From Protein Structure to Function with Bioinformatics.pdf

182 N.J. Burgoyne and R.M. Jacksonhighly among those in the rest of the protein. The ease of desolvation weights aromaticand aliphatic surface above polar and, most crucially, charged surface.Charged atoms are the least easily desolvated. This suggests that hotspot residuesprobably function to increase the ease of desolvation on binding or to facilitate theloss of water from the pockets. It might appear that the inclusion of arginine as ahot spot residue is strange due to the fact it would be hard to desolvate. However,if a positively charged residue is required in the interface for molecular recognitionthen the charged guanidinium group of arginine is much easier to desolvate than theamine group of lysine. It has also been suggested that the hydrophobic environmentmay reduce the effective dielectric constant at the location of important hydrogenbonds therefore strengthening the interactions (Bogan and Thorn 1998). It is alsoworth noting that hot-spot residues can be amongst the most conserved residues inthe protein family (Ma et al. 2003), but this conservation is generally only retainedin multiple sequence alignments when the interacting proteins in the alignment areinterlogs (interlogs are interacting proteins whose homologous proteins from otherspecies also interact).7.5.3 Predictions of Interface LocationAs mentioned earlier, non-obligate protein interfaces have no single defining characteristic,so searching for a protein interface using a single property is usuallyunsuccessful. Combining multiple properties, none of which are independentlyindicative of the binding site, can make the prediction of protein interfaces fairlyaccurate. Several approaches make their predictions by picking the most interfacelikepatch that occurs in a set of overlapping circular patches that cover the entireprotein surface. Each patch is assessed according to a range of properties where thevalues of each patch are assessed against models of a typical protein interface.These models are defined from the assessment of the same properties over largenumbers of known interfaces.The simplest procedure, implemented as Sharp2 (Murakami and Jones 2006),uses a single formula to combine six assessed properties (Jones and Thornton1997). These include: (1) hydrophobicity, measured using values derived fromexperiment, (2) solvation, using similar values, (3) a measure of how likely eachresidue is to be in the interface, (4) the planarity of the patch, (5) the roughness ofthe patch, and (6) the SAS area of the patches. This procedure is successful forapproximately 65% of all complexes tested. By using various machine-learningapproaches (procedures that can identify relationships between the properties)the prediction level can be improved. PPI-Pred (Bradford and Westhead 2005)takes a very similar approach applying machine-learning to the same properties asSharp2 (Fig. 7.7). Other approaches use different parameters, for example residue:hydrophobicity, atomic solvation energy, residue surface accessibility and conservationhave been successfully applied (Bordner and Abagyan 2005). Anothermethod, ProMate (Neuvirth et al. 2004), uses hydrophobicity, atomic distribution,