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. 2007 Mar 13:1:7.
doi: 10.1186/1752-153X-1-7.

PocketPicker: analysis of ligand binding-sites with shape descriptors

Affiliations

PocketPicker: analysis of ligand binding-sites with shape descriptors

Martin Weisel et al. Chem Cent J. .

Abstract

Background: Identification 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.

Results: We 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, LIGSITE(cs), PASS and SURFNET. Success rates PocketPicker were comparable to those of LIGSITE(cs) 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.

Conclusion: The 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.

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Figures

Figure 1
Figure 1
Two-dimensional depiction of the pocket detection process of SURFNET. A: An initial gap sphere (blue disc) is placed midway between the van der Waals surfaces of a pair of atoms. The radius of this gap sphere is then reduced until it is not penetrated by any of the neighboring atoms. The resulting final gap sphere is shown in red. B: The arrangement of final gap spheres is used to describe the shapes and sizes of protein cavities in SURFNET.
Figure 2
Figure 2
Illustration of the alpha shape theory and discrete-flow method used in CAST. A: Two-dimensional depiction of pocket atoms represented as disks of uniform radii. The blue lines show the Voronoi diagram for the pocket atoms. B: The seven bordering lines running through the atom centers represent the convex hull, which is triangulated into Delaunay triangles using information of the Voronoi diagram. The "alpha shape" or "dual complex" is defined by the shaded triangles and the black lines. Three "empty triangles" having at least one grey bordering line are shown. C: Two obtuse empty triangles (1, 3) are assigned to the obtuse triangle (2) by the discrete-flow method.
Figure 3
Figure 3
Placement of spheres for a two-dimensional molecule in PASS. A: The entire surface of the molecule is coated with virtual spheres and an initial layer of spheres residing in buried parts of the protein is specified (blue shaded circles). B: Additional layers are attached onto the initial layer in an iterative process and active site points (red disks) are exposed for potential binding pockets.
Figure 4
Figure 4
Pocket detection method used in POCKET, LIGSITE and its derivatives. Grid probes are installed at the edges of an artificial grid generated around the protein (shaded area). A scanning process is applied to detect protein-solvent-protein events (POCKET and LIGSITE) or surface-solvent-surface events (LIGSITEcs and LIGSITEcsc).
Figure 5
Figure 5
Schematic view of the pocket detection process of PocketPicker. A: Grid points located far off the protein (a) or hidden under the surface (b) are excluded from calculations. Buriedness values are calculated solely for grid points close to the protein surface (c). B: Grid probes indicating surface depressions are collected in clusters.
Figure 6
Figure 6
Triangulations of the sphere. A: The five Platonic bodies offer a symmetric decomposition of the sphere, but only the tetrahedron, the octahedron and the icosahedron (upper row) describe an exact spherical equidistribution of vectors. B: Triangulation of the octahedron was used to arrange additional vectors on the sphere. C: Distribution of 30 search rays obtained from octahedron triangulation.
Figure 7
Figure 7
Calculation of the buriedness-index of a grid point P. A: A search ray (grey plane) scans the room for atoms. Atom Q is detected, since it is located within the dimensions of the search vector. Atoms R and S are not detected, since they are not covered by the search vector. B: Distance calculations are restricted to areas controlled by neighboring centroids (encircled). Neighboring centroids are identified by scanning an extended search space (grey border).
Figure 8
Figure 8
Shapes of pocket conformations induced by IDD594 (A), zenarestat (B) and tolrestat (C). Binding sites are given in PocketPicker representation with darker spheres indicating greater buriedness.
Figure 9
Figure 9
Pocket shapes of the holo-conformation subset 1ah0/1ah4/1eko.
Figure 10
Figure 10
Pocket shapes of the holo-conformation with 1ads/1el3/2acs and 2acq/2acr/2acu forming similar subsets.
Figure 11
Figure 11
Pocket prediction for influenza virus neuramidase (PDB: 1a4g). A cleft formed between chains A and B is found to be the largest pocket and mistakenly predicted as the actual binding site. The binding sites for the ligands zanamivir (PDB: zmr) are identified as second and third largest pockets.
Figure 12
Figure 12
Binding site prediction for malate dehydrogenase (PDB: 2cmd). The ligand citrate (PDB: cit) is situated in the distant end of the elongated pocket (mesh representation) that is suggested as the largest pocket by PocketPicker (blue spheres). Due to the particular shape of the pocket this example is not rated as a correct prediction as the closest ligand atom exceeds the maximal preset distance of 4 Å towards the pocket center.

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