Shape distributions and protein similarity

In this paper we describe a similarity model that provides the objective basis for clustering proteins of similar structure. More specifically, we consider the following variant of the protein-protein similarity problem: We want to find proteins in a large database D that are very similar to a given query protein in terms of geometric shape. We give experimental evidence, that the shape similarity model of Osada, Funkhouser, Chazelle and Dobkin [OFCD02] can be transferred to the context of protein structure comparison. This model is very simple and leads to algorithms that have attractive space requirements and running times. For example, it took 0.39 seconds to retrieve the eight members of the seryl family out of 26,600 domains. Furthermore, a very high agreement with one of the most popular classification schemes proved the significance of our simplified representation of complex proteins structure by a distribution of C_α-C_α distances.