PocketPicker: analysis of ligand binding-sites with shape descriptors

By Martin Weisel, Ewgenij Proschak, Gisbert Schneider. Chemistry Central Journal, 2007

330 citations recorded by the source corpus.

BackgroundIdentification and evaluation of surface binding-pockets and occluded cavities are initial steps in protein structure-based drug design. Characterizing the active site's shape as well as the distribution of surrounding residues plays an important role for a variety of applications such as automated ligand docking or in situ modeling. Comparing the shape similarity of binding site geometries of related proteins provides further insights into the mechanisms of ligand binding.ResultsWe present PocketPicker, an automated grid-based technique for the prediction of protein binding pockets that specifies the shape of a potential binding-site with regard to its buriedness. The method was applied to a representative set of protein-ligand complexes and their corresponding apo-protein structures to evaluate the quality of binding-site predictions. The performance of the pocket detection routine was compared to results achieved with the existing methods CAST, LIGSITE, LIGSITEcs, PASS and SURFNET. Success rates PocketPicker were comparable to those of LIGSITEcs and outperformed the other tools. We introduce a descriptor that translates the arrangement of grid points delineating a detected binding-site into a correlation vector. We show that this shape descriptor is suited for comparative analyses of similar binding-site geometry by examining induced-fit phenomena in aldose reductase. This new method uses information derived from calculations of the buriedness of potential binding-sites.ConclusionThe pocket prediction routine of PocketPicker is a useful tool for identification of potential protein binding-pockets. It produces a convenient representation of binding-site shapes including an intuitive description of their accessibility. The shape-descriptor for automated classification of binding-site geometries can be used as an additional tool complementing elaborate manual inspections.

Subfields: Cell Biology, Computational Theory and Mathematics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/e2f6ecc371540d783bb89c1a5b60892ddf49bd10

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In Cell Biology

1. Russ B. Altman
2. Janet M. Thornton
3. V. Guilloux
4. John Karanicolas
5. David K. Johnson
6. Roman A. Laskowski
7. Pierre Tufféry
8. Tianyun Liu

In Computational Theory and Mathematics

1. John Karanicolas
2. David K. Johnson
3. Russ B. Altman
4. Xin Gao
5. Sheng Wang
6. Shuguang Cui
7. Xu Yan
8. Qing Wei

In Molecular Biology

1. John Karanicolas
2. Russ B. Altman
3. Yang Zhang
4. David K. Johnson
5. Janet M. Thornton
6. Xin Gao
7. Roman A. Laskowski
8. John A. Capra

Citations in the corpus

• PointSite: a point cloud segmentation tool for identification of protein ligand binding atoms (2019, background): “, PocketPicker [42], LIGSITE [14], LIGSITE csc [19]), probe sphere (e.”
• Predicting Protein Ligand Binding Sites by Combining Evolutionary Sequence Conservation and 3D Structure (2009, background): “Ligand binding pockets and residues have been a major focus of these methods [1,13–21].”
• Using Multiple Microenvironments to Find Similar Ligand-Binding Sites: Application to Kinase Inhibitor Binding (2011, methodology): “Previous methods for predicting the potential binding of small molecules to protein pockets have used evolutionary, structural, biochemical and geometric properties in order to assess pocket similarity, or ligand-pocket complementarity [1,2,3,4,5].”
• Computational Methods in Drug Discovery (2014, methodology): “ETPICKER (Weisel et al., 2007) and FLAPSITE (Henrich”
• MetaPocket: A Meta Approach to Improve Protein Ligand Binding Site Prediction (2009, positive): “…LIGSITE (Hendlich et al., 1997), LIGSITE cs (Huang and Schroeder, 2006), SURFNET (Laskowski, 1995), CAST (Liang et al., 1998), PASS (Brady and Stouten, 2000), and PocketPicker (Weisel et al., 2007), all of which use pure geometric characteristics and do not require any knowledge of the ligands.”
• Recognizing protein-ligand binding sites by global structural alignment and local geometry refinement (2012, neutral): “…lock'' (Fischer, 1894); hence shape and physiochemical complementarity are often used to detect concave pockets on proteins surface (Brady and Stouten, 2000; Hendlich et al., 1997; Huang and Schroeder, 2006; Laskowski, 1995; Le Guilloux et al., 2009; Levitt and Banaszak, 1992; Weisel et al., 2007).”
• Selectivity by Small-Molecule Inhibitors of Protein Interactions Can Be Driven by Protein Surface Fluctuations (2015, neutral): “This observation is consistent with results generated using other pocket detection methods (51-55), although”
• Fpocket: An open source platform for ligand pocket detection (2009, methodology): “For the Pocket Picker dataset, results are taken from [23] for all but fpocket.”
• Druggable Protein Interaction Sites Are More Predisposed to Surface Pocket Formation than the Rest of the Protein Surface (2013, result): “This observation is consistent with results generated using other pocket detection methods [29–33], although those other studies were not focused on inhibitors of protein-protein interactions.”