201 DOI: 10.1107/s2059798320014515] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2020] [Accepted: 11/01/2020] [Indexed: 11/11/2022] Open
Abstract
Biomacromolecular structural data make up a vital and crucial scientific resource that has grown not only in terms of its amount but also in its size and complexity. Furthermore, these data are accompanied by large and increasing amounts of experimental data. Additionally, the macromolecular data are enriched with value-added annotations describing their biological, physicochemical and structural properties. Today, the scientific community requires fast and fully interactive web visualization to exploit this complex structural information. This article provides a survey of the available cutting-edge web services that address this challenge. Specifically, it focuses on data-delivery problems, discusses the visualization of a single structure, including experimental data and annotations, and concludes with a focus on the results of molecular-dynamics simulations and the visualization of structural ensembles.
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Affiliation(s)
- David Sehnal
- CEITEC – Central European Institute of Technology, Masaryk University, Kamenice 753/5, 625 00 Brno, Czech Republic
- National Centre for Biomolecular Research, Faculty of Science, Masaryk University, Kamenice 753/5, 625 00 Brno, Czech Republic
- Protein Data Bank in Europe (PDBe), European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL–EBI), Wellcome Genome Campus, Hinxton CB10 1SD, United Kingdom
| | - Radka Svobodová
- CEITEC – Central European Institute of Technology, Masaryk University, Kamenice 753/5, 625 00 Brno, Czech Republic
- National Centre for Biomolecular Research, Faculty of Science, Masaryk University, Kamenice 753/5, 625 00 Brno, Czech Republic
| | - Karel Berka
- Regional Centre of Advanced Technologies and Materials, Department of Physical Chemistry, Faculty of Science, Palacký University Olomouc, Šlechtitelů 241/27, 779 00 Olomouc, Czech Republic
| | - Alexander S. Rose
- Research Collaboratory for Structural Bioinformatics (RCSB), San Diego Supercomputer Center, University of California San Diego, 9500 Gilman Drive, La Jolla, San Diego, CA 92093-0743, USA
| | - Stephen K. Burley
- RCSB Protein Data Bank, Institute for Quantitative Biomedicine and Department of Chemistry and Chemical Biology, Rutgers, The State University of New Jersey, 174 Frelinghuysen Road, Piscataway, NJ 08854-8076, USA
- Cancer Institute of New Jersey, Rutgers, The State University of New Jersey, 195 Little Albany Street, New Brunswick, NJ 08903-2681, USA
- RCSB Protein Data Bank, San Diego Supercomputer Center and Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, 9500 Gilman Drive, La Jolla, CA 92093-0654, USA
| | - Sameer Velankar
- Protein Data Bank in Europe (PDBe), European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL–EBI), Wellcome Genome Campus, Hinxton CB10 1SD, United Kingdom
| | - Jaroslav Koča
- CEITEC – Central European Institute of Technology, Masaryk University, Kamenice 753/5, 625 00 Brno, Czech Republic
- National Centre for Biomolecular Research, Faculty of Science, Masaryk University, Kamenice 753/5, 625 00 Brno, Czech Republic
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4
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Scherbinina SI, Toukach PV. Three-Dimensional Structures of Carbohydrates and Where to Find Them. Int J Mol Sci 2020; 21:E7702. [PMID: 33081008 PMCID: PMC7593929 DOI: 10.3390/ijms21207702] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2020] [Revised: 10/15/2020] [Accepted: 10/16/2020] [Indexed: 02/06/2023] Open
Abstract
Analysis and systematization of accumulated data on carbohydrate structural diversity is a subject of great interest for structural glycobiology. Despite being a challenging task, development of computational methods for efficient treatment and management of spatial (3D) structural features of carbohydrates breaks new ground in modern glycoscience. This review is dedicated to approaches of chemo- and glyco-informatics towards 3D structural data generation, deposition and processing in regard to carbohydrates and their derivatives. Databases, molecular modeling and experimental data validation services, and structure visualization facilities developed for last five years are reviewed.
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Affiliation(s)
- Sofya I. Scherbinina
- N.D. Zelinsky Institute of Organic Chemistry, Russian Academy of Science, Leninsky prospect 47, 119991 Moscow, Russia
- Higher Chemical College, D. Mendeleev University of Chemical Technology of Russia, Miusskaya Square 9, 125047 Moscow, Russia
| | - Philip V. Toukach
- N.D. Zelinsky Institute of Organic Chemistry, Russian Academy of Science, Leninsky prospect 47, 119991 Moscow, Russia
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5
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Houser J, Kozmon S, Mishra D, Hammerová Z, Wimmerová M, Koča J. The CH-π Interaction in Protein-Carbohydrate Binding: Bioinformatics and In Vitro Quantification. Chemistry 2020; 26:10769-10780. [PMID: 32208534 DOI: 10.1002/chem.202000593] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2020] [Revised: 03/18/2020] [Indexed: 12/16/2022]
Abstract
The molecular recognition of carbohydrates by proteins plays a key role in many biological processes including immune response, pathogen entry into a cell, and cell-cell adhesion (e.g., in cancer metastasis). Carbohydrates interact with proteins mainly through hydrogen bonding, metal-ion-mediated interaction, and non-polar dispersion interactions. The role of dispersion-driven CH-π interactions (stacking) in protein-carbohydrate recognition has been underestimated for a long time considering the polar interactions to be the main forces for saccharide interactions. However, over the last few years it turns out that non-polar interactions are equally important. In this study, we analyzed the CH-π interactions employing bioinformatics (data mining, structural analysis), several experimental (isothermal titration calorimetry (ITC), X-ray crystallography), and computational techniques. The Protein Data Bank (PDB) has been used as a source of structural data. The PDB contains over 12 000 protein complexes with carbohydrates. Stacking interactions are very frequently present in such complexes (about 39 % of identified structures). The calculations and the ITC measurement results suggest that the CH-π stacking contribution to the overall binding energy ranges from 4 up to 8 kcal mol-1 . All the results show that the stacking CH-π interactions in protein-carbohydrate complexes can be considered to be a driving force of the binding in such complexes.
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Affiliation(s)
- Josef Houser
- Central European Institute of Technology, Masaryk University, Kamenice 5, 62500, Brno, Czech Republic.,National Centre for Biomolecular Research, Faculty of Science, Masaryk University, Kotlářská 2, 61137, Brno, Czech Republic
| | - Stanislav Kozmon
- Central European Institute of Technology, Masaryk University, Kamenice 5, 62500, Brno, Czech Republic.,Institute of Chemistry, Slovak Academy of Sciences, Dúbravská cesta 9, 84538, Bratislava, Slovak Republic
| | - Deepti Mishra
- Central European Institute of Technology, Masaryk University, Kamenice 5, 62500, Brno, Czech Republic
| | - Zuzana Hammerová
- National Centre for Biomolecular Research, Faculty of Science, Masaryk University, Kotlářská 2, 61137, Brno, Czech Republic
| | - Michaela Wimmerová
- Central European Institute of Technology, Masaryk University, Kamenice 5, 62500, Brno, Czech Republic.,National Centre for Biomolecular Research, Faculty of Science, Masaryk University, Kotlářská 2, 61137, Brno, Czech Republic.,Department of Biochemistry, Faculty of Science, Masaryk University, Kotlářská 2, 611 37, Brno, Czech Republic
| | - Jaroslav Koča
- Central European Institute of Technology, Masaryk University, Kamenice 5, 62500, Brno, Czech Republic.,National Centre for Biomolecular Research, Faculty of Science, Masaryk University, Kotlářská 2, 61137, Brno, Czech Republic
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6
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Horský V, Bendová V, Toušek D, Koča J, Svobodová R. ValTrendsDB: bringing Protein Data Bank validation information closer to the user. Bioinformatics 2020; 35:5389-5390. [PMID: 31263870 PMCID: PMC6954638 DOI: 10.1093/bioinformatics/btz532] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2019] [Revised: 06/01/2019] [Accepted: 06/28/2019] [Indexed: 11/29/2022] Open
Abstract
Summary Structures in PDB tend to contain errors. This is a very serious issue for authors that rely on such potentially problematic data. The community of structural biologists develops validation methods as countermeasures, which are also included in the PDB deposition system. But how are these validation efforts influencing the structure quality of subsequently published data? Which quality aspects are improving, and which remain problematic? We developed ValTrendsDB, a database that provides the results of an extensive exploratory analysis of relationships between quality criteria, size and metadata of biomacromolecules. Key input data are sourced from PDB. The discovered trends are presented via precomputed information-rich plots. ValTrendsDB also supports the visualization of a set of user-defined structures on top of general quality trends. Therefore, ValTrendsDB enables users to see the quality of structures published by selected author, laboratory or journal, discover quality outliers, etc. ValTrendsDB is updated weekly. Availability and implementation Freely accessible at http://ncbr.muni.cz/ValTrendsDB. The web interface was implemented in JavaScript. The database was implemented in C++. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Vladimír Horský
- National Centre for Biomolecular Research, Faculty of Science, Masaryk University, Brno, Czech Republic.,CEITEC - Central European Institute of Technology, Masaryk University, Brno, Czech Republic
| | - Veronika Bendová
- National Centre for Biomolecular Research, Faculty of Science, Masaryk University, Brno, Czech Republic.,CEITEC - Central European Institute of Technology, Masaryk University, Brno, Czech Republic.,Institute of Mathematics and Statistics, Faculty of Science, Masaryk University, Brno, Czech Republic
| | - Dominik Toušek
- National Centre for Biomolecular Research, Faculty of Science, Masaryk University, Brno, Czech Republic.,CEITEC - Central European Institute of Technology, Masaryk University, Brno, Czech Republic
| | - Jaroslav Koča
- National Centre for Biomolecular Research, Faculty of Science, Masaryk University, Brno, Czech Republic.,CEITEC - Central European Institute of Technology, Masaryk University, Brno, Czech Republic
| | - Radka Svobodová
- National Centre for Biomolecular Research, Faculty of Science, Masaryk University, Brno, Czech Republic.,CEITEC - Central European Institute of Technology, Masaryk University, Brno, Czech Republic
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7
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Abstract
LiteMol suite is an innovative solution that enables near-instant delivery of model and experimental biomacromolecular structural data, providing users with an interactive and responsive experience in all modern web browsers and mobile devices. LiteMol suite is a combination of data delivery services (CoordinateServer and DensityServer), compression format (BinaryCIF), and a molecular viewer (LiteMol Viewer). The LiteMol suite is integrated into Protein Data Bank in Europe (PDBe) and other life science web applications (e.g., UniProt, Ensemble, SIB, and CNRS services), it is freely available at https://litemol.org , and its source code is available via GitHub. LiteMol suite provides advanced functionality (annotations and their visualization, powerful selection features), and this chapter will describe their use for visual inspection of protein structures.
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8
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Chen L, Baker B, Santos E, Sheep M, Daftarian D. A Visualization Tool for Cryo-EM Protein Validation with an Unsupervised Machine Learning Model in Chimera Platform. MEDICINES (BASEL, SWITZERLAND) 2019; 6:E86. [PMID: 31390767 PMCID: PMC6789601 DOI: 10.3390/medicines6030086] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/01/2019] [Revised: 07/31/2019] [Accepted: 08/02/2019] [Indexed: 11/22/2022]
Abstract
Background: Cryo-electron microscopy (cryo-EM) has become a major technique for protein structure determination. However, due to the low quality of cryo-EM density maps, many protein structures derived from cryo-EM contain outliers introduced during the modeling process. The current protein model validation system lacks identification features for cryo-EM proteins making it not enough to identify outliers in cryo-EM proteins. Methods: This study introduces an efficient unsupervised outlier detection model for validating protein models built from cryo-EM technique. The current model uses a high-resolution X-ray dataset (<1.5 Å) as the reference dataset. The distal block distance, side-chain length, phi, psi, and first chi angle of the residues in the reference dataset are collected and saved as a database of the histogram-based outlier score (HBOS). The HBOS value of the residues in target cryo-EM proteins can be read from this HBOS database. Results: Protein residues with a HBOS value greater than ten are labeled as outliers by default. Four datasets containing proteins derived from cryo-EM density maps were tested with this probabilistic anomaly detection model. Conclusions: According to the proposed model, a visualization assistant tool was designed for Chimera, a protein visualization platform.
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Affiliation(s)
- Lin Chen
- Department of Computer Science, Valdosta State University, Valdosta, GA 31693, USA.
| | - Brandon Baker
- Department of Natural Science, Elizabeth City State University, Elizabeth City, NC 27909, USA
| | - Eduardo Santos
- Department of Natural Science, Elizabeth City State University, Elizabeth City, NC 27909, USA
| | - Michell Sheep
- Department of Mathematics & Computer Science, Elizabeth City State University, Elizabeth City, NC 27909, USA
| | - Darius Daftarian
- Department of Mathematics & Computer Science, Elizabeth City State University, Elizabeth City, NC 27909, USA
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9
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Abstract
The representation of carbohydrates in 3D space using symbols is a powerful visualization method, but such representations are lacking in currently available visualization software. The work presented here allows researchers to display carbohydrate 3D structures as 3D-SNFG symbols using LiteMol from a web browser (e.g., v.litemol.org/?loadFromCS=5T3X ). Any PDB ID can be substituted at the end of the URL. Alternatively, the user may enter a PDB ID or upload a structure. LiteMol is available at https://v.litemol.org and automatically depicts any carbohydrate residues as 3D-SNFG symbols. To embed LiteMol in a webpage, visit https://github.com/dsehnal/LiteMol .
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Affiliation(s)
- David Sehnal
- CEITEC - Central European Institute of Technology , Masaryk University Brno , Kamenice 5 , 625 00 Brno-Bohunice , Czech Republic.,National Centre for Biomolecular Research , Faculty of Science , Kamenice 5 , 625 00 Brno-Bohunice , Czech Republic.,Protein Data Bank in Europe (PDBe), European Molecular Biology Laboratory , European Bioinformatics Institute (EMBL-EBI) , Wellcome Genome Campus , Hinxton , Cambridgeshire CB10 1SD , United Kingdom
| | - Oliver C Grant
- Complex Carbohydrate Research Center , University of Georgia , Athens , Georgia 30602 , United States
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10
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Smart OS, Horský V, Gore S, Svobodová Vařeková R, Bendová V, Kleywegt GJ, Velankar S. Worldwide Protein Data Bank validation information: usage and trends. Acta Crystallogr D Struct Biol 2018; 74:237-244. [PMID: 29533231 PMCID: PMC5947764 DOI: 10.1107/s2059798318003303] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2017] [Accepted: 02/26/2018] [Indexed: 11/10/2022] Open
Abstract
Realising the importance of assessing the quality of the biomolecular structures deposited in the Protein Data Bank (PDB), the Worldwide Protein Data Bank (wwPDB) partners established Validation Task Forces to obtain advice on the methods and standards to be used to validate structures determined by X-ray crystallography, nuclear magnetic resonance spectroscopy and three-dimensional electron cryo-microscopy. The resulting wwPDB validation pipeline is an integral part of the wwPDB OneDep deposition, biocuration and validation system. The wwPDB Validation Service webserver (https://validate.wwpdb.org) can be used to perform checks prior to deposition. Here, it is shown how validation metrics can be combined to produce an overall score that allows the ranking of macromolecular structures and domains in search results. The ValTrendsDB database provides users with a convenient way to access and analyse validation information and other properties of X-ray crystal structures in the PDB, including investigating trends in and correlations between different structure properties and validation metrics.
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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
- Faculty of Science, National Centre for Biomolecular Research, 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
- CEITEC - Central European Institute of Technology, Masaryk University, Kamenice 5, 625 00 Brno, Czech Republic
| | - Veronika Bendová
- Faculty of Science, National Centre for Biomolecular Research, Kamenice 5, 625 00 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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11
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Laskowski RA, Jabłońska J, Pravda L, Vařeková RS, Thornton JM. PDBsum: Structural summaries of PDB entries. Protein Sci 2017; 27:129-134. [PMID: 28875543 PMCID: PMC5734310 DOI: 10.1002/pro.3289] [Citation(s) in RCA: 780] [Impact Index Per Article: 111.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2017] [Revised: 08/29/2017] [Accepted: 08/29/2017] [Indexed: 12/11/2022]
Abstract
PDBsum is a web server providing structural information on the entries in the Protein Data Bank (PDB). The analyses are primarily image‐based and include protein secondary structure, protein‐ligand and protein‐DNA interactions, PROCHECK analyses of structural quality, and many others. The 3D structures can be viewed interactively in RasMol, PyMOL, and a JavaScript viewer called 3Dmol.js. Users can upload their own PDB files and obtain a set of password‐protected PDBsum analyses for each. The server is freely accessible to all at: http://www.ebi.ac.uk/pdbsum.
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Affiliation(s)
- Roman A Laskowski
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, United Kingdom
| | - Jagoda Jabłońska
- Laboratory of Bioinformatics and Systems Biology, Centre of New Technologies, University of Warsaw, 02-089, Warsaw, Poland
| | - Lukáš Pravda
- National Centre for Biomolecular Research, Faculty of Science and CEITEC - Central European Institute of Technology, Masaryk University Brno, 625 00, Brno-Bohunice, Czech Republic
| | - Radka Svobodová Vařeková
- National Centre for Biomolecular Research, Faculty of Science and CEITEC - Central European Institute of Technology, Masaryk University Brno, 625 00, Brno-Bohunice, Czech Republic
| | - Janet M Thornton
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, United Kingdom
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12
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Abstract
Coot is a molecular-graphics program primarily aimed at model building using X-ray data. Recently, tools for the manipulation and representation of ligands have been introduced. Here, these new tools for ligand validation and comparison are described. Ligands in the wwPDB have been scored by density-fit, distortion and atom-clash metrics. The distributions of these scores can be used to assess the relative merits of the particular ligand in the protein-ligand complex of interest by means of `sliders' akin to those now available for each accession code on the wwPDB websites.
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Affiliation(s)
- Paul Emsley
- MRC Laboratory of Molecular Biology, Francis Crick Avenue, Cambridge Biomedical Campus, Cambridge CB2 0QH, England
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13
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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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The Eighth Central European Conference "Chemistry towards Biology": Snapshot. Molecules 2016; 21:molecules21101381. [PMID: 27763518 PMCID: PMC5283649 DOI: 10.3390/molecules21101381] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2016] [Accepted: 10/12/2016] [Indexed: 01/27/2023] Open
Abstract
The Eighth Central European Conference "Chemistry towards Biology" was held in Brno, Czech Republic, on August 28-September 1, 2016 to bring together experts in biology, chemistry and design of bioactive compounds; promote the exchange of scientific results, methods and ideas; and encourage cooperation between researchers from all over the world. The topics of the conference covered "Chemistry towards Biology", meaning that the event welcomed chemists working on biology-related problems, biologists using chemical methods, and students and other researchers of the respective areas that fall within the common scope of chemistry and biology. The authors of this manuscript are plenary speakers and other participants of the symposium and members of their research teams. The following summary highlights the major points/topics of the meeting.
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15
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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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16
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Galperin MY, Rigden DJ, Fernández-Suárez XM. The 2015 Nucleic Acids Research Database Issue and molecular biology database collection. Nucleic Acids Res 2015; 43:D1-5. [PMID: 25593347 DOI: 10.1093/nar/gku1241] [Citation(s) in RCA: 38] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022] Open
Abstract
The 2015 Nucleic Acids Research Database Issue contains 172 papers that include descriptions of 56 new molecular biology databases, and updates on 115 databases whose descriptions have been previously published in NAR or other journals. Following the classification that has been introduced last year in order to simplify navigation of the entire issue, these articles are divided into eight subject categories. This year's highlights include RNAcentral, an international community portal to various databases on noncoding RNA; ValidatorDB, a validation database for protein structures and their ligands; SASBDB, a primary repository for small-angle scattering data of various macromolecular complexes; MoonProt, a database of 'moonlighting' proteins, and two new databases of protein-protein and other macromolecular complexes, ComPPI and the Complex Portal. This issue also includes an unusually high number of cancer-related databases and other databases dedicated to genomic basics of disease and potential drugs and drug targets. The size of NAR online Molecular Biology Database Collection, http://www.oxfordjournals.org/nar/database/a/, remained approximately the same, following the addition of 74 new resources and removal of 77 obsolete web sites. The entire Database Issue is freely available online on the Nucleic Acids Research web site (http://nar.oxfordjournals.org/).
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Affiliation(s)
- Michael Y Galperin
- National Center for Biotechnology Information (NCBI), National Library of Medicine, National Institutes of Health, Bethesda, MD 20894, USA
| | - Daniel J Rigden
- Institute of Integrative Biology, University of Liverpool, Crown Street, Liverpool L69 7ZB, UK
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Sehnal D, Pravda L, Svobodová Vařeková R, Ionescu CM, Koča J. PatternQuery: web application for fast detection of biomacromolecular structural patterns in the entire Protein Data Bank. Nucleic Acids Res 2015; 43:W383-8. [PMID: 26013810 PMCID: PMC4489247 DOI: 10.1093/nar/gkv561] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2015] [Accepted: 05/17/2015] [Indexed: 01/10/2023] Open
Abstract
Well defined biomacromolecular patterns such as binding sites, catalytic sites, specific protein or nucleic acid sequences, etc. precisely modulate many important biological phenomena. We introduce PatternQuery, a web-based application designed for detection and fast extraction of such patterns. The application uses a unique query language with Python-like syntax to define the patterns that will be extracted from datasets provided by the user, or from the entire Protein Data Bank (PDB). Moreover, the database-wide search can be restricted using a variety of criteria, such as PDB ID, resolution, and organism of origin, to provide only relevant data. The extraction generally takes a few seconds for several hundreds of entries, up to approximately one hour for the whole PDB. The detected patterns are made available for download to enable further processing, as well as presented in a clear tabular and graphical form directly in the browser. The unique design of the language and the provided service could pave the way towards novel PDB-wide analyses, which were either difficult or unfeasible in the past. The application is available free of charge at http://ncbr.muni.cz/PatternQuery.
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Affiliation(s)
- David Sehnal
- CEITEC - Central European Institute of Technology, Masaryk University Brno, Kamenice 5, 625 00 Brno, Czech Republic National Centre for Biomolecular Research, Faculty of Science, Masaryk University, Kotlářská 2, 611 37 Brno, Czech Republic Faculty of Informatics, Masaryk University Brno, Botanická 68a, 602 00 Brno, Czech Republic
| | - Lukáš Pravda
- CEITEC - Central European Institute of Technology, Masaryk University Brno, Kamenice 5, 625 00 Brno, Czech Republic National Centre for Biomolecular Research, Faculty of Science, Masaryk University, Kotlářská 2, 611 37 Brno, Czech Republic
| | - Radka Svobodová Vařeková
- CEITEC - Central European Institute of Technology, Masaryk University Brno, Kamenice 5, 625 00 Brno, Czech Republic National Centre for Biomolecular Research, Faculty of Science, Masaryk University, Kotlářská 2, 611 37 Brno, Czech Republic
| | - Crina-Maria Ionescu
- CEITEC - Central European Institute of Technology, Masaryk University Brno, Kamenice 5, 625 00 Brno, Czech Republic
| | - Jaroslav Koča
- CEITEC - Central European Institute of Technology, Masaryk University Brno, Kamenice 5, 625 00 Brno, Czech Republic National Centre for Biomolecular Research, Faculty of Science, Masaryk University, Kotlářská 2, 611 37 Brno, Czech Republic
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18
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Hol WGJ. Three-dimensional structures in the design of therapeutics targeting parasitic protozoa: reflections on the past, present and future. Acta Crystallogr F Struct Biol Commun 2015; 71:485-99. [PMID: 25945701 PMCID: PMC4427157 DOI: 10.1107/s2053230x15004987] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2015] [Accepted: 03/11/2015] [Indexed: 11/10/2022] Open
Abstract
Parasitic protozoa cause a range of diseases which threaten billions of human beings. They are responsible for tremendous mortality and morbidity in the least-developed areas of the world. Presented here is an overview of the evolution over the last three to four decades of structure-guided design of inhibitors, leads and drug candidates aiming at targets from parasitic protozoa. Target selection is a crucial and multi-faceted aspect of structure-guided drug design. The major impact of advances in molecular biology, genome sequencing and high-throughput screening is touched upon. The most advanced crystallographic techniques, including XFEL, have already been applied to structure determinations of drug targets from parasitic protozoa. Even cryo-electron microscopy is contributing to our understanding of the mode of binding of inhibitors to parasite ribosomes. A number of projects have been selected to illustrate how structural information has assisted in arriving at promising compounds that are currently being evaluated by pharmacological, pharmacodynamic and safety tests to assess their suitability as pharmaceutical agents. Structure-guided approaches are also applied to incorporate properties into compounds such that they are less likely to become the victim of resistance mechanisms. A great increase in the number of novel antiparasitic compounds will be needed in the future. These should then be combined into various multi-compound therapeutics to circumvent the diverse resistance mechanisms that render single-compound, or even multi-compound, drugs ineffective. The future should also see (i) an increase in the number of projects with a tight integration of structural biology, medicinal chemistry, parasitology and pharmaceutical sciences; (ii) the education of more `medicinal structural biologists' who are familiar with the properties that compounds need to have for a high probability of success in the later steps of the drug-development process; and (iii) the expansion of drug-development capabilities in middle- and low-income countries.
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Affiliation(s)
- Wim G. J. Hol
- Department of Biochemistry and Biomolecular Structure Center, University of Washington, Seattle, WA 98195, USA
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