| URL: | http://www.bioinformatics.leeds.ac.uk/sb |
| Full name: | |
| Description: | SitesBase is an easily accessible database which is simple to use and holds information about structural similarities between known ligand binding sites found in the Protein Data Bank. |
| Year founded: | 2006 |
| Last update: | 2007-06-01 |
| Version: | v2.0 |
| Accessibility: |
Unaccessible
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| Country/Region: | United Kingdom |
| Data type: | |
| Data object: |
NA
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| Database category: | |
| Major species: |
NA
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| Keywords: |
| University/Institution: | University of Leeds |
| Address: | LS2 9JT, UK |
| City: | Leeds |
| Province/State: | |
| Country/Region: | United Kingdom |
| Contact name (PI/Team): | R.M. Jackson |
| Contact email (PI/Helpdesk): | r.m.jackson@leeds.ac.uk |
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SitesBase: a database for structure-based protein-ligand binding site comparisons. [PMID: 16381853]
There are many components which govern the function of a protein within a cell. Here, we focus on the molecular recognition of small molecules and the prediction of common recognition by similarity between protein-ligand binding sites. SitesBase is an easily accessible database which is simple to use and holds information about structural similarities between known ligand binding sites found in the Protein Data Bank. These similarities are presented to the wider community enabling full analysis of molecular recognition and potentially protein structure-function relationships. SitesBase is accessible at http://www.bioinformatics.leeds.ac.uk/sb. |
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A searchable database for comparing protein-ligand binding sites for the analysis of structure-function relationships. [PMID: 16563004]
The rapid expansion of structural information for protein-ligand binding sites is potentially an important source of information in structure-based drug design and in understanding ligand cross reactivity and toxicity. We have developed a large database of ligand binding sites extracted automatically from the Protein Data Bank. This has been combined with a method for calculating binding site similarity based on geometric hashing to create a relational database for the retrieval of site similarity and binding site superposition. It contains an all-against-all comparison of binding sites and holds known protein-ligand binding sites, which are made accessible to data mining. Here we demonstrate its utility in two structure-based applications: in determining site similarity and in aiding the derivation of a receptor-based pharmacophore model. The database is available from http://www.bioinformatics.leeds.ac.uk/sb/. |