Title: Network-based protein structural classification,
Abstract: Experimental determination of protein function is resource-consuming. As an
alternative, computational prediction of protein function has received
attention. In this context, protein structural classification (PSC) can help,
by allowing for determining structural classes of currently unclassified
proteins based on their features, and then relying on the fact that proteins
with similar structures have similar functions. Existing PSC approaches rely on
sequence-based or direct ("raw") 3-dimensional (3D) structure-based protein
features. In contrast, we first model 3D structures as protein structure
networks (PSNs). Then, we use ("processed") network-based features for PSC. We
propose the use of graphlets, state-of-the-art features in many domains of
network science, in the task of PSC. Moreover, because graphlets can deal only
with unweighted PSNs, and because accounting for edge weights when constructing
PSNs could improve PSC accuracy, we also propose a deep learning framework that
automatically learns network features from the weighted PSNs. When evaluated on
a large set of ~9,400 CATH and ~12,800 SCOP protein domains (spanning 36 PSN
sets), our proposed approaches are superior to existing PSC approaches in terms
of accuracy, with comparable running time.