Searching across hundreds of databases
Are you sure you want to leave this community? Leaving the community will revoke any permissions you have been granted in this community.
Dockground: Benchmarks, Docoys, Templates, and other knowledge resources for DOCKING (RRID:SCR_007412)Copy Citation Copied
URL: http://dockground.bioinformatics.ku.edu/
Proper Citation: Dockground: Benchmarks, Docoys, Templates, and other knowledge resources for DOCKING (RRID:SCR_007412)
Description: 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.
Synonyms: Dockground, Dockground: Benchmarks Docoys Templates other knowledge resources for DOCKING, Dockground: Benchmarks Docoys Templates and other knowledge resources for DOCKING
Resource Type: data set, data or information resource
Defining Citation: PMID:17803215, PMID:16928732
Keywords: protein 3d structure, protein interaction, protein interface, protein model, structural genomics, co-crystallized, protein complex, protein recognition, protein modeling, structure prediction, protein-protein complex, benchmark
Expand Allis listed by |
|
is related to |
Research Collaboratory for Structural Bioinformatics Protein Data Bank (RCSB PDB) |
has parent organization |
We found {{ ctrl2.mentions.total_count }} mentions in open access literature.
We have not found any literature mentions for this resource.
We are searching literature mentions for this resource.
Most recent articles:
{{ mention._source.dc.creators[0].familyName }} {{ mention._source.dc.creators[0].initials }}, et al. ({{ mention._source.dc.publicationYear }}) {{ mention._source.dc.title }} {{ mention._source.dc.publishers[0].name }}, {{ mention._source.dc.publishers[0].volume }}({{ mention._source.dc.publishers[0].issue }}), {{ mention._source.dc.publishers[0].pagination }}. (PMID:{{ mention._id.replace('PMID:', '') }})
Check
for all resource mentions.
A list of researchers who have used the resource and an author search tool
A list of researchers who have used the resource and an author search tool. This is available for resources that have literature mentions.
No rating or validation information has been found for Dockground: Benchmarks, Docoys, Templates, and other knowledge resources for DOCKING.
No alerts have been found for Dockground: Benchmarks, Docoys, Templates, and other knowledge resources for DOCKING.
Source: SciCrunch Registry
Welcome to the Neuroscience Information Framework Project, designed to serve the biomedical research community. NIF maintains the largest searchable collection of neuroscience data, the largest catalog of biomedical resources, and the largest ontology for neuroscience on the web. We welcome all feedback and suggestions and are actively looking for resource providers to make their resources accessible through NIF. Learn about the tools available to help you share your data and discover a dynamic inventory of Web-based neuroscience resources.
