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Binding pocket shape analysis for protein function prediction

Structural Genomics initiatives are generating an increasing number of protein structures with very limited biochemical characterisation. The analysis and functional assignment of protein structure is a key challenge and a major bottleneck towards the goal of well-annotated genomes. As shape plays a crucial rôle in biomolecular recognition and function, the development of shape analysis techniques is important for understanding protein structure-function relationships. Protein surfaces typically contain clefts of different shapes and sizes. It has been shown that for enzymes, the active site is commonly found in the largest cleft, Laskowski et al. (1996). This makes the detection of the active site relatively straightforward, and can be performed by a program such as SURFNET, Laskowski (1995). SURFNET does this by placing expandable spheres between protein atoms such that the radii of these spheres does not penetrate the two protein atoms or any nearby atoms. The union of all such spheres is used by SURFNET to describe the 3D cleft shape. Residue conservation can be used to improve the binding pocket prediction, Glaser et al. (2005). In this contribution, we present an efficient method for comparing protein binding pockets based on a spherical harmonics expansion. Binding pocket shapes are approximated as functions on the unit sphere by describing each surface point by its spherical coordinates and setting , Morris et al. (2005). The same methodology can be applied to enhance the description by including other properties such as the electrostatic potential, Ritchie and Kemp (1999, 2000).