One of the aims of modern Bioinformatics is to discover the molecular mechanisms that rule the protein operation. This would allow us to understand the complex processes involved in living systems and possibly correct dysfunctions. The first step in this direction is the identification of the functional sites of proteins. In this paper, we propose new kernels for the automatic protein active site classification. In particular, we devise innovative attribute-value and tree substructure representations to model biological and spatial information of proteins in Support Vector Machines. We experimented with such models and the Protein Data Bank adequately pre-processed to make explicit the active site information. Our results show that structural kernels used in combination with polynomial kernels can be effectively applied to discriminate an active site from other regions of a protein. Such finding is very important since it firstly shows a successful identification of catalytic sites for a very large family of proteins belonging to a broad class of enzymes.