Graph-Kernels for the Comparative Analysis of Protein Active Sites

Abstract: Graphs are often used to describe and analyze the geometry and physicochemical composition of biomolecular structures, such as chemical compounds and protein active sites. A key problem in graph-based structure analysis is to define a measure of similarity that enables a meaningful comparison of such structures. In this regard, so-called kernel functions have recently attracted a lot of attention, especially since they allow for the application of a rich repertoire of methods from the field of kernel-based machine learning. Most of the existing kernel functions on graph structures, however, have been designed for the case of unlabeled and/or unweighted graphs. Since proteins are often more naturally and more exactly represented in terms of node-labeled and edge-weighted graphs, we propose corresponding extensions of existing graph kernels. Moreover, we propose an instance of the substructure fingerprint kernel suitability for the analysis of protein binding sites. The perform...

• Type: Conference
• Year: 2009
• Where: GCB
• Authors: Thomas Fober, Marco Mernberger, Ralph Moritz, Eyke Hüllermeier
• Keywords: Biometrics, GCB 2009, Graph Kernels, Kernel Functions, Protein Active Sites