A Supervised Approach to 3D Structural Classification of Proteins

Three dimensional protein structures determine the function of a protein within a cell. Classification of 3D structure of proteins is therefore crucial to inferring protein functional information as well as the evolution of interactions between proteins. In this paper we propose to employ a recently presented structural representation of the proteins and exploit the learning capabilities of the graph neural network model to perform the classification task.