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From Protein Structure to Function with Bioinformatics.pdf

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7 Predicting <strong>Protein</strong> <strong>Function</strong> from Surface Properties 175developing sufficiently selective drugs for this class of interactions attest <strong>to</strong> thisvariation.7.4.2 Predicting Binding Site LocationsOne thing that does seem <strong>to</strong> be common among protein-ligand binding sites is thepresence of enclosing pockets. When one molecule is smaller than another the easiestway <strong>to</strong> form extensive contact is by engulfing the ligand in a pocket. In addition,in enzymes this also has the added benefit of removing the substrate from solutionand therefore lowering the high solvent reorganisation energy associated <strong>with</strong> solutionreactions (Yadav et al. 1991). There are two major approaches <strong>to</strong> definingproperties on the surface of a protein, geometrically and energetically; these twogroups are described in the following sections.7.4.2.1 Geometrically Defined Ligand Binding SitesThe fundamental idea behind geometry based approaches is that small-moleculesfavour the largest cavity on the protein surface in which <strong>to</strong> bind (Laskowski et al.1996). There are many different approaches for defining these cavities (Laurie andJackson 2006), a few of which are covered here. The simplest methods firstenclose the protein structures in a 3D grid. In Pocket (Levitt and Banaszak 1992)probes are passed along each x, y and z grid line where cavities are defined byregions of free space enclosed by regions of protein interior. LIGSITE (Hendlichet al. 1997) makes this approach less specific <strong>to</strong> protein orientation by repeatingthe search along the cubic diagonals: this method has been re-implemented onlineas Pocket-Finder (Laurie and Jackson 2005). PASS (Brady and S<strong>to</strong>uten 2000)places probes at every position in the protein where they can lie adjacent <strong>to</strong>, butnot overlap <strong>with</strong>, three protein a<strong>to</strong>ms. These probes effectively cover the surfaceof the protein, and are filtered based on the number of protein a<strong>to</strong>ms <strong>with</strong>in a givendistance of the probes – those in cavities will lie closer <strong>to</strong> more protein a<strong>to</strong>ms thanthose that are not. Cycles of probe placing and filtering in this way eventually fillcavities <strong>with</strong> probes.SurfNet (Laskowski 1995) places spheres between pairs of a<strong>to</strong>ms in the proteinwhere the diameter of the sphere is reduced until no other protein a<strong>to</strong>ms are overlapped(this is not always possible, in which case the sphere is removed). Theretained spheres accumulate in the protein cavities. These cavities can be viewedonline for any PDB code using the “Clefts” option in PDBsum (Laskowski et al.2005). CASTp (Binkowski et al. 2003) defines the outer surface a<strong>to</strong>ms using aDelaunay representation. This is a geometric approach that assigns each a<strong>to</strong>m in themolecule the largest possible enclosing polyhedral space. The spaces of neighbouringa<strong>to</strong>ms lie against each other, but where there are no neighbours in a particulardirection the spaces represent surface a<strong>to</strong>ms. Connecting the a<strong>to</strong>m centres of these

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