The worldwide Protein Data Bank (wwPDB): ensuring a single, uniform archive of PDB data. Nucleic Acids Res 35:D301-D303
ABSTRACT The worldwide Protein Data Bank (wwPDB) is the international collaboration that manages the deposition, processing and distribution of the PDB archive. The online PDB archive is a repository for the coordinates and related information for more than 38 000 structures, including proteins, nucleic acids and large macromolecular complexes that have been determined using X-ray crystallography, NMR and electron microscopy techniques. The founding members of the wwPDB are RCSB PDB (USA), MSD-EBI (Europe) and PDBj (Japan) [H.M. Berman, K. Henrick and H. Nakamura (2003) Nature Struct. Biol., 10, 980]. The BMRB group (USA) joined the wwPDB in 2006. The mission of the wwPDB is to maintain a single archive of macromolecular structural data that are freely and publicly available to the global community. Additionally, the wwPDB provides a variety of services to a broad community of users. The wwPDB website at http://www.wwpdb.org/ provides information about services provided by the individual member organizations and about projects undertaken by the wwPDB.
Full-textDOI: · Available from: John L. Markley, Sep 25, 2015
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- "These pathways typically involve transitions between different software packages and associated file formats. [A typical pathway may include: create a *.pdb file (the original file format of the worldwide Protein Databank (Berman et al., 2007) from a *.cif file with ORTEP3, http://www.iucr.org/resources/other-directories/ software/ortep-3-for-windows or other software packages, import the *.pdb file into Meshlab, http://meshlab.sourceforge. "
ABSTRACT: Ongoing software developments for creating three-dimensional (3D) printed crystallographic models seamlessly from Crystallographic Information Framework (CIF) data (*.cif files) are reported. Color versus monochrome printing is briefly discussed. Recommendations are made on the basis of our preliminary printing efforts. A brief outlook on new materials for 3D printing is given.Powder Diffraction 12/2014; 29(S2):S42-S47. DOI:10.1017/S0885715614001092 · 0.64 Impact Factor
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- "The growing gap between known sequences of proteins [[1.6 9 10 8 in GenBank (Benson et al. 2008)] and 3D structures [*1 9 10 5 in PDB (Protein Data Bank; Berman et al. 2007)] is motivating the development of improved approaches to experimental structure determination. Of the two major approaches to protein structure determination, NMR spectroscopy lags behind X-ray crystallography in terms of automated approaches. "
ABSTRACT: Peak-picking Of Noe Data Enabled by Restriction Of Shift Assignments-Client Server (PONDEROSA-C/S) builds on the original PONDEROSA software (Lee et al. in Bioinformatics 27:1727–1728. doi:10.1093/bioinformatics/btr200, 2011) and includes improved features for structure calculation and refinement. PONDEROSA-C/S consists of three programs: Ponderosa Server, Ponderosa Client, and Ponderosa Analyzer. PONDEROSA-C/S takes as input the protein sequence, a list of assigned chemical shifts, and nuclear Overhauser data sets (13C- and/or 15N-NOESY). The output is a set of assigned NOEs and 3D structural models for the protein. Ponderosa Analyzer supports the visualization, validation, and refinement of the results from Ponderosa Server. These tools enable semi-automated NMR-based structure determination of proteins in a rapid and robust fashion. We present examples showing the use of PONDEROSA-C/S in solving structures of four proteins: two that enable comparison with the original PONDEROSA package, and two from the Critical Assessment of automated Structure Determination by NMR (Rosato et al. in Nat Methods 6:625–626. doi:10.1038/nmeth0909-625, 2009) competition. The software package can be downloaded freely in binary format from http://pine.nmrfam.wisc.edu/download_packages.html. Registered users of the National Magnetic Resonance Facility at Madison can submit jobs to the PONDEROSA-C/S server at http://ponderosa.nmrfam.wisc.edu, where instructions, tutorials, and instructions can be found. Structures are normally returned within 1–2 days. Electronic supplementary material The online version of this article (doi:10.1007/s10858-014-9855-x) contains supplementary material, which is available to authorized users.Journal of Biomolecular NMR 09/2014; 60(2-3). DOI:10.1007/s10858-014-9855-x · 3.14 Impact Factor
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- "Gene annotations are collected through ENSEMBL (Flicek et al., 2012), EMBL (Cochrane et al., 2009), GenBank (Benson et al., 2011), EntrezGene (Maglott et al., 2011), UniGene (Schuler, 1997), UniProt (Magrane and Consortium, 2011), IPI (Kersey et al., 2004), NCBI Protein (Sayers et al., 2012), RefSeq (Sayers et al., 2012), HGNC (Seal et al., 2011), GeneCards (Safran et al., 2010) and UCSC (Fujita et al., 2011) databases. Sequence domain architectures, structures and annotations are collected from PDB (Berman et al., 2007), HSSP (Schneider and Sander, 1996) and PSSH (Schafferhans et al., 2003). Functional enrichment is performed by querying the Gene Ontology repository (Ashburner et al., 2000) to collect all of the related biological processes, molecular functions and cellular components. "
ABSTRACT: The iterative process of finding relevant information in biomedical literature and performing bioinformatics analyses might result in an endless loop for an inexperienced user, considering the exponential growth of scientific corpora and the plethora of tools designed to mine PubMed(®) and related biological databases. Herein, we describe BioTextQuest(+), a web-based interactive knowledge exploration platform with significant advances to its predecessor (BioTextQuest), aiming to bridge processes such as bioentity recognition, functional annotation, document clustering and data integration towards literature mining and concept discovery. BioTextQuest(+) enables PubMed and OMIM querying, retrieval of abstracts related to a targeted request and optimal detection of genes, proteins, molecular functions, pathways and biological processes within the retrieved documents. The front-end interface facilitates the browsing of document clustering per subject, the analysis of term co-occurrence, the generation of tag clouds containing highly represented terms per cluster and at-a-glance popup windows with information about relevant genes and proteins. Moreover, to support experimental research, BioTextQuest(+) addresses integration of its primary functionality with biological repositories and software tools able to deliver further bioinformatics services. The Google-like interface extends beyond simple use by offering a range of advanced parameterization for expert users. We demonstrate the functionality of BioTextQuest(+) through several exemplary research scenarios including author disambiguation, functional term enrichment, knowledge acquisition and concept discovery linking major human diseases, such as obesity and ageing. Availability: The service is accessible at http://bioinformatics.med.uoc.gr/biotextquest. Contact: email@example.com or firstname.lastname@example.org Supplementary information: Supplementary data are available at Bioinformatics online.Bioinformatics 08/2014; 30(22). DOI:10.1093/bioinformatics/btu524 · 4.98 Impact Factor