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Gutmanas A, Alhroub Y, Battle GM, Berrisford JM, Bochet E, Conroy MJ, Dana JM, Fernandez Montecelo MA, van Ginkel G, Gore SP, Haslam P, Hatherley R, Hendrickx PMS, Hirshberg M, Lagerstedt I, Mir S, Mukhopadhyay A, Oldfield TJ, Patwardhan A, Rinaldi L, Sahni G, Sanz-García E, Sen S, Slowley RA, Velankar S, Wainwright ME, Kleywegt GJ. PDBe: Protein Data Bank in Europe. Nucleic Acids Res 2013; 42:D285-91. [PMID: 24288376 PMCID: PMC3965016 DOI: 10.1093/nar/gkt1180] [Citation(s) in RCA: 105] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Abstract
The Protein Data Bank in Europe (pdbe.org) is a founding member of the Worldwide PDB consortium (wwPDB; wwpdb.org) and as such is actively engaged in the deposition, annotation, remediation and dissemination of macromolecular structure data through the single global archive for such data, the PDB. Similarly, PDBe is a member of the EMDataBank organisation (emdatabank.org), which manages the EMDB archive for electron microscopy data. PDBe also develops tools that help the biomedical science community to make effective use of the data in the PDB and EMDB for their research. Here we describe new or improved services, including updated SIFTS mappings to other bioinformatics resources, a new browser for the PDB archive based on Gene Ontology (GO) annotation, updates to the analysis of Nuclear Magnetic Resonance-derived structures, redesigned search and browse interfaces, and new or updated visualisation and validation tools for EMDB entries.
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Affiliation(s)
- Aleksandras Gutmanas
- Protein Data Bank in Europe, European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK
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Peterson TA, Doughty E, Kann MG. Towards precision medicine: advances in computational approaches for the analysis of human variants. J Mol Biol 2013; 425:4047-63. [PMID: 23962656 PMCID: PMC3807015 DOI: 10.1016/j.jmb.2013.08.008] [Citation(s) in RCA: 93] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2013] [Revised: 08/07/2013] [Accepted: 08/08/2013] [Indexed: 12/26/2022]
Abstract
Variations and similarities in our individual genomes are part of our history, our heritage, and our identity. Some human genomic variants are associated with common traits such as hair and eye color, while others are associated with susceptibility to disease or response to drug treatment. Identifying the human variations producing clinically relevant phenotypic changes is critical for providing accurate and personalized diagnosis, prognosis, and treatment for diseases. Furthermore, a better understanding of the molecular underpinning of disease can lead to development of new drug targets for precision medicine. Several resources have been designed for collecting and storing human genomic variations in highly structured, easily accessible databases. Unfortunately, a vast amount of information about these genetic variants and their functional and phenotypic associations is currently buried in the literature, only accessible by manual curation or sophisticated text text-mining technology to extract the relevant information. In addition, the low cost of sequencing technologies coupled with increasing computational power has enabled the development of numerous computational methodologies to predict the pathogenicity of human variants. This review provides a detailed comparison of current human variant resources, including HGMD, OMIM, ClinVar, and UniProt/Swiss-Prot, followed by an overview of the computational methods and techniques used to leverage the available data to predict novel deleterious variants. We expect these resources and tools to become the foundation for understanding the molecular details of genomic variants leading to disease, which in turn will enable the promise of precision medicine.
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Affiliation(s)
- Thomas A Peterson
- Department of Biological Sciences, University of Maryland, Baltimore County, 1000 Hilltop Circle, Baltimore, MD 21250, USA
| | - Emily Doughty
- Biomedical Informatics Program, Stanford University, Stanford, CA 94305, USA
| | - Maricel G Kann
- Department of Biological Sciences, University of Maryland, Baltimore County, 1000 Hilltop Circle, Baltimore, MD 21250, USA
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Richardson JS, Prisant MG, Richardson DC. Crystallographic model validation: from diagnosis to healing. Curr Opin Struct Biol 2013; 23:707-14. [PMID: 24064406 DOI: 10.1016/j.sbi.2013.06.004] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2013] [Accepted: 06/11/2013] [Indexed: 10/26/2022]
Abstract
Model validation has evolved from a passive final gatekeeping step to an ongoing diagnosis and healing process that enables significant improvement of accuracy. A recent phase of active development was spurred by the worldwide Protein Data Bank requiring data deposition and establishing Validation Task Force committees, by strong growth in high-quality reference data, by new speed and ease of computations, and by an upswing of interest in large molecular machines and structural ensembles. Progress includes automated correction methods, concise and user-friendly validation reports for referees and on the PDB websites, extension of error correction to RNA and error diagnosis to ligands, carbohydrates, and membrane proteins, and a good start on better methods for low resolution and for multiple conformations.
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Affiliation(s)
- Jane S Richardson
- Department of Biochemistry, Duke University, 132 Nanaline Duke Bldg, DUMC 3711, Durham, NC 27710, USA.
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Villoutreix BO, Lagorce D, Labbé CM, Sperandio O, Miteva MA. One hundred thousand mouse clicks down the road: selected online resources supporting drug discovery collected over a decade. Drug Discov Today 2013; 18:1081-9. [PMID: 23831439 DOI: 10.1016/j.drudis.2013.06.013] [Citation(s) in RCA: 66] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2013] [Revised: 06/18/2013] [Accepted: 06/26/2013] [Indexed: 12/17/2022]
Abstract
Online resources enabling and supporting drug discovery have blossomed during the past ten years. However, drug hunters commonly find themselves overwhelmed by the proliferation of these computer-based resources. Ten years ago, we, the authors of this review, felt that a comprehensive list of in silico resources relating to drug discovery was needed. Especially because the internet provides a wealth of inspiring tools that, if fully exploited, could greatly assist the process. We present here a compilation of online tools and databases collected over the past decade. The tools were essentially found through literature and internet searches and, currently, our list contains over 1500 URLs. We also briefly highlight some recently reported services and comment about ongoing and future efforts in the field.
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Affiliation(s)
- Bruno O Villoutreix
- Université Paris Diderot, Sorbonne Paris Cité, Inserm UMR-S 973, Molécules Thérapeutiques In Silico, 39 rue Helene Brion, 75013 Paris, France.
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Haas J, Roth S, Arnold K, Kiefer F, Schmidt T, Bordoli L, Schwede T. The Protein Model Portal--a comprehensive resource for protein structure and model information. DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION 2013; 2013:bat031. [PMID: 23624946 PMCID: PMC3889916 DOI: 10.1093/database/bat031] [Citation(s) in RCA: 199] [Impact Index Per Article: 18.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
The Protein Model Portal (PMP) has been developed to foster effective use of 3D molecular models in biomedical research by providing convenient and comprehensive access to structural information for proteins. Both experimental structures and theoretical models for a given protein can be searched simultaneously and analyzed for structural variability. By providing a comprehensive view on structural information, PMP offers the opportunity to apply consistent assessment and validation criteria to the complete set of structural models available for proteins. PMP is an open project so that new methods developed by the community can contribute to PMP, for example, new modeling servers for creating homology models and model quality estimation servers for model validation. The accuracy of participating modeling servers is continuously evaluated by the Continuous Automated Model EvaluatiOn (CAMEO) project. The PMP offers a unique interface to visualize structural coverage of a protein combining both theoretical models and experimental structures, allowing straightforward assessment of the model quality and hence their utility. The portal is updated regularly and actively developed to include latest methods in the field of computational structural biology. Database URL:http://www.proteinmodelportal.org
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Affiliation(s)
- Juergen Haas
- Biozentrum University of Basel, Klingelbergstrasse 50-70, 4056 Basel, Switzerland
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Borkotoky S, Meena CK, Khan MW, Murali A. Three dimensional electron microscopy and in silico tools for macromolecular structure determination. EXCLI JOURNAL 2013; 12:335-46. [PMID: 27092033 PMCID: PMC4827587] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/21/2013] [Accepted: 04/14/2013] [Indexed: 12/02/2022]
Abstract
Recently, structural biology witnessed a major tool - electron microscopy - in solving the structures of macromolecules in addition to the conventional techniques, X-ray crystallography and nuclear magnetic resonance (NMR). Three dimensional transmission electron microscopy (3DTEM) is one of the most sophisticated techniques for structure determination of molecular machines. Known to give the 3-dimensional structures in its native form with literally no upper limit on size of the macromolecule, this tool does not need the crystallization of the protein. Combining the 3DTEM data with in silico tools, one can have better refined structure of a desired complex. In this review we are discussing about the recent advancements in three dimensional electron microscopy and tools associated with it.
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Affiliation(s)
- Subhomoi Borkotoky
- Centre for Bioinformatics, School of Life Sciences, Pondicherry University, Puducherry-605014, India
| | - Chetan Kumar Meena
- Centre for Bioinformatics, School of Life Sciences, Pondicherry University, Puducherry-605014, India
| | - Mohammad Wahab Khan
- Centre for Bioinformatics, School of Life Sciences, Pondicherry University, Puducherry-605014, India
| | - Ayaluru Murali
- Centre for Bioinformatics, School of Life Sciences, Pondicherry University, Puducherry-605014, India,*To whom correspondence should be addressed: Ayaluru Murali, Centre for Bioinformatics, School of Life Sciences, Pondicherry University, Puducherry-605014, India, E-mail:
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De Bari H, Berry EA. Structure of Vibrio cholerae ribosome hibernation promoting factor. Acta Crystallogr Sect F Struct Biol Cryst Commun 2013; 69:228-36. [PMID: 23519794 PMCID: PMC3606564 DOI: 10.1107/s1744309113000961] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2012] [Accepted: 01/10/2013] [Indexed: 11/10/2022]
Abstract
The X-ray crystal structure of ribosome hibernation promoting factor (HPF) from Vibrio cholerae is presented at 2.0 Å resolution. The crystal was phased by two-wavelength MAD using cocrystallized cobalt. The asymmetric unit contained two molecules of HPF linked by four Co atoms. The metal-binding sites observed in the crystal are probably not related to biological function. The structure of HPF has a typical β-α-β-β-β-α fold consistent with previous structures of YfiA and HPF from Escherichia coli. Comparison of the new structure with that of HPF from E. coli bound to the Thermus thermophilus ribosome [Polikanov et al. (2012), Science, 336, 915-918] shows that no significant structural changes are induced in HPF by binding.
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Affiliation(s)
- Heather De Bari
- Biochemistry and Molecular Biology, SUNY Upstate Medical University, 750 E. Adams Avenue, Syracuse, NY 13210, USA
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