Identi fication of Pockets on Protein Surface to Predict Protein–Ligand Binding Sites - Identification of Ligand Binding Site and Protein-Protein Interaction Area - page 25

Q-SiteFinder (Laurie and Jackson 2005 ) , SURFNET (Laskowski 1995 ) , Fpocket
(Le Guilloux et al. 2009 ) , GHECOM (Kawabata 2010 ), ConCavity (Capra et al.
2009 ), POCASA (Yu et al. 2010 ), PocketPicker (Weisel et al. 2007 ) , SiteHound
(Ghersi and Sanchez 2009 ; Hernandez et al. 2009 ) and so on. Some of these methods
have been described in details in other chapters. Most of the existing methods for
protein-ligand binding site prediction can be classified into two types: geometry-
based and energy-based. The geometry-based methods can be further classified into
grid-based, sphere-based and a-shape-based (Kawabata 2010 ; Yu et al. 2010 ) .
In the grid based methods, the protein structure is projected into a 3D grid and the
grid points are categorized into different types such as “outside protein”, “inside
protein” and “near protein surface” according to their positions related to the protein.
Then those grid points not inside protein are clustered using some geometry attributes
and those grids points at the pocket sites can be recognized in the end. LIGSITE CS ,
GHECOM, PocketPicker and ConCavity are the representatives of such type.
In LIGSITE cs , the grid points are categorized into three types: inside protein, near
surface and in the solvent. For all the solvent points, a seven-direction scanning is
applied. Every grid point will be evaluated by the number of SSS (surface-solvent-
surface) event it has, and if the grid point has more or equal than five such events,
it normally locates at a pocket site point. LIGSITE cs will be explained in details
in the next section. GHECOM also firstly projects the protein into a 3D grid, and
the geometry attribute used in this method is mathematical morphology. It uses the
theory of mathematical morphology to define the pocket region on protein surface.
In mathematical morphology (Masuya and Doi 1995 ), there are four basic opera-
tions of dilation, erosion, opening and closing for a probe to define a pocket site. In
ConCavity, a 3D grid is constructed to include the protein as well. Each grid point
is evaluated and scored by the structural information and the evolutional informa-
tion. In the end, the regions with many high-scoring grid points are considered to be
pocket sites. In the sphere-based approaches, the common strategy is to fulfill the
spheres on protein surface layer by layer and a cutting method is applied when
fulfilling. The final pocket sites are that those regions which are in rich of such
spheres. This kind of methods include SURFNET, PASS, PHECOM (Kawabata and
Go 2007 ) and POCASA (Yu et al. 2010 ). Approaches based on a -shape include
CAST and Fpocket. CAST computes the triangulations of the protein's surface
atoms and these triangulations are grouped by letting small sized ones flow towards
the neighboring larger one. The pocket sites are the collection of empty triangles.
Different from CAST, Fpocket uses the idea of a- sphere which is a sphere contacting
four atoms on its boundary and containing no inside atom. The next step is to iden-
tify clusters of spheres close together and those clusters are potential pocket sites.
In contrast to geometry-based methods, Q-SiteFinder (Laurie and Jackson 2005 )
aims to find pocket sites by computing the interaction energy between protein atoms
and a small molecule probe. In Q-SiteFinder, layers of methyl (―CH3) probes are
initialized on protein surface to calculate the van der Waals interaction energy
between the protein atoms and the probes. Then the probes are clustered into many
groups and are ranked by the total energy of probes. Those clusters with high energy
will be the potential ligand binding sites. SiteHound (Ghersi and Sanchez 2009 ;
Hernandez et al. 2009 ) is similar to Q-SiteFinder but it includes Lennard-Jones and