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SIENA is a software pipeline enabling the fully automated construction of protein structure ensembles from the PDB. Starting with a single query structure, all binding sites with high sequence similarity are extracted from the PDB, aligned, and superimposed. SIENA also handles complicated cases, such as comparing binding sites at protein domain interfaces or within multimeric proteins.

DoGSiteScorer is a grid-based automated pocket detection and analysis tool. It applies a Difference of Gaussian filter to detect potential binding pockets and splits them into sub-pockets. The method solely uses the 3D structure of the protein. Global properties, describing the size, shape, and chemical features of the predicted (sub-)pockets, are calculated. Per default, a simple druggability score based on a linear combination of the three descriptors describing volume, hydrophobicity, and enclosure is provided for each (sub-)pocket. Furthermore, a subset of meaningful descriptors is incorporated in a support vector machine (libsvm) to predict the (sub-)pocket druggability score (values are between zero and one). The higher the score, the more druggable the pocket is estimated to be.

DoGSite3 was developed for predicting robust and reliable small molecule binding sites and computing their geometrical and chemical descriptors. It is based on the grid-based DoGSite algorithm for predicting pockets and their sub-pockets. The new tool is largely rotation- and translation-invariant due to a normalization procedure before binding site prediction. Known ligands in the structure can be used to bias the grid by sufficiently buried ligand fragments. The output encompasses novel chemical binding site descriptors considering solvent accessibility. Compared to its predecessor, it shows increased robustness through comprehensive parameter optimization. DoGSite3 runs finish within seconds.