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on page 1 showing 20 out of 54 results from 1 sources

    AffinDB

Cite this (AffinDB, RRID:SCR_001690)

URL: http://pc1664.pharmazie.uni-marburg.de/affinity/

Resource Type: Resource, data or information resource, database

Database of affinity data for protein-ligand complexes of the Protein Data Bank (PDB) providing direct and free access to the experimental affinity of a given complex structure. Affinity data are exclusively obtained from the scientific literature. As of Thursday, May 01st, 2014, AffinDB contains 748 affinity values covering 474 different PDB complexes. More than one affinity value may be associated with a single PDB complex, which is most frequently due to multiple references reporting affinity data for the same complex. AffinDB provides access to data in three different forms:
# Summary information for PDB entry
# Affinity information window
# Tabular reports

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    AmiGO

Cite this (AmiGO, RRID:SCR_002143)

URL: http://amigo.geneontology.org/

Resource Type: Resource, data analysis service, database, analysis service resource, software application, production service resource, service resource, software resource, data or information resource

Official Web-based tools for searching and browsing the Gene Ontology database, which consists of a controlled vocabulary of terms covering biological concepts, and a large number of genes or gene products whose attributes have been annotated using GO terms. It can be accessed online at the main installation or deployed locally. The Gene Ontology project is a major bioinformatics initiative with the aim of standardizing the representation of gene and gene product attributes across species and databases. AmiGO can be used to:
* search for a gene or gene product, or a list of gene or gene products, and view the GO term associations
* perform a sequence identity BLAST search and view the GO term associations for the genes or proteins returned
* search for GO terms and view the genes or gene products they are annotated to
* browse the GO ontology and view terms
* the slimmer tool can be used to map the granular annotations of the query set of genes to one or more high-level
* term enrichment tool is used to discover what a set of genes may have in common by examining annotations and finding significant shared GO terms.
* GOOSE is for advanced users who want to run custom SQL queries against the GO database.

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Cite this (Binding MOAD, RRID:SCR_002294)

URL: http://www.bindingmoad.org/

Resource Type: Resource, data or information resource, database

Database of protein-ligand crystal structures that is a subset of the Protein Data Bank (PDB), containing every high-quality example of ligand-protein binding. The resolved protein crystal structures with clearly identified biologically relevant ligands are annotated with experimentally determined binding data extracted from literature. A viewer is provided to examine the protein-ligand structures. Ligands have additional chemical data, allowing for cheminformatics mining. The binding-affinity data ranges 13 orders of magnitude. The issue of redundancy in the data has also been addressed. To create a nonredundant dataset, one protein from each of the 1780 protein families was chosen as a representative. Representatives were chosen by tightest binding, best resolution, etc. For the 1780 best complexes that comprise the nonredundant version of Binding MOAD, 475 (27%) have binding data. This collection of protein-ligand complexes will be useful in elucidating the biophysical patterns of molecular recognition and enzymatic regulation. The complexes with binding-affinity data will help in the development of improved scoring functions and structure-based drug discovery techniques.

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Cite this (Biological Magnetic Resonance Data Bank, RRID:SCR_002296)

URL: http://www.bmrb.wisc.edu/

Resource Type: Resource, service resource, data or information resource, data repository, storage service resource, database

Public depository that collects, annotates, archives, and disseminates important spectral and quantitative data derived from nuclear magnetic resonance (NMR) spectroscopic investigations of biological macromolecules and metabolites. BMRB provides reference information and maintains a collection of NMR pulse sequences and computer software for biomolecular NMR. The BMRB archive consists of four main data depositories: quantitative NMR spectral parameters for proteins, peptides, nucleic acids, carbohydrates and ligands or cofactors and derived data; databases for NMR restraints processed from original author depositions available from the Protein Data Bank; time-domain spectral data from NMR experiments used to assign spectral resonances and determine the structures of biological macromolecules; and a database of one- and two-dimensional (1)H and (13)C one- and two-dimensional NMR spectra for over 250 metabolites. BMRB has tools for querying the archive and retrieving information and an ftp site where data in the archive can be downloaded in bulk. BMRB accepts quantitative NMR data for a peptide, protein, nucleic acid, or polysaccharide and their ligands and cofactors assigned on an atom-specific basis, and data derived from the analysis of such data.

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    canSAR

Cite this (canSAR, RRID:SCR_006794)

URL: https://cansar.icr.ac.uk/

Resource Type: Resource, data analysis service, production service resource, analysis service resource, database, service resource, data or information resource

canSAR is an integrated database that brings together biological, chemical, pharmacological (and eventually clinical) data. Its goal is to integrate this data and make it accessible to cancer research scientists from multiple disciplines, in order to help with hypothesis generation in cancer research and support translational research. This cancer research and drug discovery resource was developed to utilize the growing publicly available biological annotation, chemical screening, RNA interference screening, expression, amplification and 3D structural data. Scientists can, in a single place, rapidly identify biological annotation of a target, its structural characterization, expression levels and protein interaction data, as well as suitable cell lines for experiments, potential tool compounds and similarity to known drug targets. canSAR has, from the outset, been completely use-case driven which has dramatically influenced the design of the back-end and the functionality provided through the interfaces. The Web interface provides flexible, multipoint entry into canSAR. This allows easy access to the multidisciplinary data within, including target and compound synopses, bioactivity views and expert tools for chemogenomic, expression and protein interaction network data.

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Cite this (CAPS Database, RRID:SCR_006862)

URL: http://www.bioinsilico.org/cgi-bin/CAPSDB/staticHTML/home

Resource Type: Resource, data or information resource, database

It is a structural classification of helix-cappings or caps compiled from protein structures. Caps extracted from protein structures have been structurally classified based on geometry and conformation and organized in a tree-like hierarchical classification where the different levels correspond to different properties of the caps. CASP-DB is fully browsable and searchable and is regularly updated. The regions of the polypeptide chain immediately preceding or following an ????????-helix are known as Nt- and Ct cappings, respectively. Cappings play a central role stabilizing ????????-helices due to lack of intrahelical hydrogen bonds in the first and last turn. Sequence patterns of amino acid type preferences have been derived for cappings but the structural motifs associated to them are still unclassified. CAPS-DB is a database of clusters of structural patterns of different capping types. The clustering algorithm is based in the geometry and the (????????-?????)-space conformation of these regions. CAPS-DB is a relational database that allows the user to search, browse, inspect and retrieve structural data associated to cappings. The contents of CAPS-DB might be of interest to a wide range of scientist covering different areas such as protein design and engineering, structural biology and bioinformatics. CapsDB v4.0 * PDB structures: 4591 * Number of clusters: 859 * Number of caps: 31452

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    CARP

Cite this (CARP, RRID:SCR_009021)

URL: http://www.glycosciences.de/tools/carp/

Resource Type: Resource, analysis service resource, data analysis service, service resource, production service resource

Service that generates Ramachandran-like plots of carbohydrate linkage torsions in pdb-files. The Ramachandran Plot, where backbone torsion angles are plotted against each other, is a frequently used tool to evaluate the quality of a protein 3D structure. For carbohydrate structures, linkage torsions can be evaluated in a similar way. Preferred Phi/Psi values of the torsion angles of glycosidic bonds depend strongly on the types of monosaccharides involved in the linkage, the kind of linkage (1-3, 1-4, etc) as well as the degree of branching of the structure. CARP analyses carbohydrate data given in PDB files using the pdb2linucs algorithm. For each different linkage type a separate plot is generated. The user can choose between two sources for plot background information for comparison: data obtained from PDB provided by GlyTorsion or from GlycoMapsDB. GlycoMapsDB provides calculated conformational maps, which show energetically preferred regions for a specific linkage, while PDB data are based on experimentally solved structures. For seldom occuring linkages, however, PDB data are often rare, so maybe not sufficient background information for comparison will be available from this source.

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Cite this (ccPDB - Compilation and Creation of datasets from PDB, RRID:SCR_005870)

URL: http://crdd.osdd.net/raghava/ccpdb/

Resource Type: Resource, web service, software resource, data or information resource, data access protocol, database

ccPDB (Compilation and Creation of datasets from PDB) is designed to provide service to scientific community working in the field of function or structure annoation of proteins. This database of datasets is based on Protein Data Bank (PDB), where all datasets were derived from PDB. ccPDB have four modules; i) compilation of datasets, ii) creation of datasets, iii) web services and iv) Important links. * Compilation of Datasets: Datasets at ccPDB can be classified in two categories, i) datasets collected from literature and ii) datasets compiled from PDB. We are in process of collecting PDB datasetsfrom literature and maintaining at ccPDB. We are also requesting community to suggest datasets. In addition, we generate datasets from PDB, these datasets were generated using commonly used standard protocols like non-redundant chains, structures solved at high resolution. * Creation of datasets: This module developed for creating customized datasets where user can create a dataset using his/her conditions from PDB. This module will be useful for those users who wish to create a new dataset as per ones requirement. This module have six steps, which are described in help page. * Web Services: We integrated following web services in ccPDB; i) Analyze of PDB ID service allows user to submit their PDB on around 40 servers from single point, ii) BLAST search allows user to perform BLAST search of their protein against PDB, iii) Structural information service is designed for annotating a protein structure from PDB ID, iv) Search in PDB facilitate user in searching structures in PDB, v)Generate patterns service facility to generate different types of patterns required for machine learning techniques and vi) Download useful information allows user to download various types of information for a given set of proteins (PDB IDs). * Important Links: One of major objectives of this web site is to provide links to web servers related to functional annotation of proteins. In first phase we have collected and compiled these links in different categories. In future attempt will be made to collect as many links as possible.

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Cite this (Combinatorial Extension (CE), RRID:SCR_007585)

URL: http://source.rcsb.org/jfatcatserver/ceHome.jsp

Resource Type: Resource, data or information resource, database

CE is a databases of alignments for all polypeptide chains. A representative set of proteins is available and kept current with the PDB, a method for calculating pairwise structure alignments. CE aligns two polypeptide chains using characteristics of their local geometry as defined by vectors between C alpha positions. Matches are termed aligned fragment pairs (AFPs). Heuristics are used in defining a set of optimal paths joining AFPs with gaps as needed. The path with the best RMSD is subject to dynamic programming to achieve an optimal alignment. For specific families of proteins additional characteristics are used to weight the alignment. Complete details are described in the paper (PDF format). Databases of alignments for all polypeptide chains and a representative set of proteins is available and kept current with the PDB

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Cite this (Community Structure-Activity Resource, RRID:SCR_002206)

URL: http://www.csardock.org

Resource Type: Resource, service resource, data set, data repository, storage service resource, data or information resource

Experimental datasets of crystal structures and binding affinities for diverse protein-ligand complexes. Some datasets are generated in house while others are collected from the literature or deposited by academic labs, national centers, and the pharmaceutical industry. For the community to improve their approaches, they need exceptional datasets to train scoring functions and develop new docking algorithms. They aim to provide the highest quality data for a diverse collection of proteins and small molecule ligands. They need input from the community in developing target priorities. Ideal targets will have many high-quality crystal structures (apo and 10-20 bound to diverse ligands) and affinity data for 25 compounds that range in size, scaffold, and logP. It is best if the ligand set has several congeneric series that span a broad range of affinity, with low nanomolar to mid-micromolar being most desirable. They prefer Kd data over Ki data over IC50 data (no % activity data). They will determine solubility, pKa, logP/logD data for the ligands whenever possible. They have augmented some donated IC50 data by determining Kon/Koff and ITC data.

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Cite this (ConsensusPathDB, RRID:SCR_002231)

URL: http://cpdb.molgen.mpg.de

Resource Type: Resource, data or information resource, database

An integrative interaction database that integrates different types of functional interactions from heterogeneous interaction data resources. Physical protein interactions, metabolic and signaling reactions and gene regulatory interactions are integrated in a seamless functional association network that simultaneously describes multiple functional aspects of genes, proteins, complexes, metabolites, etc. With human, yeast and mouse complex functional interactions, it currently constitutes the most comprehensive publicly available interaction repository for these species. Different ways of utilizing these integrated interaction data, in particular with tools for visualization, analysis and interpretation of high-throughput expression data in the light of functional interactions and biological pathways is offered.

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Cite this (ConSurf Server Database, RRID:SCR_002320)

URL: http://consurfdb.tau.ac.il/

Resource Type: Resource, data or information resource, database

Database of pre-calculated ConSurf evolutionary conservation profiles for proteins of known structure in the PDB. Amino acid sequences similar to each sequence in the PDB were collected and multiply aligned using CSI-BLAST and MAFFT, respectively. The evolutionary conservation of each amino acid position in the alignment was calculated using the Rate4Site algorithm, implemented in the ConSurf web-server. The algorithm takes explicitly into account the phylogenetic relations between the aligned proteins and the stochastic nature of the evolutionary process. Rate4Site assigns a conservation level for each residue using an empirical Bayesian inference. Visual inspection of the conservation patterns on the 3-dimensional structure often enables the identification of key residues that comprise the functionally-important regions of the protein. ConSurf-DB differs from ConSurf in that ConSurf-DB provides pre-calculated conservation profiles, while ConSurf enables considerable flexibility in setting the parameters of the calculation, and accepts optional uploads of atomic coordinates, multiple sequence alignments, and phylogenetic trees for use in the calculation.

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Cite this (DDBJ - DNA Data Bank of Japan, RRID:SCR_002359)

URL: http://www.ddbj.nig.ac.jp

Resource Type: Resource, service resource, data or information resource, data repository, storage service resource, database

The sole nucleotide sequence data bank in Asia, which is officially certified to collect nucleotide sequences from researchers and to issue the internationally recognized accession number to data submitters. It is one of 3 summit databanks that construct the DDBJ/EMBL/GenBank International Nucleotide Sequence Database, which was established through cooperative work with EBI in Europe and NCBI in USA. Since the collected data is exchanged with EMBL-Bank/EBI; European Bioinformatics Institute and GenBank/NCBI; National Center for Biotechnology Information on a daily basis, the three data banks share virtually the same data at any given time. The virtually unified database is called INSD; International Nucleotide Sequence Database. DDBJ collects sequence data mainly from Japanese researchers, but accepts data and issues accession numbers to researchers in other countries.

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Cite this (Dockground: Benchmarks, Docoys, Templates, and other knowledge resources for DOCKING, RRID:SCR_007412)

URL: http://dockground.bioinformatics.ku.edu/

Resource Type: Resource, data set, data or information resource

Data sets, tools and computational techniques for modeling of protein interactions, including docking benchmarks, docking decoys and docking templates. Adequate computational techniques for modeling of protein interactions are important because of the growing number of known protein 3D structures, particularly in the context of structural genomics. The first release of the DOCKGROUND resource (Douguet et al., Bioinformatics 2006; 22:2612-2618) implemented a comprehensive database of cocrystallized (bound) protein-protein complexes in a relational database of annotated structures. Additional releases added features to the set of bound structures, such as regularly updated downloadable datasets: automatically generated nonredundant set, built according to most common criteria, and a manually curated set that includes only biological nonobligate complexes along with a number of additional useful characteristics. Also included are unbound (experimental and simulated) protein-protein complexes. Complexes from the bound dataset are used to identify crystallized unbound analogs. If such analogs do not exist, the unbound structures are simulated by rotamer library optimization. Thus, the database contains comprehensive sets of complexes suitable for large scale benchmarking of docking algorithms. Advanced methodologies for simulating unbound conformations are being explored for the next release. The Dockground project is developed by the Vakser lab at the Center for Bioinformatics at the University of Kansas. Parts of Dockground were co-developed by Dominique Douguet from the Center of Structural Biochemistry (INSERM U554 - CNRS UMR5048), Montpellier, France.

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Cite this (DOMINE: Database of Protein Interactions, RRID:SCR_002399)

URL: http://domine.utdallas.edu

Resource Type: Resource, data or information resource, database

Database of known and predicted protein domain (domain-domain) interactions containing interactions inferred from PDB entries, and those that are predicted by 8 different computational approaches using Pfam domain definitions. DOMINE contains a total of 26,219 domain-domain interactions (among 5,410 domains) out of which 6,634 are inferred from PDB entries, and 21,620 are predicted by at least one computational approach. Of the 21,620 computational predictions, 2,989 interactions are high-confidence predictions (HCPs), 2,537 interactions are medium-confidence predictions (MCPs), and the remaining 16,094 are low-confidence predictions (LCPs). (May 2014)

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Cite this (DOMMINO - Database Of MacroMolecular INteractiOns, RRID:SCR_005958)

URL: http://dommino.org

Resource Type: Resource, data or information resource, database

DOMMINO is a comprehensive structural database on macromolecular interactions. As of June, 2011, it contains more than 407,000 binary interactions. The distinctive features of DOMMINO are: # Automated updates: DOMMINO is fully automated and is designed to update itself on a weekly basis, one day after a PDB weekly update. Thus, the community will be able to study macromolecular interactions almost immediately after they are released by PDB. # Coverage of non-domain mediated interactions: In addition to domain-domain and domain-peptide interactions the database characterizes the interaction between domains and unstructured protein regions that are not parts of a domain, such as inter-domain linkers and N- and C-termini. The interactions that involve the latter unstructured parts of proteins have been included to the database for the first time providing additional ~186,000 interactions (~45% of the total number of interactions, as of June, 2011). # Coverage of new structural domains: DOMMINO employs one of the most accurate structural classifications of proteins, SCOP. In addition to the existing SCOP-annotated domains, we employ a state-of-the-art machine learning approach to classify newer protein structures into existing SCOP families. With the progress of structural genomics, we do not expect a significant growth of the number of structurally novel folds or protein families and therefore our method allows covering almost all new protein structures. In total, using this predictive approach has allowed us to add more than 261,000 new interactions, almost twice as many as existing SCOP-annotated interactions. # The web-interface is designed to give the user a possibility of a flexible search as well as the capability to study macromolecular interactions in a PDB structure at the interaction network level and at the individual interface level. The web interface of the DOMMINO database includes a comprehensive list of help topics linked to the specific actions. In addition, we have designed a step-by-step tutorial that covers all aspects of working with the data from DOMMINO using the web interface.

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Cite this (Electron Microscopy Data Bank, RRID:SCR_006506)

URL: http://www.ebi.ac.uk/pdbe/emdb/

Resource Type: Resource, data analysis service, production service resource, analysis service resource, database, service resource, storage service resource, data repository, data or information resource

A public repository for electron microscopy density maps of macromolecular complexes and subcellular structures. It covers a variety of techniques, including single-particle analysis, electron tomography, and electron (2D) crystallography. It is operated jointly by the PDBe, and the Research Collaboratory for Structural Bioinformatics (RCSB PDB) as a part of EMDataBank which is funded by a joint NIH grant to the EBI, the RCSB and the National Center for Macromolecular Imaging (NCMI). Ftp archive available.

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Cite this (EMDataBank.org: Unified Data Resource for Cryo Electron Microscopy, RRID:SCR_003207)

URL: http://www.emdatabank.org

Resource Type: Resource, service resource, data or information resource, data repository, storage service resource, database

A unified global portal for deposition and retrieval of cryo electron microscopy (3DEM) density maps, atomic models, and associated metadata, as well as a resource for news, events, software tools, data standards, validation methods for the 3DEM community. It is a joint effort among investigators of the Protein Databank in Europe (PDBe) at the European Bioinformatics Institute, the Research Collaboratory for Structural Bioinformatics (RCSB) at Rutgers, and the National Center for Macromolecular Imaging (NCMI) at Baylor College of Medicine. A major goal of the EMDataBank project in their current funding period is to work with the 3DEM community to (1) establish data-validation methods that can be used in the process of structure determination, (2) define the key indicators of a well-determined structure that should accompany every deposition, and (3) implement appropriate validation procedures for maps and map-derived models into a 3DEM validation pipeline. The EM Validation Task Force (EM VTF) has made initial recommendations to guide development of 3DEM validation criteria. Their aim is to facilitate the many processes needed to assess, establish, and disseminate validation methods and standards. The Web Service allows other applications to obtain data regarding maps/structures in the EMDB. The data is returned in XML format.

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Cite this (Enzyme Structures Database, RRID:SCR_007125)

URL: http://www.ebi.ac.uk/thornton-srv/databases/enzymes/

Resource Type: Resource, data or information resource, image collection, database

Database of known enzyme structures that have been deposited in the Protein Data Bank (PDB). The enzyme structures are classified by their E.C. number of the ENZYME Data Bank. Browse the classification hierarchy or enter an EC number or search-string. There are currently 45,638 PDB-enzyme entries in the PDB (as at 23 February, 2013) involving 38,109 separate PDB files - some files having more than one E.C. number associated with them.

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    FlyMine

Cite this (FlyMine, RRID:SCR_002694)

URL: http://www.flymine.org/

Resource Type: Resource, data analysis service, data access protocol, production service resource, analysis service resource, database, web service, service resource, software resource, data or information resource

An integrated database of genomic, expression and protein data for Drosophila, Anopheles, C. elegans and other organisms. You can run flexible queries, export results and analyze lists of data. FlyMine presents data in categories, with each providing information on a particular type of data (for example Gene Expression or Protein Interactions). Template queries, as well as the QueryBuilder itself, allow you to perform searches that span data from more than one category. Advanced users can use a flexible query interface to construct their own data mining queries across the multiple integrated data sources, to modify existing template queries or to create your own template queries. Access our FlyMine data via our Application Programming Interface (API). We provide client libraries in the following languages: Perl, Python, Ruby and & Java API

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