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DoGSiteScorer: Active Site Prediction and Analysis Server

Please note: the DoGSiteScorer: Active Site Prediction and Analysis Server will be deactivated by end april. You will find the functionality of the DoGSiteScorer: Active Site Prediction and Analysis Server as part of our new service named ProteinsPlus. ProteinsPlus is available for free under the following URL:

DogSiteScorer is an automated pocket detection and analysis tool which can be used for protein druggability assessment.
Predictions with DoGSiteScorer are based on calculated size, shape and chemical features of automatically predicted pockets, incorporated into a support vector machine for druggability estimation.

Usage: Input the 4-letter pdb code of the structure to be processed, or upload a pdb file. After clicking Calculate and analyze pockets, the evaluation of your input structure will take a few seconds. If your structure is valid, you will be directed to the next page, where you can customize several parameters.

required field
Protein: Input PDB code or upload
optional field
Ligand: None Extract from PDB or upload mol2 file

In case you are publishing results calculated with DoGSiteScorer, please cite the following paper:
A. Volkamer, D. Kuhn, T. Grombacher, F. Rippmann, M. Rarey. Combining global and local measures for structure-based druggability predictions. J. Chem. Inf. Model. 2012,52,360-372.

Based on the 3D coordinates of a protein, its potential active sites on the protein surface are calculated with DoGSite1. DoGSite is a grid-based function prediction method which uses a Difference of Gaussian filter to detect potential pockets on the protein surface and splits them into subpockets. Subsequently, global properties, describing the size, shape and chemical features of the predicted pockets are calculated. Examples for these descriptors are volume, depth, surface, ellipsoid main axes, site lining atoms and residues, as well as functional groups present in the pockets.
Per default, a SimpleScore is provided for each pocket, based on a linear combination on the three descriptors describing volume, hydrophobicity and enclosure. For the discrimination of the druggability, a subset of meaningful descriptors is used in a support vector maschine (libsvm).2 The druggability model was trained and tested on the DD dataset3 consisting of 1069 structures and yielded prediction accuracies of 88%.
For each queried input structure, a druggability score between zero and one is returned. The higher the score the more druggable the pocket is estimated to be.

Pocket prediction
J. Chem. Inf. Model cover picture: A. Volkamer et al. 2010, 50(11).

1 A. Volkamer et al. Analyzing the topology of active sites: on the prediction of pockets and subpockets. J. Chem. Inf. Model. 2010,50(11), 2041-52.
2 A. Volkamer et al. Combining global and local measures for strucure-based druggability predictions. J. Chem. Inf. Model. 2012,52,360-372
3 P. Schmidtke et al. Understanding and predicting druggability. A high-throughput method for detection of drug binding sites. J. Med. Chem. 2010, 53, 5858−5867.

DoGSiteScorer has been developed in cooperation with Merck KgA and BioSolveIT.
The project is part of the Biokatalyse2021 cluster and funded by the BMBF under grant 0315292A.

DogSiteScorer is free for academic research.
In all other cases, or if you have any questions concerning DoGSiteScorer server, please contact dogsite@zbh.uni-hamburg.de.

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