Abstract

Finding elements of proteins that in°uence ligand binding speci¯city is an essential aspect of research in many ¯elds. To assist in this e®ort, this paper presents two statistical models, based on the same theoretical foundation, for evaluating structural similarity among binding cavities. The ¯rst model specializes in the \uni¯ed " comparison of whole cavities, enabling the selection of cavities that are too dissimilar to have similar binding speci¯city. The second model enables a \regionalized " comparison of cavities within a user-de¯ned region, enabling the selection of cavities that are too dissimilar to bind the same molecular fragments in the given region. We applied these models to analyze the ligand binding cavities of the serine protease and enolase superfamilies. Next, we observed that our uni¯ed model correctly separated sets of cavities with identical binding preferences from other sets with varying binding preferences, and that our regionalized model correctly distinguished cavity regions that are too dissimilar to bind similar molecular fragments in the user-de¯ned region. These observations point to applications of statistical modeling that can be used to examine and, more importantly, identify in°uential structural similarities within binding site structure in order to better detect in°uences on proteinligand binding speci¯city.